Enhancement and segmentation of histopathological images of cancer using dynamic stochastic resonance

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 error (GCE), and 0.185 average variation of information (VI). These indices are higher than the other conventional segmentation methods and have the advantage to identify the target objects in low contrast images.