Computer vision systems for automated breast cancer diagnosis using Fine Needle Aspiration Cytology (FNAC) images are under development for a while now. Accurate segmentation of the nuclei in microscopic images is crucial for functioning of these systems, as most quantify and analyze nuclear features for diagnosis. This paper presents a nucleus segmentation system (NSS) involving pre-processing, pre-segmentation and refined segmentation stages. The NSS includes a novel pixel transformation step to create a high contrast grayscale representation of the input color image. The grayscale image gives NSS the capability-to disregard elements that mimic nuclear morphological and luminescence characteristics, and to minimize effects of non-specific staining of cytoplasm by Hematoxylin. Experimental results illustrate generalizability of the NSS to use multiple refined segmentation techniques and particularly achieve accurate nucleus segmentation using active contours without edges(F-score > 0.92). The paper also presents the results of experiments conducted to study the impact of image pre-processing steps on the NSS performance. The pre-processing steps are observed to improve accuracy and consistency across tested refined segmentation techniques.
Garud, H., Karri, S. P. K., Sheet, D., Maity, A. K., Chatterjee, J., Mahadevappa, M., & Ray, A. K. (2016). Methods and system for segmentation of isolated nuclei in microscopic breast fine needle aspiration cytology images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10481 LNCS, pp. 380–392). Springer Verlag. https://doi.org/10.1007/978-3-319-68124-5_33