A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images

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.