Initial Binarization of Cell Images Using Bimodal Method

In this process, we employ a detailed methodology for processing cell images. Initially, we apply the bimodal thresholding technique to convert cell images into binary format—transforming them into images containing only black and white pixels. This algorithm automatically determines an optimal threshold by analyzing the histogram distribution of pixel intensities, typically utilizing Otsu's method which maximizes inter-class variance. Following binarization, we proceed with image segmentation, a critical step that partitions the image into distinct regions or individual cellular objects using techniques like watershed transformation or region-growing algorithms. Finally, we implement cancer cell identification through computer vision algorithms that automatically detect and annotate cancerous cells within the images, potentially employing convolutional neural networks (CNNs) or feature-based classification methods that analyze morphological characteristics such as nucleus size, shape irregularity, and cytoplasmic ratio.