Abstract
Breast histopathological image analysis helps in understanding the structure and distribution of the nucleus, thereby assisting in the detection of breast cancer. But analysis of histopathological image is challenging due to various reasons such as heterogeneity of nucleus structure, overlapping nuclei, clustered nuclei, variations in illumination, presence of noise etc. Limited availability of breast histopathological image dataset with fine annotations for detection of nucleus has restricted the analysis of histopathological images at the pixel-level. This paper presents fine annotations for nucleus segmentation of breast histopathological image datasets. Various textures such as Filter Banks, Gray Level Co-occurrence matrix and Local Binary Patterns are studied along with colour features for semantic segmentation of nuclei from histopathological images. Support Vector Machine and Multi Layer Perceptron algorithms are trained to perform pixelwise classification. The performance of the three texture features are evaluated on the two datasets and the results are presented in this paper.
Highlights
Breast cancer is the most common malignancy found in women [1]
Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) classifiers are trained on 70% of the data, 10% of the data is used for validation and 20% of the data is used for testing
Developing a semantic segmentation algorithm for nucleus segmentation in breast histopathological images is challenging due to lack of finely-annotated datasets
Summary
Breast cancer is the most common malignancy found in women [1]. Microscopic analysis of tumour helps in the detection of malignancy. Histopathological imaging has been considered as a ‘gold standard’ in recognizing almost all sorts of cancers since it captures microscopic structures of the cells and tissues. For accurate identification of breast cancer, a biopsy accompanied by microscopic examination is an essential step. A small section of tissue from the suspicious region of the body is removed, processed and stained for further evaluation. The nuclei of the tissue are expressed in dark purple and other structure in pink colour when stained with Hematoxylin and Eosin (H&E). Pathologists perform microscopic examination of stained tissues. The manual evaluation of histopathological images is a tedious and highly time-consuming task. Computer-Aided Diagnostic (CAD) system plays an important role in evaluation of
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