Superpixel approach in high resolution histopathological image segmentation

Abstract

Segmentation of cellular structures with high accuracy has a crucial importance for the detection of cancerous regions in histopathologic images. The proper segmentation of cellular structures is one of the most important issues to be considered when making a diagnosis by pathologists. In this study, the contribution of the superpixel method to the segmentation of high-resolution histopathologic images of renal cell carcinoma from the TCGA (The Cancer Genome Atlas) data set was investigated. The superpixel method performs clustering based on color similarities and spatial proximity of the pixels in histopathologic images. When the results are evaluated, it has been observed that the superpixel method has a positive contribution to both the segmentation success and the running time.