chandan chakraborty, phdcse.iitkgp.ac.in/conf/cbbh/lectures/chandan_comppathology.pdf · chandan...

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Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School of Medical Science & Technology IIT Kharagpur Computational Pathology

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Page 1: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

Chandan Chakraborty, PhDAssociate Professor

BIOSTATISTICS & MEDICAL INFORMATICS LabSchool of Medical Science & Technology

IIT Kharagpur

Computational Pathology

Page 2: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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

Page 3: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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.

Page 4: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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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

Page 5: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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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

Cha

ract

eriz

atio

nW

ater

shed

Se

gmen

tatio

n

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

Page 6: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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Segmentation Accuracy by Pixel Classification method = 98%

Results

Normal

OSF without dysplasia

OSF with dysplasia

Page 7: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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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

Page 8: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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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

Page 9: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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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

Page 10: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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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

Page 11: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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

Page 12: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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

Page 13: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

GMM: EM estimation

E-Step :

M-Step:

Page 14: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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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

Page 15: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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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

Page 16: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

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

Page 17: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

Web-enabled Quantitative Microscopy for Blood Smear ScreeningTo develop CAD system for malaria, anemia using light

microscopic imagesFunded byDIT, GoI

Page 18: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

Web-enabled Malaria Parasite Screening

System

Page 19: Chandan Chakraborty, PhDcse.iitkgp.ac.in/conf/CBBH/lectures/Chandan_CompPathology.pdf · Chandan Chakraborty, PhD Associate Professor BIOSTATISTICS & MEDICAL INFORMATICS Lab School

IBM Faculty Award ‘12 SPRAD’10