Lecture
TR 12:30-1:50, Bossone 605, January 4 – March 19, 2016
Course Website http://bioimage.coe.drexel.edu/courses/CTIA
Important: before the 2 nd meeting, verify access to the course website! All lecture notes will be posted, typically in advance. Homework assignments will be posted on the last slide of the weekly lecture notes.
Andrew R Cohen, PhD: e-mail: acohen@coe.drexel.edu (preferred contact) office: Bossone 110 lab: Bossone 514 office hours: TBD
Both texts are optional, but very useful. If you have a strong MATLAB background, but no biology, consider text #2. Likewise, if you have a strong biology background but not much programming or
MATLAB, consider text #1.
1. Gonzales, R., R. Woods, et al. (2009). Digital Image Processing Using MATLAB. Knoxville,
TN, Gatesmark Publishing.
2. Alberts, Bruce. Essential Cell Biology: An introducton to the Molecular Biology of the Cell .
Vol. 3. Garland, 2009.
Image analysis is the business of making quantitative measurements from images. In this course, we focus on multi-dimensional images of biological cells, tissues, and organisms captured by modern optical microscopes.

Andrew R. Cohen Cell & Tissue Image Analysis - Winter 2014/2015 - Syllabus Page 2 of 6
Modern microscope imaging systems are amazing (and expensive!). They can capture 3-D structure and location of cells and tissue. By fluorescence multiplexing, we can image multiple structures and functional markers in their relative context. Using time-lapse imaging combined with a set of techniques for live-cell imaging, we can record changes over time. These changes could represent things like structural dynamics, cell movements, and transport of molecules within a cell. Combining the above methods allows us to record biological processes in their spatial & temporal context.
Images from modern biological microscopes are multi-dimensional, capturing the dimensions of space, optical properties of tissue, spectra representing chemical information, and the dimension of time.
This course is about systematic ways to make quantitative measurements from these images.
Basically, we are interested in two kinds of measurements: intrinsic, and associative. Intrinsic measurements quantify aspects of objects in each imaging channel. Associative measurements describe the relationships among these objects in space and time. These measurements can be combined with other forms of bio-informatics data to understand cells and tissue.
This course is aimed at a multi-disciplinary audience. Students in Biology, Biomedical
Engineering (BME), Electrical Engineering (EE), and Computer Science (CS) can all expect to benefit. You should have some prior programming experience (MATLAB would be perfect). If you are an EE or CS major, you must be willing to learn some basic biology. If you are a
Biology or BME major, you must be willing to pick up MATLAB programming skills.
•
Image analysis and quantification needs arising in hypothesis testing, systems biology, assays, structure and function studies, drug discovery, toxicology, and tissue engineering

Andrew R. Cohen Cell & Tissue Image Analysis - Winter 2014/2015 - Syllabus Page 3 of 6
–
Mini tour of modern cell biology and histology
–
Issues in Cell and Tissue Image Analysis
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Examples from contemporary applications throughout the course.
•
Review of modern biological imaging systems
– Basics of optical microscopy instrumentation
–
Low-level algorithms (deconvolution, unmixing)
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Molecular imaging using fluorescence and related methods such as FLIM, FRET
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Fluorescent labeling methods (immunofluorescence, fluorescent proteins);
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High-throughput and time-lapse microscopy;
• Core Image Analysis Methods
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Limitations of manual and stereological assessment
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Common morphologies of compartments, surfaces and signals;
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Segmentation algorithms for core image objects;
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Algorithms for tracking moving objects and change analysis;
– Object classification and clustering algorithms;
– Intrinsic Measurements (Feature extraction);
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Primary Associative Measurements of spatial, temporal, and functional relationships;
– Protocols for classical and edit-based validation, and performance assessment
– Secondary Associative Measurements and Cell networks
• Assignments (roughly one per week) – 50%
• Term Project – 50%
• Class Participation – 2%
• Generous reward for effort

Andrew R. Cohen Cell & Tissue Image Analysis - Winter 2014/2015 - Syllabus
Grading Scale
A
A-
B+
B
B-
95 and above
90-94
86-89
82-85
78-81
C+
C
C-
D
F
74-77
70-73
66-69
60-65
<60
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This design of this course has been adapted with permission from a course originally taught by Prof.
Badri Roysam.
The lectures are designed to give you a broad understanding of the subject. The term project is designed to give you an in-depth understanding of a selected topic. The ideal term project is a cross-disciplinary one. It is conducted by a team of two students – one student is from Biology or BME, and the other is from EE or CS. Each student contributes equally.
At the end of the semester, each term project will be presented in the form of a poster presentation. The location and time of the presentation will be announced in class. It is usually held during Finals Week.
Homework should be submitted to the class grader: Eric Wait by email to : ericwait@drexel.edu
.
Please cc: me on all submissions : acohen@coe.drexel.edu
.
Homework submitted after 6pm on the day it is due will be counted as late, with 10% deducted.
An additional 10% will be deducted for every 24 hours. No homework will be accepted after one week.
If you are taking the graduate section (690) of this course, it is expected that your term research project will be significantly more advanced than an undergraduate research project. The term project will likely relate and, importantly, extend your doctoral or master’s thesis, and may constitute a chapter in that document. If you are an undergraduate or non-thesis option graduate student you may find this to be a significant amount of extra effort. If you are uncertain as to the scope of the expected research, please contact Dr. Cohen before the add / drop deadline.

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Graduate participants will be expected to complete extra reading / homework assignments as well.
Attendance and participation are mandatory at all lecture and lab sections. If you are sick do not come
to class. If you must miss lecture, be sure to notify Prof. Cohen.

Students will obtain a familiarity with the principles of biological microscopy for both live and fixed cell and tissue.

Students will be familiarized with the prinicples of optical imaging systems, including fluorescence and phase contrast microscopy.
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Students will be introduced to the basic functionality included in the MATLAB image processing toolkit.
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Students will learn the basic steps of denoising, thresholding, segmenting, tracking and classifying objects measured from biological microscopy imaging data.
Any instances of cheating or plagiarism will be treated with the utmost seriousness. The lab and lecture assignments for this course are not intended to be collaborative. If you are having difficulty with any of the course assignments or concepts, consult with the instructor or TA.
The University policy on plagiarism and academic integrity can be found at: http://www.drexel.edu/studentlife/community_standards/studentHandbook/general_information/cod e_of_conduct/ http://www.drexel.edu/provost/policies/academic_dishonesty.asp
Every effort will be made to accommodate students with documented disabilities, as described in the official website for the Drexel office of equality and diversity: http://www.drexel.edu/oed/disabilityResources/faculty/ExpectationsOfFaculty/
Drexel policy for dropping or withdrawing from classes can be found here:

Andrew R. Cohen Cell & Tissue Image Analysis - Winter 2014/2015 - Syllabus http://www.drexel.edu/provost/policies/course_drop.asp
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This syllabus is a "living document" that can be changed during the term at instructor's discretion. These changes will be communicated in lecture and also distributed via the course website.