Abstract
Pathology images are color in nature due to the use of chemical staining in biopsy examination. Aware of the high color diagnosticity in pathology images, this work introduces a compact rotation-invariant texture descriptor, named quantized diagnostic counter-color pattern (QDCP), for digital pathology image understanding. On the basis of color similarity quantified by the inner product of unit-length color vectors, local counter-color textons are indexed first. Then the underlined distribution of QDCP indexes is estimated by an image-wise histogram. Since QDCP is computed based on color difference directly, it is robust to small color variation usually observed in pathology images. This study also discusses QDCP’s extraction, parameter settings, and feature fusion techniques in a generic pathology image analysis pipeline, and introduces two more descriptors QDCP-LBP and QDCP/LBP. Experimentation on public pathology image sets suggests that the introduced color texture descriptors, especially QDCP-LBP, outperform prior color texture features in terms of strong descriptive power, low computational complexity, and high adaptability to different image sets.
Highlights
Pathology is a medical sub-specialty that studies and practices the diagnosis of disease through examining biopsy samples or surgical specimens under microscopes by pathologists
The statistics of local color texton are summarized by an image-wise histogram HQDCP, which is used as a color texture feature in analysis
Because we observe similar trends in the results associated with fisher’s linear discrinminant (FLD) and K-nearest neighbors (KNN), their receiver operating characteristic (ROC) curves are omitted here
Summary
Pathology is a medical sub-specialty that studies and practices the diagnosis of disease through examining biopsy samples or surgical specimens under microscopes by pathologists. It serves as the golden truth of cancer diagnosis. To address subjectivity in pathology examination [1, 2], digital pathology exploits image analysis techniques and pattern recognition algorithms for histological information understanding in tissue images, and merges as a promising approach owing to its time-efficiency, consistency, and objectivity. A digital pathology diagnosis system is a pattern recognition system. Given a query pathology image, a machine understands it by comparing a set of quantitative features from the image against the stored feature sets in the database. Extraction of discriminative features from color pathology images is important
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