Anuranjeeta, . and sharma, shiru and Sharma, Neeraj and Singh, Munendra and Shukla, K K (2020) Enhancement and segmentation of histopathological images of cancer using dynamic stochastic resonance. International Journal of of Medical Engineering and Informatics, 10. ISSN 1755-0653

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Abstract

Pathologists face difficulty in cell image detection as uneven dye causes the low contrast and inhomogeneity. The proposed discrete cosine transform (DCT)-based dynamic stochastic resonance (DSR) technique enhances the histopathological images of cancer. Further, the DSR-based Otsu’s thresholding processed image helps in the better segmentation of histopathological images of four types of cancer cells, i.e., breast, cervix, ovarian and prostate cancer. The comparison of segmentation results were performed on the University of California, Santabarbara (UCSB) available breast cancer datasets for analysis. The algorithm has been applied to total 22 breast cancer images including benign and malignant and compared with region of interest (ROI) segmented ground truth images to validate the performance of proposed DSR-based Otsu’s thresholding. DSR-based Otsu’s segmentation obtained better results with 0.776 average correlation, 0.979 average normalised probabilistic rand (NPR) index, 0.011 average global consistency

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