Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan...
Transcript of Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan...
Chandan Chakraborty, PhDAssociate Professor
BIOSTATISTICS & MEDICAL INFORMATICS LabSchool of Medical Science & Technology
IIT Kharagpur
Computational Pathology
Research Areas [ BMI Lab ]
Histopathology
Cytology
Ultrasonography (USG)
Brain MRI
Fundus Imaging
Computer Vision & PatternRecognition for Medical Imaging
Biostatistics & Medical Informatics
Clinical Risk Evaluation
ECG Analysis
Web-enabled Malaria-Screening System
DR screening system
Bioinformatics
Histopathological tissue evaluation for Oral Submucous Fibrosis Detection – Computer Vision Approach
OSF – Oral Submucous Fibrosis [Without and With Dysplasia]
Conventional diagnosis:
Biopsy samples of Oral Tissue & Stained
Microscopic image evaluation of STAINED tissue section
Diagnostic markers:
Epithelium tissue, Basal Cell & its Nuclei,
Basement Membrane, Cell Population
10x magnification (H&E) epithelial thickness
100x magnification (H&E) basal cell
40 x magnification (PAS) basement membrane
Texas Instr.Funded byTexas Instr.
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Texture based Approach- Self similar pattern of cells- Discontinuity & similarities- Grayscale image gradient extraction
Texture characterization using Gabor filters- Texture gradient computation
2 2
2 212 21, ,
2
x yi fxg x y f e e
' ' ' ', , , ; cos sin , cos sinmn m n n n ng x y g x y f x x y y y x
Epithelium Segmentation
Krishnan et al. Micron’11
Texture Gradient Computation- Texture gradient from multichannel data- Vector gradient to detect boundaries - Computer eigen value and vectors and Texture gradient- Intensity gradient computation for upper border of epithelial layer
- Final gradient: , , ,G x y m I x y TG x y
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Texture Gradient Computation
Channel 1 Channel 2 Channel n
Original epithelial image (RGB)
Image PreprocessingContrast enhancement, Wiener filtering and Shading correction
Color space conversionRGB -> Lab
Color space conversionRGB -> Grayscale
Convolution with Gabor Filter
Intensity Gradient Computation
Gradient Combination and Minima Selection
Watershed Transform
Multistep Region Merging
Post ProcessingEdge enhancement and morphological operations
Text
ure
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Texture Gradient based Epithelium Segmentation
Chakraborty et al. J Tissue & Cell’11Krishnan et al. Microscopy, Spain ’11
Region merging• Combines smaller regions using
Hotelling T2 <
• Stopping criteria: Intensity diff. * Histogram diff. Textural diff.
225, , 40 and 10ij i j ijH R R TS
24 output texture channels for 6 orientations at 4 scales
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Segmentation Accuracy by Pixel Classification method = 98%
Results
Normal
OSF without dysplasia
OSF with dysplasia
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Quantitative Microscopy to Basal Cell NucleiMicroscopic
Imaging
Basal layer extraction(fuzzy divergence, morphological operations)
Basal cell nuclei segmentation(color deconvolution, watershed, morphological operations, GVF)
Feature extraction(morphological, textural)
Feature selection(unsupervised feature selection)
Normal OSF
Preprocessing(median filtering, anisotropic diffusion)
Classification(Bayesian, SVM, k-means, FCM, GMM)
Training set
Testing set (k-fold cross validation)
Krishnan et al. ESWA’12
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Fitting the parabola for extracted edge. Generation of n parallel parabolas to fitted parabola (Rust 2001).AND operation between original image and image generated from sequence of parabolas.
Enhancement of the nuclei by color deconvolution Morphological operations to segregate each nucleus.Image segmentation using Watershed algorithm & define each segment as pseudo cell.Separation of nucleus from cell by color deconvolution and GVF snakes
(a) (b) (c)
(a) Extracted basal layer(b) Contrast enhanced nuclei using color
deconvolution(c) Thresholded image of (b) using fuzzy
divergence(d) After performing morphological
operations on image (c)(e) Watershed output over image (d)(f) Segmented boundaries of basal cells (g) superimposed on the extracted basal
layer
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(a) Area, (b) Perimeter, (c) Compactness (d) Eccentricity (e) Fourier descriptors (f) Zernike moments.
Nuclei tracking using GVF snakes
Initial boundary around nuclei using watershedWhite and dark spots present in NucleiActive contour curves evolve from internal and external forces GVF – a class of external forces for active contours to capture boundary
concavities by considering magnitude and direction of gradients
Basal cell image Gradient image Deformation of the contour Final contour
Nuclei Feature extraction
Krishnan TCRT’11
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EXTRACTED FEATURES
STATISTICALLY SIGNFICANT FEATURES’ SELECTIONt-test and one-Way ANOVA
FEATURE RANKING F-statistics and Information Gain
COMPRESSION using PCA
MULTICOLLINEARITY ?
NoYes
Optimal Set of Ranked Features Optimal Set of Principal
Components
Statistical Analysis for Feature Space Optimization
Bayesian Classification
Decision problem:
Bayes’ Rule:
A statistical classifier: Performs probabilistic prediction,
i.e., predicts class membership probabilities
Naïve Bayes:
Assuming all Features Are INDEPENDENT
Gaussian Mixture Model based Classification
Class weight, class prior probability, multinomial
Multivariate Normal
Number of hidden components
Normal parameters
Observations
Class weights
Normal = Gaussian
A formalism for modeling a probability density function as a sum of parameterized functions.
mm
M
mm xPxP
,,1
GMM: EM estimation
E-Step :
M-Step:
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MethodologyStep1: SECT cell segmentation using multilevel
thresholding.Step2: Morphometric (compactness and eccentricity)
feature extraction.Step3: SECT cell classification using SVM.
Normal Group OSF GroupRound shape
cellsSpindle shape
cellsRound shape
cellsSpindle shape
cellsCompactness 12.28±6.33* 13.99±13.03* 12.25±6.35* 14.49±13.21*Eccentricity 0.66±0.15* 0.25±0.19* 0.65±0.15* 0.24±0.19*
Normal (n=730) OSF (n=1110)SECT cell population 36.50±5.77* 54.70±17.13*
Sensitivity=90.47%
Specificity=87.54%
Accuracy=88.69%
Quantitative analysis of SECT cell population
Chakraborty et al. CBM’09
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Classifiers Sensitivity (%) Specificity (%) Accuracy (%)k-means 84.44 83.22 84.00
FCM 90.14 88.18 89.45GMM 89.62 91.73 90.37
Classifiers Sensitivity (%) Specificity (%) Accuracy (%)Bayesian 96.43 96.62 96.56
SVM 99.74 99.53 99.66
Classification techniques & performance evaluation
Krishnan et al. J Med Syst’10
Overview of oral biopsy screening system for OSF diagnosis
ThicknessECTI featuresTexture Features
Inflammatory CellsFibroblast Cells
BM thicknessBasal CellIrregularity
Epithelial Layer
BM LayerMagnification: 10×
Magnification: 40×SECT Layer
Magnification: 40×
Machine LearningSVM & GMM
Sensitivity: 90.47%Specificity: 87.54%
Publication: Jnl Papers = 16 US Patent = 1 Conf. = 6 Book chap. = 4
Web-enabled Quantitative Microscopy for Blood Smear ScreeningTo develop CAD system for malaria, anemia using light
microscopic imagesFunded byDIT, GoI
Web-enabled Malaria Parasite Screening
System
IBM Faculty Award ‘12 SPRAD’10