Abstract
Computer-Aided Analysis of Blood Smear Images helps to identify several cell features which cannot be analyzed with the existing manual techniques. For this purpose, segmentation of required cell component is very important. The motive of this work is to segment the Cell Periphery, which holds the cytoplasm, from the Blood Smear Images. Primarily, these images are enhanced to increase the observability of various cell components. The enhancement is done using Dark Contrast Algorithm (DCA). This enhanced image is further segmented using K-Medoid Clustering, a technique based on spatial clustering. The number of clusters obtained as output are predefined. This technique clusters the data with the help of Similarity Index, to distribute it according to their similarities or dissimilarities by updating the medoids. The image hence obtained gives us the segmented Cell Periphery. The assessment of this proposed approach is done using parameters like Second Derivative like Measure of Enhancement (SDME) and Measure of Enhancement (EME) for enhancement and Dice Coefficient (DC) for segmentation.
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Verma, S., Bhateja, V., Singh, S., Gupta, S., Dogra, A., Nhu, N.G. (2023). Segmentation of Cell Periphery from Blood Smear Images Using Dark Contrast Algorithm and K-Medoid Clustering. In: Chakravarthy, V., Bhateja, V., Flores Fuentes, W., Anguera, J., Vasavi, K.P. (eds) Advances in Signal Processing, Embedded Systems and IoT . Lecture Notes in Electrical Engineering, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-19-8865-3_24
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DOI: https://doi.org/10.1007/978-981-19-8865-3_24
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