Image Processing for Automatic Cell Nucleus Segmentation Using Super pixel and Clustering Methods on Histopathological Images

Abstract

On a daily basis, it appears that the number of cancer cases and cancer-related deaths are increasing. Early detection and treatment of the malignant region are critical for successful treatment. Early detection of sick cells is made possible with the use of computer-assisted programmes, which are then diagnosed by experienced pathologists due to their efforts. Using computer-aided programmes, this research found that cell nuclei could be automatically detected in high-resolution histopathological images using global segmentation methods such as k-Means and Fuzzy C Means and algorithms from superpixel segmentation methods such as SLIC, Quick-shift, Felzenszwalb, Watershed, and ERS. Using high-quality histopathology pictures, the researchers discovered that the k-means and FCM algorithms performed significantly better than the baseline techniques in the study. In terms of precision, the Quickshift and SLIC approaches produced superior outcomes. The K-means and FCM algorithms perform best in the F-M test, and the true negative ratio is more successful in the Quickshift and SLIC methods than in the F-M test.