Abstract
Proper Segmentation of cytological images is a prerequisite for appropriate localization of nucleus of cancer cells to classify the specimen as benign or malignant. Most of the works till date pursue segmentation for a specific or single type of cancer cells. The present work considers cytological images from various types of cancer cells. As nuclei from the different types of cells carry different attributes, segmentation becomes a challenging task. Here a superpixel based segmentation techniques with different morphological and clustering algorithms such as anisotropic diffusion, DBscan, Fuzzy C-means etc. have been proposed to distinguish nucleus from different kinds of cells. Finally the segmented nuclei are used to extract features for classification using five different classifiers separately and observed an improved recognition accuracy.
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Acknowledgement
Authors are thankful to the “Center for Microprocessor Application for Training Education and Research” of Computer Science & Engineering Department, Jadavpur University, for providing infrastructure facilities during progress of the work. Authors are also thankful to Mr. Sandipan Choudhuri of Arizona State University, USA for his valuable suggestion and Dr. Debasree Mondal of Theism Medical Diagnostics Centre, Kolkata, West Bengal, India for providing us diagnosed samples.
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Mitra, S., Dey, S., Das, N., Chakrabarty, S., Nasipuri, M., Naskar, M.K. (2018). Identification of Benign and Malignant Cells from Cytological Images Using Superpixel Based Segmentation Approach. In: Mandal, J., Sinha, D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_24
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