Title: Enhancement and segmentation of histopathological images of cancer using dynamic stochastic resonance
Authors: Anuranjeeta; Shiru Sharma; Neeraj Sharma; Munendra Singh; K.K. Shukla
Addresses: School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi 221005, India ' School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi 221005, India ' School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi 221005, India ' Department of Mechatronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India ' Department of Computer Science and Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi 221005, India
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 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.
Keywords: dynamic stochastic resonance; DSR; image enhancement; tissue; segmentation; histopathological image.
International Journal of Medical Engineering and Informatics, 2020 Vol.12 No.2, pp.180 - 193
Received: 26 Oct 2017
Accepted: 28 Aug 2018
Published online: 27 Apr 2020 *