LABORATORY OF COMPUTATIONAL
IMAGING AND BIOINFORMATICS
Annual Report: 2011
Director: Dr. Anant Madabhushi
Associate Professor,
Department of Biomedical Engineering
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
LCIB Website @ http://lcib.rutgers.edu
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
LAB MEMBERS
April 2011
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
LAB MEMBERS
Lab Director: Anant Madabhushi, PhD
Administrative Assistant: Rhonda Breen-Simone
Postdocs:
• Mirabela Rusu, PhD
• Tao Wan, PhD
Graduate Students
• Sahir Ali
• Ajay Basavanhally
• Andrew Janowczyk (IIT, Bombay)
• George Lee
• Shoshana Rosskamm
• Rachel Sparks
• Pallavi Tiwari
• Rob Toth
• Satish Viswanath
Research Faculty
• James Monaco, PhD
Undergraduate Students
• Ronak Chawla
• Joe Galero
• Abhishek Golugula
• Aparma Kannan
• Sudha Karthigeyan
• Ross Kleiman
• Prateeka Koul
• Eileen Hwuang
• Pratik Patel
• Srivathsan Prabu
• Elaine Yu
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
LAB MEMBERS
Assistant
Faculty
Lab Director:
Anant Madabhushi, PhD
Research Faculty
James Monaco, PhD
Rhonda
Breen-Simone
PostDocs
Mirabela Rusu, PhD
Tao Wan, PhD
Graduate Students
Sahir Ali
Ajay Basavanhally
Andrew Janowczyk
(IIT, Bombay)
George Lee
Shoshana Rosskamm
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
LAB MEMBERS
Graduate Students (Contd)
Rachel Sparks
Pallavi Tiwari
Rob Toth
Satish Viswanath
Undergraduate Students
• Ronak Chawla
• Aparma Kannan
• Sudha Karthigeyan
• Ross Kleiman
• Prateeka Koul
• Eileen Hwuang
• Srivathsan Prabu
Joe Galero
Elaine Yu
Pratik Patel
Abhishek Golugula
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
RECENT ALUMNI
PostDocs
Gaoyu Xiao, PhD
Jun Xu, PhD
Professor at
Nanjing
University
Master Students
PhD Students
Shannon Agner Jon Chappelow
Research
3rd Year Medical
Scientist at
Student at
UMDNJ-RWJMS Accuray, Inc.
Scott Doyle
Director of
Research at
Ibris, Inc.
Akshay Shridar
Assistant Project
Manager at Integra
Life Sciences
Najeeb Chowdhury
Healthcare Market
Research Analyst
at AlphaDetail Inc.
7
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
SOME OF OUR COLLABORATORS
John E .Tomaszewski
Mark Rosen, Michael D. Feldman, Shridar Ganesan
8
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
CONFERENCE PARTICIPATION - 2011
SPIE, Finalist Student Paper Award: Shannon Agner
MICCAI: Sahir Ali, Satish Viswanath, Ajay Basavanhally
9
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
SUMMARY OF ACCOMPLISHMENTS 2011
Lab Members: 25 (2 faculty, 2 post-docs, 9 graduate, 1 admin, 11
undergrads)
Papers





Books: 1
Theses: 3 PhD+2 MS
Journal Papers: 24
Peer-Reviewed Conference Papers: 31
Abstracts: 18
5 new PCT Patents Filed
5 new grants
 1 NIH
 1 DOD
On going projects: 45
10
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
AWARDS AND ACCOMPLISHMENTS
Editorial, Committees, Membership - Anant Madabhushi
Editor (Guest), Special Issue on IEEE Transactions on Biomedical Engineering Letters: Multi-Scale/Resolution
Signal and Image Analysis, 2011.
Co-Chair, Session on “Image Analysis in Cancer Detection”, IEEE Engineering in Medicine and
Biology Conference 2011, Boston, MA, September 1, 2011.
Chair, Workshop on Prostate Cancer Imaging, MICCAI 2011.
Chair, Workshop on Histopathology Image Analysis, MICCAI 2011.
Co-Organizer, Tutorial on Manifold Learning for Medical Images, MICCAI 2011.
Co-Chair, Special Session on Prostate Cancer Imaging, IEEE International Symposium on Biomedical
Imaging, March 2011.
Program Committee Member, Computer-Aided Diagnosis conference at SPIE Medical Imaging, 2011.
Program Committee Member, MICCAI 2011.
Panelist and Invited Speaker, “Preparing for Campus Interviews & Negotiating Academic Job Offers”,
University of Pennsylvania, January, 2011.
Program Committee on IEEE Conference on Bioinformatics and Bioengineering (BIBE), 2011.
Technical Advisory Board Member, IEEE EMBS/ISBI, 2010-Present
Co-Chair, Workshop on Trends in Experimental Pathology: Imaging Organisms/Experimental Trends
– “Brave New World”, In conjunction with 2011 Annual Meeting at Experimental Biology, American
Society for Investigative Pathology, April 9-13, 2011, Washington, DC.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
AWARDS AND ACCOMPLISHMENTS
Awards - Anant Madabhushi
First Prize, SNR Denton Elevator Pitch Competition, Rutgers Entrepreneurship Day, 2011
Media Recognition
“Entrepreneurs flock to Rutgers Entrepreneurship Conference”, New Jersey Tech Weekly,
November 22nd, 2011.
“University Startup receives NIH SBIR Funding”, Daily Targum, October 11, 2011.
“Rutgers-Affiliated Start-Up Company Receives Funding for Technology to Help Choose Breast
Cancer Treatments”, Rutgers Today, October 6th, 2011. Story featured on the following news
sites
AAAS EurekAlert!
Noodls.com
NJBIZ.com
www.njcrea.com
www.biomedicalproducts.com
www.bioprodmag.com www.physorg.com
www.news-medical.net
www.biosciencetechnology.com
www.biosciencetechnology.com
www.bionj.org
“The Race for Another Cure”, Rutgers Magazine, Pg. 31, Winter 2011 Issue.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
NEW GRANTS - 2011
Madabhushi, Anant (PI)
Date: 07/01/11-12/31/11
Tech Commercialization Fund, ORSP, Rutgers
Image based risk score predictors for ER+ Breast
Cancers
Role: PI
Madabhushi, Anant (MPI)
Date: 09/03/11-08/31/12
NIH R43EB015199-01
Decision Support System for Predicting outcome of
ER+ breast cancers
Role: PI with Shridar Ganesan (CINJ), David Harding
(IbRiS Inc.)
Madabhushi, Anant (PI)
Date: 07/01/11- 06/30/12
Pre-doctoral Training Fellowship
Society for Imaging Informatics in Medicine
Computerized Decision Support for Prostate Cancer
Detection from Multi-parametric MRI
Role: Mentor for Satish Viswanath
Madabhushi, Anant (PI)
Date: 01/01/12 -12/31/13
Pre-doctoral Training Fellowship in Health Disparity
Department of Defense
Comparing Proteomic, Histological Biomarkers for
Biochemical Failure Among African Americans and
Caucasians Following Radical Prostatectomy
Role: Mentor for George Lee
Monaco, James (PI)
Date: 06/09/11 - 07/15/11
Burroughs Wellcome Fund
Pathology Training and Collaboration between Rutgers
and University of Michigan
Role: Collaborator
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
STUDENTS AWARDS AND FELLOWSHIPS
Pallavi Tiwari, Travel Award ($250) from SciWomen, The Office for the promotion of
Women in Science, Engineering and Mathematics (STEM), 2011
Sahirzeeshan Ali, Travel Award ($600) from Omnyx to attend Workshop on
Histopathology Image Analysis, Toronto, Canada, 2011
Ajay Basavanhally, Travel Award ($600) from Omnyx to attend Workshop on
Histopathology Image Analysis, Toronto, Canada, 2011
Joseph Galero, Elaine Yu, BMEStart Competition, Honorable Mention, NCIAA, 2011.
Shannon Agner, “Textural Kinetics: A Novel Dynamic Contrast-Enhanced (DCE)-MRI
Feature for Breast Lesion Classification” nominated for best paper of 2010 by the Journal
of Digital Imaging editorial board.
Joe Galero, Best Poster Award for “Integrated Texton and Bag of Words Classifier for
Identifying Anaplastic Medulloblastomas" at BME Senior Design Conference 2011
Elaine Yu, Admitted to Pittsburgh Tissue Engineering Initiative Summer Internship program
with a research scholarship ($3,850), April 2011.
Shannon Agner, Outstanding Graduate Student Award, School of Engineering, April 15,
2011.
Pratik Patel, Selected as an intern to (RISE) Research in Science and Engineering Program
($2500), Germany, 2011.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
STUDENTS AWARDS AND FELLOWSHIPS (CONTD)
Ajay Basavanhally, Travel Award ($300) from IEEE Intl Symposium on Biomedical Imaging,
2011
Shannon Agner, Departmental Travel Award ($200), 2011
Satish Viswanath, Departmental Travel Award ($100), 2011
Akshay Shridar, Departmental Travel Award ($100), 2011
Ajay Basavanhally, Departmental Travel Award ($100), 2011
Scott Doyle, Departmental Travel Award ($100), 2011
George Lee, Departmental Travel Award ($100), 2011
Satish Viswanath, Interview featured in NJN TV Channel, March 12th, 2011.
Satish Viswanath, Interview featured in CP News Story, “Study Finds most Indian graduate
students in the US want to use their degrees back home”, 2011.
Sudha Karthigeyan, Selected as an intern to (UROP) Undergraduate Research
Opportunities Program ($2500), 2011.
Shannon Agner, Michael B. Merickel Best Student Paper Award Finalist, SPIE Medical
Imaging, 2011.
Shannon Agner, Rachel Sparks, BMEIdea Competition Finalist, NCIAA ($500), 2011.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS FOR 2011
Summary
35
30
25
20
15
10
5
0
Theses
Journal Papers
Conference Papers
Abstracts
16
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
BOOKS
Madabhushi, A, Dowling, J, Huisman, H,
Barratt, D, Prostate Cancer Imaging:
Image analysis and image guided
interventions, International Workshop
Held in Conjunction with MICCAI 2011,
Proceedings Springer Verlag
ISBN-10: 3642239439, 2011
17
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
THESES
PhD
Shannon Agner, A Computerized Image Analysis Framework for Dynamic Contrast
Enhanced Magnetic Resonance Imaging (DCE-MRI): Applications to Breast Cancer (04/2011)
Jon Chappelow , Multimodal Image Registration using Multivariate Information Theoretic
Similarity Measures: Applications to Prostate Cancer Diagnosis and Targeted Treatment
(04/2011)
Scott Doyle, Computerized Detection, Segmentation and Classification of Digital Pathology:
Case Study in Prostate Cancer (04/2011)
MS
Najeeb Chowdhury, Concurrent segmentation of the prostate on MRI and CT via linked
statistical shape models for radiotherapy planning (07/2011)
Akshay Shridar, Content-Based Image Retrieval of Digitized Histopathology via Boosted
Spectral Embedding (BoSE) (10/2011)
18
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS
JOURNAL PAPERS
Hipp, J, Monaco, J, Kunju, P Cheng, P, Yagi, Y, Rodriguez-Canales, J, Emmert-Buck, M, Hewitt, S,
Feldman, M, Tomaszewski, J, Shih, N, Toner, M, Tompkins, R, Flotte, T, Lucas, D, Gilbertson, Kunju,
LP, J, Balis, U, Madabhushi, A, “Integration of architectural and cytologic driven image algorithms for
prostate adenocarcinoma identification”, Analytical Cellular Pathology, Accepted.
Chowdhury, N, Toth, R, Chappelow, J, Kim, S, Motwani, S, Punekar, S, Lin, H, Both, S, Vapiwala, N,
Hahn, S, Madabhushi, A, “Concurrent segmentation of the prostate on MRI and CT via linked
statistical shape models for radiotherapy planning”, Medical Physics, Accepted.
Golugula, A, Lee, G, Master, S, Feldman, M, Tomaszewski, J, Speicher, D, Madabhushi, A,
“Supervised Regularized Canonical Correlation Analysis: Integrating Histologic and Proteomic
measurements for predicting Biochemical Failures following Prostate Surgery”, BMC Bioinformatics,
Accepted.
Viswanath, S, Bloch, B, Chappelow, J, Toth, R, Rofsky, N, Genega, E, Lenkinski, R, Madabhushi, A,
“Central Gland and Peripheral Zone Prostate Tumors have Significantly Different Quantitative
Imaging Signatures on 3 Tesla Endorectal, In Vivo T2-Weighted Magnetic Resonance Imagery”,
Journal of Magnetic Resonance Imaging, Accepted.
Janowczyk, A, Chandran, S, Singh, R, Sasaroli, D, Coukos, G, Feldman, M, Madabhushi, A, “HighThroughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical
Normalized Cuts”, IEEE Trans on Biomedical Engineering, In Press.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS
JOURNAL PAPERS (CONTD)
Hipp, J, Monaco, J, Kunju, P Cheng, P, Yagi, Y, Rodriguez-Canales, J, Emmert-Buck, M, Hewitt, S,
Feldman, M, Tomaszewski, J, Shih, N, Toner, M, Tompkins, R, Flotte, T, Lucas, D, Gilbertson,
Madabhushi, A, Kunju, LP, J, Balis, U, “Optimization of complex cancer morphology detection using
the SIVQ pattern recognition algorithm”, Analytical Cellular Pathology, 2011 Oct 11 [Epub ahead of
print] (PMID: 21988838)
Hipp, J, Cheng, J, Pantanowitz, L, Hewitt, S, Yagi, Y, Monaco, J, Madabhushi, A, Rodriguez-canales, J,
Hanson, J, Roy-Chowdhuri, S, Filie, A, Feldman, M, Tomaszewski, J, Shih, N, Gilbertson, J, EmmertBuck, M, Balis, U, “Image Microarrays (IMA) Digital Pathology's Missing Tool”, Journal of Pathology
Informatics, Accepted
Ali, S, Madabhushi, A, "GPU Implementation of an Integrated Shape Based Active Contour:
Application to Digital Pathology", Journal of Pathology Informatics, Accepted.
Basavanhally, A, Ganesan, S, Shih, N, Feldman, M, Tomaszewski, J, Madabhushi, A, “Multi-Field-ofView Strategy for Image-Based Outcome Prediction of ER+ Breast Cancer Histopathology Using
Spatio-Architectural and Vascular Features”, Journal of Pathology Informatics, Accepted.
Ghaznavi, D, Evans, A, Madabhushi, A, Feldman, M, “Digital Imaging in Pathology: Whole-Slide
Imaging and Beyond”, Annual Review of Pathology: Mechanisms of Disease, Accepted.
Doyle, S, Monaco, J, Tomaszewski, J, Feldman, M, Madabhushi, A, "An Active Learning Based
Classification Strategy for the Minority Class Problem: Application to Histopathology Annotation",
BMC Bioinformatics, 12:424, 2011.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS
JOURNAL PAPERS (CONTD)
Hipp, J, Monaco, J, Cheng, J, Lucas, D, Madabhushi, A, Balis, U, Automated Vector Selection of SIVQ
and Parallel Computing Integration MATLABTM: Innovations supporting large-scale and highthroughput image analysis studies, Journal of Pathology Informatics, 2:37, 2011 (PMID: 21886893).
Tiwari, P, Viswanath, S, Kurhanewicz, J, Shridar, A, Madabhushi, A, “Multimodal Wavelet Embedding
Representation for data Combination (MaWERiC): Integrating Magnetic Resonance Imaging and
Spectroscopy for Prostate Cancer Detection”, NMR in Biomedicine, 2011 doi: 10.1002/nbm.1777,
(PMID: 21960175).
Hipp, J, Sica, J, McKenna, B, Monaco, J, Madabhushi, A, Cheng, J, Balis, U, The need for the
pathology community to sponsor a WSI repository with technical guidance from the pathology
informatics community, Journal of Pathology Informatics, 2:31, 2011 (PMID: 21845229).
Bulman, J, Toth, R, Patel, AD, Bloch, BN, McMahon, CJ, Ngo, L, Madabhushi, A, Rofsky, N,
Automated Computer-Derived Prostate Volumes from MRI Data: Comparison to RadiologistDerived MRI Volumes and Pathology Specimen Volumes, Radiology, Accepted.
Hipp, J, Flotte, T, Monaco, J, Cheng, J, Madabhushi, A, Yagi, Y, Rodriguez-Canales, J, Emmert-Buck,
M, Dugan, MC, Hewitt, S, Toner, M, Tompkins, R, Lucas, D, Gilbertson, JR, Balis, U, Computer
Aided Diagnostic (CAD) tools aim to empower rather than replace Pathologists: Lessons learned
from computational chess, Journal of Pathology Informatics, 2:25, 2011 (PMID: 21773056).
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS
JOURNAL PAPERS (CONTD)
Xu, J, Janowcyzk, A., Tomaszewski, J., Feldman, M., and Madabhushi, A, “A High-throughput Active
Contour Scheme for Segmentation of Histopathological Imagery”, Medical Image Analysis, vol. 15, pp.
851-62, 2011 (PMID: 21570336).
Chappelow, J, Bloch, N., Rofsky, N, Genega, E, Lenkinski, R, DeWolf, W, Madabhushi, A, Elastic
Registration of Multimodal Prostate MRI and Histology via Multi-Attribute Combined Mutual
Information, Medical Physics, vol. 38[4], pp. 2005-2018, 2011 (PMID: 21626933).
Toth, R, Bloch, N, Genega, E, Rofsky, N, Lenkinsky, R, Rosen, M, Kalyanpur, A, Pungavkar, S,
Madabhushi, A, Accurate Prostate Volume Estimation Using Multi-Feature Active Shape Models on
T2-Weighted MRI, Academic Radiology, vol. 18[6], pp. 745-54, 2011 (PMID: 21549962).
Chappelow, J, Feldman, M, Tomaszewski, J, Shih, N, Madabhushi, A, HistoStitcher©: An Interactive
Program for Accurate and Rapid Reconstruction of Digitized Whole Histological Sections from
Tissue Fragments, Computerized Medical Imaging and Graphics, vol. 35[7-8], pp. 542-56, 2011 (PMID:
21397459).
Monaco, J, Madabhushi, A, “Weighted Maximum Posterior Marginals for Random Fields Using an
Ensemble of Conditional Densities from Multiple Markov Chain Monte Carlo Simulations”, IEEE
Transactions on Medical Imaging, vol. 30[7], pp. 1353-64, 2011 (PMID: 21335309).
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS
JOURNAL PAPERS (CONTD)
Madabhushi, A, Agner, S, Doyle, S, Basavanhally, A, Lee, G, Computer-Aided Prognosis: Predicting
patient and disease outcome via quantitative fusion of multi-scale, multi-modal data, Special Issue of
Computerized Medical Imaging and Graphics on Whole Slide Microscopic Image Processing, vol. 35[7-8],
pp. 506-14, 2011 (PMID: 21333490).
Xiao, G, Bloch, N, Chappelow, J, Genega, E, Rofsky, N, Lenkinsky, R, Tomaszewski, J, Feldman, M,
Rosen, M, Madabhushi, A, Determining Histology-MRI Slice Correspondences for Defining MRIbased Disease Signatures of Prostate Cancer, Special Issue of Computerized Medical Imaging and
Graphics on Whole Slide Microscopic Image Processing, vol. 35[7-8], pp. 568-78, 2011 (PMID:
21255974).
Toth, R, Tiwari, P, Rosen, M, Reed, G, Kurhanewicz, J, Kalyanpur, A, Pungavkar, S, Madabhushi, A, A
Magnetic Resonance Spectroscopy Driven Initialization Scheme for Active Shape Model Based
Prostate Segmentation, Medical Image Analysis, vol. 15(2), pp. 214-225, 2011 (PMID: 21195016).
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS
PEER-REVIEWED CONFERENCE PROCEEDINGS
Tiwari, P, Viswanath, S, Kurhanewicz, J, Madabhushi, A, Weighted Combination of Multi-parametric
MR Imaging markers for Evaluating Radiation Therapy changes in the prostate, Workshop on
Prostate Cancer Imaging, In Conjunction with MICCAI 2011, pp. 80-91.
Ginsburg, S, Tiwari, P, Kurhanewicz, J, Madabhushi, A, Variable Ranking with PCA: Finding
Multiparametric MR Imaging Biomarkers for Prostate Cancer Diagnosis and Grading, Workshop on
Prostate Cancer Imaging, In Conjunction with MICCAI 2011, pp. 146-157.
Palumbo, D, Yee, B, Leedy, S, O’Dea, P, Viswanath, S, Madabhushi, A, Interplay between Bias Field
Correction, Intensity Standardization, and Noise Filtering for T2-weighted MRI, IEEE Engineering in
Medicine and Biology Conference, 2011, Accepted.
Golugula, A, Lee, G, Master, S, Feldman, M, Tomaszewski, J, Madabhushi, A, Supervised Regularized
Canonical Correlation Analysis: Integrating Histologic and Proteomic Data for Predicting
Biochemical Failures, IEEE Engineering in Medicine and Biology Conference, 2011, Accepted
Galero, J, Judkins, A, Bacon, J, Ellison, D, Madabhushi, A, An Integrated Texton and Bag of Words
Classifier for Identifying Anaplastic Medulloblastomas, IEEE Engineering in Medicine and Biology
Conference, 2011, Accepted
Patel, P, Chappelow, J, Tomaszewski, J, Feldman, M, Rosen, M, Madabhushi, A, Spatially Weighted
Mutual Information (SWMI) for Registration of Digitally Reconstructed ex vivo Whole Mount
Histology and in vivo prostate MRI, IEEE Engineering in Medicine and Biology Conference, 2011,
Accepted
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS
PEER-REVIEWED CONFERENCE PROCEEDINGS (CONTD)
Yu, E, Monaco, J, Shih, N, Feldman, M, Tomaszewski, J, Madabhushi, A, Detection of Prostate
Cancer on Histopathology using Color Fractals and Probabilistic Pairwise Markov Models, IEEE
Engineering in Medicine and Biology Conference, 2011, Accepted
Golugula, A, Lee, G, Madabhushi, A, Evaluating Feature Selection Strategies for High Dimensional,
Small Sample Size Datasets, IEEE Engineering in Medicine and Biology Conference, 2011, Accepted
Ali, S, Veltri, R, Epstein, J, Christudass, C, Madabhushi, A, “Adaptive Energy Selective active contour
with Shape Priors For Nuclear Segmentation and Gleason grading of prostate cancer”, In Proc of
Medical Image Computing and Computer Assisted Interventions (MICCAI), pp. 661-669, 2011
(PMID: 22003675).
Xiao, G, Madabhushi, A, “Aggregated Distance Metric learning (ADM) for image classification in
presence of limited training data”, In Proc of Medical Image Computing and Computer Assisted
Interventions (MICCAI), pp. 33-40, 2011 (PMID: 22003681).
Tchikindas, L, Sparks, R, Baccon, J, Ellison, D, Judkins, A, Madabhushi, A, “Segmentation of Nodular
Medulloblastoma Using Random Walker and Hierarchical Normalized Cuts”, IEEE Northeast
Bioengineering Conference (NEBC), pp. 1-2, 2011.
Lai, Y, Viswanath, S, Baccon, J, Ellison, D, Judkins, A, Madabhushi, A, “A Texture-based Classifier to
Discriminate Anaplastic from Non-Anaplastic Medulloblastoma”, IEEE Northeast Bioengineering
Conference (NEBC), pp. 1-2, 2011.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS
PEER-REVIEWED CONFERENCE PROCEEDINGS (CONTD)
Basavanhally, A, Ganesan, S, Shih, Natalie, Mies, C, Feldman, M, Tomaszewski, J, Madabhushi, A, “A
Boosted Classifier for Integrating Multiple Fields of View: Breast Cancer Grading in Histopathology”,
IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 125-128, 2011.
Sparks, R, Madabhushi, A, “Out-of-Sample Extrapolation using Semi-Supervised Manifold Learning
(OSE-SSL): Content-based Image Retrieval for Prostate Histology Grading”, IEEE International
Symposium on Biomedical Imaging, Chicago, IL, pp. 734-737, 2011.
Shridar, A, Doyle, S, Madabhushi, A, “Boosted Spectral Embedding (BoSE): Applications to ContentBased Image Retrieval of Histopathology”, IEEE International Symposium on Biomedical Imaging,
Chicago, IL, pp. 1897-1900, 2011.
Tiwari, P, Viswanath, S, Lee, G, Madabhushi, A, “Multi-modal data fusion schemes for integrated
classification of imaging and non-imaging data”, IEEE International Symposium on Biomedical Imaging,
Chicago, IL, pp. 165-168, 2011.
Toth, R, Sparks, R, Madabhushi, A, “Medial Axis based Statistical Shape Model (MASSM):
Applications to 3D Prostate Segmentation on MRI”, IEEE International Symposium on Biomedical
Imaging, Chicago, IL, pp. 1463-466, 2011.
Doyle, S, Feldman, M, Tomaszewski, J, Shih, N, Madabhushi, A, “Cascaded Multi-class Pair-wise
Classifier (CASCAMPA) for normal, cancerous, and cancer confounder classes in prostate
histology”, IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 715-718, 2011.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS
PEER-REVIEWED CONFERENCE PROCEEDINGS (CONTD)
Ali, S, Madabhushi, A, ”Active Contour for Overlap Resolution using Watershed based Initialization
(ACOReW): Applications to Histopathology”, IEEE International Symposium on Biomedical Imaging,
Chicago, IL, pp. 614-617, 2011.
Viswanath, S, Tiwari, P, Chappelow, J, Toth, R, Kurhanewicz, J, Madabhushi, A, “CadOnc©: An
Integrated Toolkit for Evaluating Radiation Therapy Related Changes in the Prostate Using MultiParametric MRI”, IEEE International Symposium on Biomedical Imaging, Chicago, IL, pp. 2095-98,
2011.
Toth, R, Chappelow, J, Kutter, O, Vetter, C, Russ, C, Feldman, M, Tomaszewski, J, Shih, N,
Madabhushi, A, Incorporating the whole-mount prostate histology reconstruction program
Histostitcher© into the extensible imaging platform (XIP) framework, SPIE Medical Imaging, 2012,
Accepted.
Sparks, R, Madabhushi, A, Gleason grading of prostate histology utilizing statistical shape model of
manifolds (SSMM), SPIE Medical Imaging, 2012, Accepted.
Agner, S, Xu, J, Rosen, M, Karthigeyan, S, Englander, S, Madabhushi, A, Spectral embedding based
active contour (SEAC): application to breast lesion segmentation on DCE-MRI. SPIE Medical
Imaging, vol. 7963, pp. 796305-1 – 12, 2011.
Ali, S, Madabhushi, A, Segmenting multiple overlapping objects via an integrated region and boundary
based active contour incorporating shape priors: applications to histopathology, SPIE Medical
Imaging, 2011, Accepted.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED PUBLICATIONS
PEER-REVIEWED CONFERENCE PROCEEDINGS (CONTD)
Sparks, R, Madabhushi, A, Content-based image retrieval utilizing shape modeling and manifold
learning, SPIE Medical Imaging, 2011, Accepted.
Chowdhury, N, Chappelow, J, Toth, R, Madabhushi, A, Novel approach for building linked statistical
shape models for multimodal prostate radiotherapy planning, SPIE Medical Imaging, 2011, Accepted.
Janowczyk, A, Chandran, S, Feldman, M, Madabhushi, A, Local morphologic scale: application to
segmenting tumor infiltrating lymphocytes in ovarian cancer TMAs, SPIE Medical Imaging, 2011,
Accepted.
Basavanhally, A, Yu, E, Ganesan, S, Feldman, M, Tomaszewski, J, Madabhushi, A, Incorporating
domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods,
SPIE Medical Imaging, 2011, Accepted.
Toth, R, Bulman, J, Patel, A, Bloch, N, Genega, E, Rofsky, N, Lenkinski, R, Madabhushi, A, Integrating
an Adaptive Region Based Appearance Model with a Landmark Free Statistical Shape Model:
Application to Prostate MRI Segmentation, SPIE Medical Imaging, 2011, vol. 7962, In Press.
Viswanath, S, Chappelow, J, Patel, P, A, Bloch, N, Genega, E, Rofsky, N, Lenkinski, R, Madabhushi, A,
Enhanced multiprotocol analysis via intelligent supervised embedding (EMPrAvISE) for prostate
cancer detection on MRI, SPIE Medical Imaging, 2011, In Press.
Viswanath, S, Palumbo, D, Chappelow, J, Patel, P, A, Bloch, N, Genega, E, Rofsky, N, Lenkinski, R,
Madabhushi, A, Empirical evaluation of bias field correction algorithms for computer-aided detection
of prostate cancer on T2w MRI, SPIE Medical Imaging, 2011, In Press.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED ABSTRACTS
P Raess, J Monaco, R Chawla, A Bagg, M Weiss, J Choi, and A Madabhushi “Alpha-Hemoglobin
Stabilizing Protein Specifically Identifies Nucleated Erythroid Precursors and Enables Identification of
Architectural Distortion in Myelodysplastic Syndromes by Computerized Image Analysis", United
States and Canadian Academy of Pathology's 101st Annual Meeting, March 17-23, 2012 Accepted.
Hipp, J, Smith, S, Cheng, J, Tomlins, S, Monaco, J, Madabhushi, A, Kunju, P, Balis, U, “Optimization of
detection of complex cancer morphology using the SIVQ pattern recognition algorithm”, First
Congress of the International Academy of Digital Pathology, pp. 28, 2011 (Best Poster Award).
Tiwari, P, Kurhanewicz, J, Madabhushi, A, “Computerized quantitative data integration of multiprotocol MRI for identification of high grade prostate cancer in vivo”, in Proc. ISMRM, pp. 2640,
2011.
Sparks, R, Madabhushi, A, “Computerized Classification of Benign and Malignant Breast Lesions on
DCE-MRI Utilizing Novel Shape Descriptors“ in Proc. ISMRM, pp. 2626, 2011.
Agner, S, Xu, J, Karthigeyan, S, Madabhushi, A, “Computerized Lesion Segmentation on DCE MRI
Using Active Contours and Spectral Embedding”, in Proc. ISMRM, pp. 136, 2011.
Toth, R., Bloch, B.N., Genega, E.M., Rofsky, N.M., Lenkinski, R.E., Rosen, M, Madabhushi, A,
"Accurate Prostate Volume Determination from T2-w MRI using Statistical Shape Models," in Proc.
ISMRM, pp. 2643, 2011.
Xiao, G, Bloch, B. N., Chappelow, J, Genega, E, Rofsky, N, Lenkinski, R, Tomaszewski, J, Feldman, M,
Rosen, M, Kalyanpur, A, and Madabhushi, A, “Determining histology-MRI slice correspondences for
mapping prostate cancer extent in vivo”, in Proc. ISMRM, pp. 2645, 2011.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED ABSTRACTS (CONTD)
Viswanath, S, Chappelow, J, Tiwari, P, Kurhanewicz, J, Madabhushi, A, "CADOnc: A Computerized
Decision Support System for Quantifying Radiation Therapy Changes in the Prostate via MultiParametric MRI", in Proc. ISMRM, pp. 2647, 2011.
Viswanath, S, Bloch, B. N., Chappelow, J, Patel, P, Rofsky, N, Lenkinski, R, Genega, E, and
Madabhushi, A, "EMPrAvISE: A Computerized Decision Support System for Automated Prostate
Cancer Detection from Multi-Protocol MRI", in Proc. ISMRM, pp. 2642, 2011.
Hipp, J, Cheng, J, Hansen, J, Hewitt, S, Monaco, J, Madabhushi, A, Han, S, Yan, W, Rodriguez-canales,
W, Hipp, J, Tangrea, M, Emmert-Buck, M, Balis, U, “Ring Vector Image analysis (SIVQ): A highthroughput discovery tool for surgical pathologists”, United States and Canadian Academy of
Pathology's 100th Annual Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.
Janowczyk, A, Chandran, S, Feldman, M, Madabhushi, A, “Quantifying Tumor Infiltrating
Lymphocytes in Ovarian Cancer TMAs”, United States and Canadian Academy of Pathology's 100th
Annual Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.
Xu, J, Sparks, R, Janowczyk, A, Tomaszewski, J, Feldman, M, Madabhushi, A, “High-Throughput
Prostate Cancer Gland Segmentation and Classification from Digitized Needle Core Biopsies”,
United States and Canadian Academy of Pathology's 100th Annual Meeting, San Antonio, TX,
February 26 - March 4, 2011, Accepted.
Sparks, R, Madabhushi, A, “Quantifying Gland Morphology for Computerized Prostate Cancer
Detection and Gleason Grading”, United States and Canadian Academy of Pathology's 100th Annual
Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
PEER REVIEWED ABSTRACTS (CONTD)
Monaco, J, Tomaszewski, J, Madabhushi, A, “Automated Tumor Volume Estimation Using Digitized
Prostatectomy Specimens”, United States and Canadian Academy of Pathology's 100th Annual
Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.
Lee, G, Doyle, S, Monaco, J, Feldman, M, Tomaszewski, J, Master, S, Madabhushi, A, “Fusion of
proteomic and histologic image features for predicting prostate cancer recurrence after radical
prostatectomy”, United States and Canadian Academy of Pathology's 100th Annual Meeting, San
Antonio, TX, February 26 - March 4, 2011, Accepted.
Basavanhally, A, Ganesan, S, Feldman, M, Mies, C, Tomaszewski, J, Madabhushi, A, “Histologic Imagebased Classifier for Predicting Outcome of ER+ Breast Cancers”, Laboratory Investigation, vol.
91[1], pp. 27A-27A, 2011.
Sridhar, A, Doyle, S, Tomaszewski, J, Feldman, M, Madabhushi, A, “A Content-Based Image Retrieval
System for Digitized Prostate Histopathology”, United States and Canadian Academy of Pathology's
100th Annual Meeting, San Antonio, TX, February 26 - March 4, 2011, Accepted.
Tiwari, P, Kurhanewicz, J, Madabhushi, A, “Multimodal Integration of Magnetic Resonance Imaging
and Spectroscopy for Detection of Aggressive Prostate Cancer”, Innovative Minds in Prostate
Cancer Today (IMPaCT), 2011, Accepted.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
LCIB WORKSHOPS 2011
1. Histopathology Image Analysis, Clinical
Challenges and Quantitative Image Analysis
Solutions, MICCAI 2011
http://lcib.rutgers.edu/hima2011/
2. Prostate Cancer Imaging, Computer Aided
Diagnosis, Prognosis, and Intervention,
MICCAI 2011
http://lcib.rutgers.edu/pci2011/
3. Tutorial on Manifold Learning With Medical Images, MICCAI 2011
http://campar.in.tum.de/personal/mateus/2011MiccaiManifoldTutorial/html
32
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
SPECIAL ISSUE
JOURNAL OF PATHOLOGY INFORMATICS
In conjunction with the
Histopathology Image Analysis
(HIMA) workshop at MICCAI 2011
Editors: Anant Madabhushi, Nasir
Rajpoot, Metin Gurcan, Michael D.
Feldman
http://www.jpathinformatics.org/
33
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
ACTIVE RESEARCH
METHODS
MULTIMODAL DATA
INTEGRATION
COMPUTER AIDED
DIAGNOSIS
IMAGE SEGMENTATION
MACHINE LEARNING
IMAGE REGISTRATION
RADIOLOGY
HISTOPATHOLOGY
BIOINFORMATICS
PROSTATE CANCER
OVARIAN CANCER
BREAST CANCER
MEDULLOBLASTOMA
MYELODYSPLASTIC SYNDROME 34
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
METHODS
CLASSIFICATION
FEATURE
SELECTION
ACTIVE LEARNING &
CONTENT BASED
IMAGE RETRIEVAL
IMAGE
SEGMENTATION
MULTI-MODAL
REGISTRATION
NON-LINEAR
DIMENSIONALITY
REDUCTION
KNOWLEDGE
REPRESENTATION
AND MULTI-MODAL
DATA FUSION
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
IMAGE REGISTRATION
36
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Incorporating Histostitcher© into the Extensible Imaging
Platform (XIP) Framework
•
•
Histostitcher algorithm incorporated into professional XIP software framework.
GPU rendering, “Google Maps” – like zooming and scrolling
Previous version of Histostitcher© graphical user interface usable
prototype developed using Matlab.
New version of
Histostitcher©
graphical user
interface developed
using XIP.
37
Toth, R., Chappelow, J., Kutter, O., Vetter, C., Russ, C., Feldman, M., Tomaszewski, J., Shih, N., Madabhushi, A., “Incorporating the Whole-Mount Prostate Histology Reconstruction
Program Histostitcher© into the Extensible Imaging Platform (XIP) Framework,” SPIE Medical Imaging, 2012, Accepted.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
MULTIMODAL IMAGE
REGISTRATION
38
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Histology-MRI Fusion
Step 1: Multiprotocol MRI Registration
T2+DCE
DCE
T2
Step 3: Histology + MRI
Fusion
• Extent of CaP is difficult to find on multi protocol MRI
Step 2: Stitching sections using Histostitcher ©
f
f
1st Stitch
2nd Stitch
3rd Stitch
f
• CaP ground truth exists on high resolution histology fragments
39
Patel, P, Chappelow J, Tomaszewski JE, Feldman MD, Rosen MA, Shih N, Madabhushi A. 2011. Spatially weighted mutual information (SWMI) for registration of digitally
reconstructed ex vivo whole mount histology and in vivo prostate MRI. IEEE International Conference of Engineering in Medicine and Biology Society (EMBS). :6269-627
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Determination of histology-MRI slice correspondences
group-wise
comparison
Final histology-MRI slice
correspondences
2D affine histology-MRI registration
40
itted to Computerized Medical Imaging and
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
KNOWLEDGE REPRESENTATION
AND DATA FUSION
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Weighted Multi-Kernel Learning (WMKL)
Classification Accuracy
A multi-channel framework for merging imaging and non-imaging modalities
for stratifying at-risk prostate cancer patients
WMKL (Fusion)
Proteomics
Architectural
Morphological
35
45
55
65
75
85
95
Lee, G, Feldman MD, Master SR, Tomaszewski, JE, Madabhushi, A. Weighted Multi-Kernel Learning (WMKL): Predicting Biochemical Recurrence by Combining
Histologic Imaging and Proteomic Signatures (Submitted to Bioinformatics 2011)
42
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Supervised Regularized Canonical Correlation Analysis
Objective: Integrate
Histology and
Proteomic data to
make prognostic
decisions
Histology Images
Novel Variation: Uses
a Feature Selection
method to determine
optimal embedding
Proteomic data
Class 1
Score 1
Score 2
:
Score n
T-test
WRST
WLT
Class 2
Optimal
Score
Supervised Variation
SRCCA produces higher classification accuracies
Optimal SRCCA Embedding
43
Golugula, A, Lee G, Master SR, Feldman MD, Tomaszewski JE, Madabhushi A. 2011. Supervised Regularized Canonical Correlation Analysis: Integrating Histologic and Proteomic
Measurements for Predicting Biochemical Recurrence Following Prostate Surgery. Accepted for publication in BMC Bioiformatics 2011
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
IMAGE SEGMENTATION
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
ASM Initialization
MRS signals inside the prostate are
distinct from signals outside the prostate
The cluster with the most signals, in all
experiments, shown to be background
spectra (green below)
Therefore, largest cluster is removed
45
Toth, R., Tiwari, P., Rosen, M., Reed, G., Kurhanewicz, J., Kalyanpur, A., Pungavkar, S., Madabhushi, A. “A Magnetic Resonance Spectroscopy Driven Initialization Scheme for
Active Shape Model Based Prostate Segmentation.” Medical Image Analysis 15 (2), Apr 2011. pp. 214-225.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Prostate Volume Estimation
Red = Ground Truth
Green = ASM Result
R2 = .82
Each voxel:
= 0.22cm x 0.027cm x 0.027cm
= 1.6 x 10-4 mL
R2 = .70
Robert Toth, B. Nicolas Bloch, Elizabeth M. Genega, Neil M. Rofsky, Robert E. Lenkinski, Mark A. Rosen, Arjun Kalyanpur, Sona Pungavkar, Anant Madabhushi., “Accurate
Prostate Volume Estimation Using Multi-Feature Active Shape Models on T2-Weighted MRI,” Academic Radiology, 2011, 18 (2), Jun 2011, pp. 745-754.
Bulman, J.C., Toth, R., Patel, A.D., Bloch, N.B., MacMahon C.J., Ngo L., Madabhushi, A., Rofsky, N.M., "Automated Computer-Derived Prostate Volumes from MRI Data:
Comparison to Radiologist-Derived MRI Volumes and Pathology Specimen Volumes," Radiology 2011. Accepted pending revisions.
46
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Prostate Volume Estimation
(b) Ellipsoid
(yellow) is
generated from
the axes, and
volume of
ellipsoid is
calculated.
Ellipsoid (yellow)
may be
significantly
different from
ground truth
volume (green).
Aggregate of slice
segmentations (pink) are
used to estimate the
prostate volume (green).
47
Toth, R., Bloch, B.N., Genega, E.M., Rofsky, N.M., Lenkinski, R.E., Rosen, M.A.,Madabhushi, A., "Accurate Prostate Volume Determination from T2-w MRI using
Statistical Shape Models," ISMRM 2011.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Prostate Volume Estimation
• R2 = 0.865
160
140
Path Volume (cc)
• Comparison of automated
segmentation prostate volume
(“MFA”) to clinical pathology
volume.
y = 0.888x + 12.554
R² = 0.865
120
100
80
60
40
20
0
0
50
100
150
MFA Volume (cc)
48
Bulman, J.C., Toth, R., Patel, A.D., Bloch, N.B., MacMahon C.J., Ngo L., Madabhushi, A., Rofsky, N.M., "Automated Computer-Derived Prostate Volumes from
MRI Data: Comparison to Radiologist-Derived MRI Volumes and Pathology Specimen Volumes," Radiology 2011. Accepted pending revisions.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Medial Axis Statistical Shape Model
• Markers (“atoms”) along the
medial axis of an object can
capture underlying shape
variations.
• Much less reconstruction
error (dark red on the right)
than traditional landmark
based shape models.
• Accurate shape variations
capture in application to
prostate segmentation
(bottom right)
49
Robert Toth, Rachel Sparks, Anant Madabhushi., “Medial Axis Based Statistical Shape Model (Massm): Applications To 3d Prostate Segmentation On MRI,” ISBI 2011, Under Review.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Landmark Free Statistical Shape Models and Region
Based Statistical Appearance Models
• Levelset function used to
create statistical shape
model.
• Smooth and accurate
• Bayesian based appearance
model adapts to current
image, and is intelligently
combined with statistical
shape model.
• Highly accurate segmentation
results for prostate MRI.
50
Robert Toth, Julie Bulman, Amish Patel, B. Nicholas Bloch, Elizabeth M. Genega, Neil M. Rofsky, Robert E. Lenkinski, Anant Madabhushi. “Integrating an Adaptive Region Based
Appearance Model with a Landmark Free Statistical Shape Model: Application to Prostate MRI Segmentation.” SPIE Medical Imaging 2011, Accepted.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Adaptive Energy Selective Active Contour with Shape Priors for
Nuclear Segmentation and Gleason Grading of Prostate Cancer
- Able to predict Gleason grade 3 vs 4 with 84.1% accuracy
- Using 7 nuclear features extracted from individual nuclei
- Developed an efficient segmentation scheme that selectively invoked shape prior in to active contour.
Evolve
Initial Scene
Segmentation
from
Watershed
Transformation
Detect
Overlap –
Concavity
Detection
Selectively
invoke
Shape Prior
appropriate
level sets
Figure PCA representation of nuclear morphologic features reveals separation of (a)
primary grade 3 and grade 4 and (b) Gleason patterns 6, 7, 8 and 9. How many studies
used here – specify clearly.
51
Ali, S, Veltri R, Epstein JI, Christudass C, Madabhushi A. 2011. Adaptive Energy Selective Active Contour with Shape Priors for Nuclear Segmentation and Gleason Grading of
Prostate Cancer. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) . 6891:661–669
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
GPU Implementation of Integrated Shape Based Active
Contour: Application to Digital Pathology
A GPU accelerated framework, implemented
using the NVIDIAs (CUDA), aimed for the
segmentation of nuclei in (H&E) images based on
an integrated active contour model.
Parallelized implementation of the Ali et al.
scheme with multiple level sets operating in
parallel.
Exploit parallelism and efficient memory
management in the GPU to achieve massive
speed up of 19x compared to CPU.
Ali, S, Madabhushi, A. ”Segmenting multiple overlapping objects via an integrated region and boundary based active contour incorporating shape priors: applications to histopathology”,
SPIE Medical Imaging, 79622W (2011).
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Segmentation of Nodular Medulloblastoma
A novel fusion of Hierarchical Normalized Cuts (HN-Cuts) and
Random Walker (RW) to segment medulloblastoma nodules
(a) Original Image
(b) HN-Cuts
Segmentation
(c) RW Probability
Map
(d) Final
Segmentation
53
Tchikindas, L, Sparks R, Baccon J, Ellison D, Judkins AR, Madabhushi A. 2011. Segmentation of nodular medulloblastoma using Random Walker and Hierarchical Normalized
Cuts. IEEE North-East Bioengineering Conference (NEBEC). :1-2
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
High-throughput Gland Segmentation and Classification as
Benign and Malignant from Digitized Needle Core Biopsies
Detection: a hierarchical frequency
weighted mean shift normalized cut
(HNCut) for initial detection of
glands
HNCut-CGAC model
for gland segmentation
Segmentation: Color gradient based
geodesic active contour model
(CGAC) for gland segmentation
Classification: a diffeomorphic based
similarity (DBS) feature extraction
for classification of glands as benign
or cancerous.
SVM for gland
classification
Morphological
feature extraction
54
Xu, J, Janowczyk A, Chandran S, Madabhushi A. 2011. A high-throughput active contour scheme for segmentation of histopathological imagery. Medical Image Analysis
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
NON-LINEAR DIMENSIONALITY
REDUCTION AND MANIFOLD
LEARNING
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Out-of-Sample Extrapolation Utilizing Semi-Supervised
Learning (OSE-SSL)
Semi-supervised learning
Learn embedding with class labels
known for some samples
Out of Sample Extrapolation
Estimate embeddings for new
samples
OSE-SSL
Integrating the use of known label
information (SSL) and the ability to estimate
new embeddings (OSE). Resulting in an
manifold learning technique that is able to
extrapolate new cases and take semantic
information into account.
56
Sparks, RE, Madabhushi, A. Out-of-Sample Extrapolation for Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval of Prostate Histology. IEEE
International Society of Biomedical Imaging (ISBI) 2011, pp 734-738.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Statistical Shape Model of Manifolds (SSMM)
Changes in dataset
affect low dimensional
embedding obtained
from manifold learning
(b)
(b)
(a)
(c)
(c)
(a)
(a)
Figure 1. Two manifolds for a dataset of 888 prostate
histopathology glands, where each manifold is trained
with only 800 glands.
(1) From a set of manifolds, (2) SSMM constrains the manifold for the
full dataset, giving a robust representation
we construct a SSMM.
(b)
(b)
μ+3σ
(c)
μ
(a)
μ-3σ
(c)
57
Sparks, RE, Madabhushi, A. Gleason grading of prostate histology utilizing statistical shape model of manifolds (SSMM.) In Proc. SPIE 2012, in press.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Semi-Supervised Graph Embedding with Active Learning
New methodology leverages Active Learning with Semi-Supervised
Dimensionality Reduction (DR) for improved data representations
New Data Representation
Original Data Representation
SVM Decision Boundary
New Active
Learning
Candidates
Active Learning
Candidates
improve data
representation
Initial Training
Labels
F
Class 1
Class 2
Query
Unlabeled
Lee, G., Madabhushi A., "Semi-Supervised Graph Embedding Scheme with Active Learning (SSGEAL): Classifying High Dimensional Biomedical Data", in Pattern Recognition in
Bioinformatics (PRIB), LNCS 6282, pp. 207-218, 2010.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
CLASSIFICATION
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Aggregated Distance Metric Learning (ADM) for Image
Classification in Presence of Limited Training Data
BDM
Aggregated distance
t
…
…
…
…
BD
M
t
Final classification via NN
search
Training cohort
of
BDM on each
different classes:
Training image sub-sets
(
: image,
: label)
to get distance
Distance of test image
metric
to each class
t
based on all learned
Xiao, G., Madabhushi, A.: Aggregated distance metric learning (ADM) for image classification in presence of limited training data, MICCAI, Part III, LNCS 6893, 33-40 (2011)
60
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
ACTIVE LEARNING AND
CONTENT BASED IMAGE
RETRIEVAL
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Boosted Spectral Embedding (BoSE): Applications to
Content-Based Image Retrieval
Query,
Annotated Database
…
Feature Extraction
…
…
Feature Weighting
…
Reduce dimensionality of data
using BoSE: it selectively weights
the features that are best able to
discriminate between two classes.
BoSE
PR
Curves
Perform image retrieval in lowerdimensional space created by BoSE
and compare it to image retrieval in
lower-dimensional space created by
spectral embedding (SE).
The greater the area
under the Precision-Recall
curve (AUC), the better
the performance of the
CBIR system.
62
Sridhar, A,. Doyle S., Madabhushi, A. Content-Based Image Retrieval of Digitized Histopathology in Boosted Spectrally Embedded Spaces . IEEE Transactions on
Information Technology in Biomedicine, November 2011. (submitted)
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Content Based Image Retrieval (CBIR) with Explicit
Shape Descriptors
a. Database Image
Feature Extraction
OSE-SSL
Results
b. Learn Embeddings
c. Query Image
Feature Extraction
d. Extrapolate Query
e. Image Retrieval
….
Precision –Recall Curves. OSESSL has better performance rates
compared to manifold learning or
OSE. Evaluated on a prostate
histopathology dataset containing
888 glands.
63
Sparks, RE, Madabhushi, A.. Content-based image retrieval utilizing Explicit Shape Descriptors: applications to breast MRI and prostate histopathology. Proc. SPIE 2011, Vol. 7692 pp.
79621I--13.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Consensus Active Learning
Active Learning Example
Random Learning:
All Samples Eligible
Active Learning:
Intermediate Samples Eligible
Consensus Learning:
Intermediate Samples from Multiple Algorithms
+
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
High
Certainty
Intermediate
Certainty
High
Certainty
=
Eligible for
Uninformative
Consensus Learning
Labeling
Points
Points
Active Learning (left) identifies samples whose class is uncertain, and these samples
are assigned to an expert for labeling. Compared with Random Learning (top right),
Active Learning reduces the number of eligible samples for labeling. Multiple Active
Learning algorithms can be combined (bottom right) to generate a consensus learning
set, which yields an accurate classifier with fewer samples eligible for labeling.
Doyle, S, Monaco JP, Feldman MD, Tomaszewski JE, Madabhushi A. 2011. An Active Learning Based Classification Strategy for the Minority Class Problem: Application to
Histopathology Annotation. BMC bioinformatics. 12(1):424
64
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
BIOINFORMATICS APPLICATIONS
FEATURE SELECTION/EVALUATION
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Variable Ranking with PCA
A method for finding biomarkers applied to identify multiparametric
MR imaging markers for prostate cancer diagnosis and grading
Identify imaging markers
Perform PCA
Compute PCA-VIP
66
Ginsburg, S, Tiwari, P, Kurhanewicz, J, Madabhushi, A. 2011. Variable ranking with PCA: Finding multiparametric MR imaging markers for prostate cancer diagnosis
and grading. Workshop on Prostate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention (in conjunction with MICCAI). 6963:146-157.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Robustness of a Feature Selection (FS) Scheme
Problem: Classification Accuracy, de facto measure to evaluate FS, is not always sufficient
Objective: To determine a new measure to evaluate FS in the context of small sample size data
With subsampling to create a
small sample size, few features
appear more than 10 times and
results are not consistent
High Dimensional Proteomic Feature vector
Robustness: higher is more optimal
67
Golugula, A, Lee, G and Madabhushi, A, Evaluating Feature Selection Strategies for High Dimensional, Small Sample Size Datasets, IEEE Engineering in Medicine and Biology
Conference, 2011
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
COMPUTER AIDED DIAGNOSIS
(CAD)
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
CAD FOR PROSTATE MRI
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Evaluation of bias field correction (BFC) schemes for
prostate cancer detection on T2w MRI
Compared 3 different algorithms, N3 found to offer best BFC.
Choice of BFC scheme should be based on overarching application,
current evaluation measures not dependent on application.
70
Viswanath, SE, Palumbo D, Chappelow J, Patel P, Bloch BN, Rofsky NM, Lenkinski RE, Genega EM, Madabhushi A. 2011. Empirical evaluation of bias field
correction algorithms for computer-aided detection of prostate cancer on T2w MRI. SPIE Medical Imaging. 7963:79630V.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Interplay between bias field correction, intensity
standardization, and noise filtering for T2w MRI
Quantitatively evaluated 7 different combinations of 3 pre-processing
operations, determined best for optimal prostate cancer detection
Original
After
Bias field
correction
After
Noise
correction
After
Intensity
standardization
Histograms from 4 different studies showing the effect on the intensity distributions after each operation. Last set show relatively noisefree, well-aligned distributions, shown to be most optimal for classification.
71
Palumbo, D, Yee B, O'Dea P, Leedy S, Viswanath SE, Madabhushi A. 2011. Interplay between bias field correction, intensity standardization, and noise filtering for
T2-weighted MRI. IEEE International Conference of Engineering in Medicine and Biology Society (EMBS). :5080-5083.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Determining Quantitative Imaging Signatures (QIS) for Central
Gland and Peripheral Zone Prostate Tumors on T2-w MRI
Determined that
unique
combinations of
texture features
characterize
each of CG and
PZ CaP
Classification via
QDA yielded
0.86 AUC for
CG CaP, 0.74
AUC for PZ
CaP
Central Gland (CG) Prostate Cancer
Original MRI +
CaP extent
Top texture
feature in QIS
CaP detection result
based off QIS
Peripheral Zone (PZ) Prostate Cancer
72
S. Viswanath, BN Bloch, JC Chappelow, R Toth, NM Rofsky, EM Genega, RE Lenkinski, A. Madabhushi, Central Gland and Peripheral Zone Prostate Tumors have Significantly Different
Quantitative Imaging Signatures on 3 Tesla Endorectal, In Vivo T2-Weighted Magnetic Resonance Imagery, JMRI, (Provisionally accepted), 2011
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Multi-modal Weighted Embedding Representation for data
Combination (MaWERiC) for prostate cancer diagnosis
Feature Extraction
Wavelet Feature
Extraction- MRI
Wavelet Feature
Extraction- MRS
Data Representation
0.6
DR on high D Gabor
features
0.4
0.2
0
-0.2
DR on high D
wavelet MRS features
0.6
0.4
0.2
0
-0.4
0.6
-0.2
0.4
0.3
0.4
0.2
0.2
0.1
0
-0.2
0.4
0.2
0
-0.1
0.2
0
-0.2
0
-0.2
-0.2
-0.4
-0.4
-0.6
Data Integration
(MRI+MRS)
Data classification
Tiwari, P, et al.. Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer
detection. NMR in Biomedicine, 2011.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Weighted Combination of Multi-parametric MR markers for
evaluating radiation therapy related changes in the prostate
Heat map for treatment evaluation
Expert delineated
ground truth
Metabolic map
T2w MRI
MP-MRI map
ADC map
High
treatment
change
CaP
recurrence
CaP pre-RT
New CaP foci
Low
treatment
change
74
Tiwari, P, et al. . Weighted Combination of Multi-Parametric MR Imaging Markers for Evaluating Radiation Therapy Related Changes in the Prostate. Workshop on Prostate Cancer Imaging:
Computer-Aided Diagnosis, Prognosis, and Intervention (in conjunction with MICCAI). 6963:80-91
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Concurrent segmentation of the prostate on MRI and CT
via linked statistical shape models for radiotherapy planning
75
Chowdhury, N, ,Toth R, Chappelow J, Kim S, Motwani, S, Punekar, S, Hahn S, Vapiwala N, Lin H, Both S, Madabhushi A. Concurrent segmentation of the prostate
on MRI and CT via linked statistical shape models for radiotherapy planning (accepted, pending changes to Medical Physics)
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Multimodal integration of magnetic resonance imaging and
spectroscopy for detection of high grade prostate cancer
Clinical Goal: To quantitatively combine imaging (T2-w MRI) and nonimaging (MRS) data
to distinguish high (aggressive) vs. low-grade (indolent) prostate cancer (CaP)
Step 1. Integrated low dimensional
representation of T2w MRI/MRS
1
0.9
0.8
Receiver operating characteristic
(ROC) curve for high vs. low grade
CaP classified on a per voxel basis
via a RF classifier.
0.7
0.6
0.5
0.4
0.3
T2-w MRI
MRS
T2-w MRI + MRS
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
76
Tiwari, P, Kurhanewicz J, Rosen MA, Madabhushi A. 2010. Semi Supervised Multi Kernel (SeSMiK) Graph Embedding: Identifying Aggressive Prostate Cancer via Magnetic
Resonance Imaging and Spectroscopy. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 6363:666-673.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
CAD FOR PROSTATE WHOLE
MOUNT HISTOLOGY
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Detection of Prostate Cancer on Histopathology using
Color Fractals and Probabilistic Pairwise Markov Models
Novel per pixel color fractal dimension algorithm for whole-mount
prostate histopathology
Incorporated novel Markov Prior (PPMM) for spatial dependencies
Provide means for tumor volume estimation; facilitate initial step of
Gleason grading; reduce inter-expert variability
Something
Ground Truth
CFD Alone
CFD + PPMM
78
Yu E, Monaco JP, Tomaszewski JE, Shih N, Feldman MD, Madabhushi A. 2011. Detection of prostate cancer on histopathology using color fractals and Probabilistic Pairwise
Markov models. IEEE International Conference of Engineering in Medicine and Biology Society (EMBS): 3427-3430.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Two Novel Means for Varying the Performance of
MRF-based Classification Systems
Prostate histology images
First method allows
variation of
performance of
MRFs classified
using MAP
estimation
Second method
allows variation of
performance of
MRFs classified
using MPM
estimation
79
J Monaco and A Madabhushi, Class-Specific Weighting for Markov Random Field Estimation: Application to Medical Image Segmentation, submitted to Medical Image Analysis.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Weighted Maximum Posterior Marginals for Random Fields
(a) H&E stained whole-mount prostate histology section; black ink mark
indicates “ground-truth” of CaP extent as delineated by a pathologist. (b) Result
of automated gland segmentation. (c) Magnified view of white box in (b). (d)
Green dots indicate the centroids of those glands labeled as malignant.
80
Monaco, JP, Madabhushi A. 2011. Weighted Maximum Posterior Marginals for Random Fields using an Ensemble of Conditional Densities from Multiple Markov Chain Monte
Carlo Simulations.. IEEE Transactions on Medical Imaging
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Cascaded Multi-Category Classification
Dataset
Cancer
Grade 3+4
Grade 3
Non-Cancer
Grade 5
Grade 4
• Distinguish classes sequentially, determined via domain knowledge
• Advantages: Binary decision boundaries, simple classifiers, optimized
feature sets, high class separability, minimal class heterogeneity
Doyle, S, Feldman MD, Tomaszewski JE, Shih N, Madabhushi A. 2011. Cascaded Multi-Class Pairwise Classifier (CascaMPa) For Normal, Cancerous, And Cancer Confounder
Classes In Prostate Histology. IEEE International Symposium on Biomedical Imaging (ISBI). :715-718
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
CAD FOR BREAST DCE MRI
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Spectral Embedding based Active Contour (SEAC)
Spectral
Embedding
Active contour
segmentation
Agner, S, Xu J, Rosen MA, Karthigeyan S, Englander S, Madabhushi A. 2011. Spectral embedding based active contour (SEAC): application to breast lesion segmentation
on DCE-MRI. SPIE Medical Imaging. 7963
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Spectral Embedding based Registration Registration of Breast
Dynamic Contrast Enhanced Magnetic Resonance Imaging
Unregistered
SI Registered
SERg
84
Karthigeyan S, Agner, S, et al. Submitted for Aresty
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Explicit Shape Descriptors Quantifying Breast DCEMRI Morphology
Dissimilar
High
Value
Similar
Low
Value
85
Sparks, RE, Madabhushi, A. Computerized Classification of Benign and Malignant Breast Lesions on DCE-MRI Utilizing Novel Shape Descriptors. ISMRM
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
CAD ON BREAST CANCER
HISTOPATHOLOGY
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Tubule Detection in Breast Cancer Histopathology
Identifying tubules by quantifying the spatial arrangement of nuclei and lumen
objects
87
Basavanhally, A, Yu E, Xu J, Ganesan S, Feldman MD, Tomaszewski JE, Madabhushi A. 2011. Incorporating domain knowledge for tubule detection in breast histopathology using
O'Callaghan neighborhoods. SPIE Medical Imaging. 7963:796310.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Boosted Classifier For Integrating Multiple Fields Of
View: Breast Cancer Grading In Histopathology
Combining FOVs at various sizes to yield improved grading for ER+ breast
cancer patients
Multi-FOV Framework
Results
FOV size
4000 x 4000
2000 x 2000
1000 x 1000
500 x 500
250 x 250
multi-FOV
AUC
0.573
0.702
0.787
0.819
0.605
0.816
Basavanhally, A, Ganesan S, Shih N, Mies C, Feldman MD, Tomaszewski JE, Madabhushi A. 2011. A Boosted Classifier For Integrating Multiple Fields Of View: Breast Cancer
Grading In Histopathology. IEEE International Symposium on Biomedical Imaging (ISBI). :125-128
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Multi-Parametric, Multi-FOV ER+ Breast Cancer Prognosis:
Combination of H & E and IHC Stained Histopathology
Independent multi-FOV classifiers for tissue architecture (H & E) and CD34
expression (IHC) are combined to yield improved prognostic prediction
Tissue architecture
Patient
Prediction
of patient
outcome
CD34 expression
Basavanhally, A, Feldman MD, Shih N, Mies C, Tomaszewski JE, Ganesan S, Madabhushi A., Multi-field-of-view strategy for image-based outcome prediction of multi-parametric
estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX, Journal of Pathology Informatics, 2011 (in press).
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
CAD ON OVARIAN CANCER
TISSUE MICROARRAYS
90
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Local Morphologic Scale (LMS): Classifying Tumor Infiltrating
Lymphocytes in Ovarian Cancer Tissue MicroArrays
TILs
Non-TILs
1. Identify lymphocytes using HNcut
2. Quantify local morphology using LMS
3. Generate features from LMS signature
4. Use supervised classifier to identify
Tumor Infiltrating Lymphocytes (TILs),
a prognostic indicator for Ovarian cancer
A. Janowczyk, S. Chandran, M. Feldman, A. Madabhushi Local Morphologic Scale: Application to Segmenting Tumor Infiltrating Lymphocytes in Ovarian Cancer
Tissue MicroArrays SPIE Medical Imaging 2011.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
CAD FOR THE DETECTION OF
MYELODYSPLASTIC SYNDROME
92
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Automated Detection of Myelodysplastic Syndrome
Goal: differentiate MDS from normal
tissue via the analysis of islands
erythroid precursors
Procedure: Step1) Segment islands,
Step2) Extract features, Step 3)
Classify images
MDS tissue specimen (erythroid precursors in brown)
Map of adipose (black) and everything else (white)
85th Percentile of Area (cyan)
Adipose Area (green)
Both (black)
93
Segmentations or precursor islands
Classification Results: Receiver Operator Characteristic Curve
P Raess, J Monaco, R Chawla, A Bagg, M Weiss, J Choi, and A Madabhushi, Image Segmentation with Implicit Color Standardization Using Cascaded EM: Detection of
Myelodysplastic Syndromes, United States and Canadian Academy of Pathology's 101st Annual Meeting, March 17-23, 2012, Accepted
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
CLASSIFICATION OF
ANAPLASTIC
MEDULLOBLASTOMA
94
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
An Integrated Texton and Bag of Words Classifier for
Identifying Anaplastic Medulloblastomas
Determine the aggressiveness of medulloblastoma to help construct personalized
treatment plans for patients, a novel application of the Texton and Bag of Words
classification scheme
Currently achieving an average of .76 classification accuracy, a .17 increase over
initial parameters
Original
Image
Feature
Generation
Texton
Application
Texton Map
Texton Model
for Classifying
Bag of Words
Application
Galaro, J, Judkins AR, Ellison D, Baccon J, Madabhushi A. 2011. An integrated texton and bag of words classifier for identifying anaplastic medulloblastomas. IEEE International
Conference of Engineering in Medicine and Biology Society (EMBS). :3443-3446.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
A Texture-based Classifier to Discriminate Anaplastic
from Non-Anaplastic Medulloblastoma
Haar
Haralick
Laws
Texture features in conjunction with
ensemble classification found to allow
differentiation between anaplastic and nonanaplastic medulloblastoma
96
Lai, Y, Viswanath SE, Baccon J, Ellison D, Judkins AR, Madabhushi A. 2011. A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma. IEEE North-East
Bioengineering Conference (NEBEC). :1-2.
Laboratory for Computational
Imaging and Bioinformatics (LCIB)
Annual Report 2011
Funding Agencies (Acknowledgements)
•
•
•
•
•
•
•
•
National Cancer Institute (NIH)
Department of Defense (DOD)
New Jersey Commission on Cancer Research (NJCCR)
Society for Imaging Informatics in Medicine (SIIM)
Aresty Foundation
Rutgers University
Cancer Institute of New Jersey
Burroughs Wellcome Fund