醫療影像處理在診斷上之應用
嘉義大學資工系 教授
時間: 2009年5月13日
柯建全
Outline
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Introduction
Object of medical image processing
Imaging devices
applications
Related techniques for Medical
imaging
Research Results
Future works
Introduction
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What is Medical imaging?
Why do we need digital image
processing?
What kind of problems are often
caused in medical images?
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Blurring caused by respiratory or motion
Low contrast caused by imaging device or
resolution
Complicated textures
Research trends have been transferred
from 2-D to 3-D reconstruction
Introduction (continue)
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Integrate all possible methods in
the filed of DIP, pattern recognition,
and computer graphics
Qualitative
 Quantitative
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Three categories of imaging in
different modalities
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Structural image
Functional image
Molecular image
Object
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Help physicians diagnose
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Reduce inter- and intra-variability
Produce qualitative and quantitative
assessment by computer
technologies
Determine appropriate treatments
according to the analyses
Surgical simulation or skills to
reduce possible erros
Medical Imaging Modalities
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X-ray
Ultrasound: non-invasive
Computed tomography
Magnetic resonance imaging
SPECT (Single photon emission
tomography)
PET( Positron emission tomography)
Microscopy
X-ray
Ultrasound
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2-D sonography
3-D sonography
Doppler color sonography
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A series of 2-D projection
Reconstruction
4-D sonography
Computed tomography
MRI
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可以觀察活體三度空間的斷層影像
磁振影像取影像時可以適當控制而得到不
同參數的影像，如溫度、流場(flow)、水
含量、分子擴散( diffusion)、 灌流
(perfusion)、化學位移(chemical shift)、
功能性(functional MRI) 及不同核種如
氫、碳、磷
MRI-structural and functional image
Related techniques
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Image processing
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Segmentation
Registration
Feature Extraction
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Shape feature
Texture
Motion tracking
Pattern recognition
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Supervised learning
Un-supervised learning
Neuro network
Fuzzy
Support vector machine(SVM)
Genetic algorithm
Related techniques
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3-D graphic
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Virtual diagnose or visualization
Fusion between different modalities
Bio-medical visualization
SPECT-functional image
PET(Positron Emission Tomography )
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PET以分子細胞學為基礎，將帶有特殊標記的葡
萄糖合成藥劑注入受檢者體內，利用PET掃瞄儀
的高解析度與靈敏度作全身的掃描，藉由癌細胞
分裂迅速，新陳代謝特別旺盛，攝取葡萄糖達到
正常細胞二至十倍，造成掃描圖像上出現明顯的
「光點」
能於癌細胞的早期(約0.5公分)準確地判定癌細
胞，提供醫師作為診斷及治療的依據，診斷率高
達87-91％，30歲以上的成年人及有癌症家族史
的民眾，建議每隔1～2年做一次PET檢查。
PET (Positron emission tomography)
Applications in a hospital
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Assist surgeon plan surgical operation or
diagnose
Picture archiving system (PACS)
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將醫療系統中所有的影像，以數位化的方式儲存，並經
由網路傳遞至同系統中，供使用者於遠側電腦螢幕閱讀
影像並判讀。
Telemedicine
Surgical simulation: Medical Visualization，
Surgical augmented Reality, Medicalpurpose robot, Surgery Simulation，Image
Guided Surgery，Computer Aided Surgery
Estimate the location, size and shape of
tumor
PACS System
Virtual Surgery
Related techniques
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Classification of normal or abnormal
tissues such as carcinoma
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Pre-processing: Contrast enhancement,
noise removal, and edge detection
Lesion segmentation: extract contours
of interest
thresholding
 2-D segmentation
 3-D segmentation based on voxel data
 Color image processing
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Our study
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Contour detection and blood flow
measurements in cardiac nuclear
medical imaging
Virtual colonoscopy
Bone tumor segmentation with MRI
and virtual display
Breast carcinoma based on
histology
原
始
系
列
影
像
影
像
放
大
影
像
強
化
影
像
去
雜
訊
影像前處理
左心室輪
廓偵測
心室功能
計算
(a)強化後影像
(b)心臟血流變化區域
(c)心臟區域輪廓
Background
Region
Contours within a sequence of frames
Result
No.
EF
ES
ED
PER
PFR
1
16.3
558 ml
667ml
-0.7
0.4
2
37.4
256ml
775ml
-1.12
1.87
3
53.5
56ml
120ml
-0.56
2.67
4
84.3
60ml
380ml
-1.33
4.21
Tab 4.1 心室功能量測參數
Virtual colonscopy-Browsing or navigation
within a colon
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Helical CT –patients injected contrast
medium
Re-sampling—Voxel-based
Interpolation
Automatic segmentation (seed)
 threshloding
Determination of the skeleton of the colon
Connected-Component Labeling
Surface rendering and volume rendering
Extraction of suspicious sub-volumes for
diagnosis
Automatic segmentation
Determination of the skeleton of the
colon
Display and measurement
Bone tumor segmentation with MRI and
virtual display—Contrast medium
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Otsu thresholding
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Region growing
Tri-linear interpolation
Morphological post-processing
Surface rendering
Measurement
Histogram of T1 weighted and T2 weighted
Classification of Breast Carcinoma
開始
輸入組織影像
(1524*1012)
色彩分離
(RGB)
影像分割
(Gray level、Otsu、Laplacian)
特徵參數分析
(導管比例、管腔個數、組織紋理...)
貝式網路判斷
正常
異常
系統判斷為正常
12
6
系統判斷為異常
1
11
準確性
76.67%
敏感度
有效性
64.71%
92.31%
Requirements for medical image
processing system in clinical diagnosis
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Automatic and less human interaction
Qualitative and quantitative measurements
Stable and reliable (experiments with much
more cases)
Performance evaluation
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True positive, true negative, false positive, false
negative
Accuracy, sensitivity, and specificity
Receiving operating characteristic curve (An index
for evaluating the effectiveness of classification
 Optimal classification threshold
 Area under ROC approach 1 – better classification
ROC curve
Analyses of prognosis on breast cancer
for a stained tissue
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Microscopy with different resolution (400
or 100) for a stained tissue
Fluorescent microscopy in detecting the
number of chromosome
Immunohistochemistry(IHC)
Preliminaries or problems ?
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Blurring often caused by patient motion or
respiration
Clinical opinion or idea obtained from an
experienced surgeon
Non-absolute answers at some specific
conditions
Trade-off between complexity and
performance
Large variations for different image
modality
Automation is necessary so as to help
physicians
Prove identification accuracy—comparison
between manual and image processing
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Thanks for your attention!