Abstract
Cancer is named based on the organ or cell type where it originates, such as Colon Melanoma or skin melanoma. Lymphoma, a prevalent cancer in India, is characterized by the swelling of lymph nodes, which are crucial for infection defense. Accurate labeling and understanding of various cell components in microscopic images are essential for the development of automated cancer diagnosis systems. This research addresses the problem of segmenting microscopic images to identify homogeneous cell regions, which is vital for precise cancer detection. We deployed a Statistical Region Merging (SRM) technique to segment slide views into regions with consistent properties. The method was tested on a set of lymphoma and histopathological images to evaluate its generality. Experimental results demonstrate segmentation outcomes at various detail levels, highlighting the effectiveness of the technique. The segmentation coarseness was adjusted using the parameter$Q$, allowing for detailed and accurate identification of cell components.