Co-Occurrence and Morphological Analysis for Colon Tissue Biopsy

ClassificationKhalid Masood, Nasir Rajpoot, Kashif Rajpoot*, Hammad Qureshi

Signal and Image Processing Group, University of Warwick (UK) * Wolfson Medical Vision Lab, Oxford University (UK)

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Problem Definition• Given hyperspectral image cube of a patient’s colon tissue

biopsy sample, automatically label the sample as Benign or Malignant

• Our approach is based on the idea that malignancy of a tumor alters the macro-architecture of the tissue glands:– Nice tubular structure of the glands for benign tumors– No such structure for malignant tumors

Benign Malignant

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Motivation for Colon Biopsy Classification

• Useful for screening of the colon cancer

• Visual assessment by pathologists is very subjective

• Significant intra- and inter-observational variation between pathologists

• Quantitative histopathological analysis techniques offer objective, reliable, accurate, and reproducible assessment

Source: NIH

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Hyperspectral Imaging

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are needed to see this picture.

Ordinary cameras only capturereflections from RGB colorsHyperspectral cameras capture reflections from a range of visible wavelengths

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Hyperspectral Imaging (HSI)

• HSI is a fast and reliable means of characterizing the histochemistry of tissues– HSI is also used extensively in remote sensing, satellite

imaging, and defence (target detection etc.) applications

• The Nuance multispectral imaging system can acquire 20 subbands in visible wavelength range of 420-720nm

• Each hyperspectral image is a 3D data cube with a spectral coordinate in the z direction representing 20 subbands

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Our Classification Algorithm• Based on a spectral-spatial analysis of the input

data cube, our algorithm consists of three stages:

– Stage I: Dimensionality reduction, followed by segmentation

– Stage II: Morphological/textural analysis of the segmented results

– Stage III: Classification using Subspace Projection methods and Support Vector Machines (SVM)

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Stage I: Segmentation• Dimensionality reduction (from 20 to 4 bands of the multispectral

image cube) is achieved using independent component analysis (ICA) and k-means clustering

Nuclei, cytoplasm; gland secretions; stroma of the lamina propria

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Stage II: Feature Extraction• Four binary images are extracted

from each segmented image

• Two sets of features are calculated: morphological and co-occurrence matrix features:

• Morphological Features:– These describe the shape, size,

orientation and other attributes of the cellular components

– These features are calculated on patches (blocks) of the segmented image

• Co-Occurrence Features:– These describe the textural

properties of a given neighborhood

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Morphological Features• Morphological features describe the shape and texture of the image

• Feature vector consists of five to ten morphological features

• Discriminant morphological features are:– Euler Number : number of contiguous parts– Convex Area : number of pixels in convex image– Extent : the proportion of pixels in the bounding box– Solidity : the proportion of pixels in the convex hull – Area : the actual number of pixels in the patch– EquivDiameter : the diameter of a circle with the same area as of the

patch

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Co-occurrence Features• The co-occurrence matrix is constructed by

analysing the gray levels of neighboring pixels• The (i,j)th element of a co-occurrence matrix for a

particular angle and distance is given by the joint conditional pdf:

• Three attributes used are:

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Stage III : Classification• Subspace Projection methods

– Principle Component Analysis (PCA)– Linear Discriminant Analysis (LDA)– Kernel PCA– Kernel LDA

• Support Vector Machines (SVM)– Polynomial kernel– Gaussian kernel

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Principal Component Analysis (PCA)

• Eigenvectors of the data in the embedding space can be used to detect directions of maximum variance.

• The principal components can be computed

by solving the eigenvalue problem:

• The coefficients of projection along a few top

principal directions can be used as features

• Nearest-neighbor classifier is used for

assigning label to a biopsy sample

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Linear Discriminant Analysis (LDA)• Limitations of the PCA

• As opposed to PCA, which maximises the overall scatter, LDA maximises the ratio of between-class scatter Sb to within-class scatter Sw

• The wi can be computed by solving the generalised eigenvalue problem:

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Linear Boundary Assumption• In most real-world problems, separating

boundaries are not necessarily linear! Consider, for instance, the following example:

Will a linear classifier work?

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The Kernel Trick

• Kernel machines (eg, SVMs) transform non-linear decision boundaries to linear ones in higher dimensional feature space

• Two dimensional features (let us say) are mapped to a three dimensional feature space through a non-linear transform

• Non-linear ellipsoidal decision boundary is replaced to linear boundary in higher dimensional space

• The trick is to replace dot products in F with a kernel function in the input space R so that the non-linear mapping is performed implicitly in R

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SVM Kernel Functions• A few commonly used kernel functions are:

• Classifier’s performance highly sensitive to parameter values

• Best kernel parameter values are searched

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Experimentation• Two sets of Experiments: Mixed testing and Leave one out (LOO)

• For mixed testing:– 4096 patches (blocks) per image of 16x16 dimensions per patch– Morphological features– Training set contains one quarter of the patches while remaining three

quarters make the test set – PCA and modular LDA are used in mixed testing

• Leave one out testing is done on gray level co-occurrence features– 16x16 patches– Support Vector Machines (polynomial kernel and gaussian kernel are

used)– Kernel parameters are optimized

• Classification label is assigned to a slide according to the class of the majority of the patches

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Experimental Results

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Experimental Results

AUCH of the ROC curve for LDA goes up to 0.92 for 5 features.

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Conclusions and Future Work

• Conclusions– Tissue segmentation affects the performance– Implementation of LDA saves the computational cost

and the performance achieved by it is encouraging– Gaussian kernel SVM gives no false alarm

• Future Work– More effective segmentation– Combination of classifiers

– Shape modelling of nuclei glands

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Acknowledgements

• Prof David Rimm, Department of Pathology, Yale University School of Medicine (USA)

• Prof Gustave Davis, Department of Pathology, Yale University School of Medicine (USA)

• Prof Ronald Coifman, Department of Applied Mathematics, Yale University (USA)

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Thanks for your attention

Any Questions?