Color-Based Clustering Algorithms Segmentation of Malignant Leukemia Cells

Yousif Abdallah, Sami Elgak, Mohamed Alanzi, Norah Alshanhrai, Ghalia K. Alfuraih, Mohammed Rafiq, Nagi I. Ali

Abstract: Doctors separate items using microscopic photography in order to see details of leukemia cells that were not evident in the initial image. The process of modifying or transferring photographs is referred to as "image augmentation. " When some features of a photograph are improved, unintended consequences can emerge. We created a new nonlinear approach for enhancing the contrast of soft tissues in microscopic imaging images by combining clipping and nonlinear binning procedures in order to get the highest possible image quality following denoising and color-based clustering filtering. To get non-optional results, the Gaussian and Poisson distributions exaggerate noise variance in low-intensity regions (low photon counts), while underestimating it in high-intensity parts (high photon counts). In two independent studies, the MatLab application was utilized to collect and analyze the contrast enhancement results from ten images taken during two separate tests. To estimate the proper number of bins or, more accurately, grey levels, the entropy and average distance between a gray-level histogram and the contrast enhancement function's curve must be calculated. Histogram.

Keywords: Color-based, Clustering, Segmentation, Leukemia Cells

How to Cite?: Yousif Abdallah, Sami Elgak, Mohamed Alanzi, Norah Alshanhrai, Ghalia K. Alfuraih, Mohammed Rafiq, Nagi I. Ali, "Color-Based Clustering Algorithms Segmentation of Malignant Leukemia Cells", Volume 11 Issue 2, February 2022, International Journal of Science and Research (IJSR), Pages: 1220-1223, https://www.ijsr.net/getabstract.php?paperid=SR22226191205, DOI: https://dx.doi.org/10.21275/SR22226191205