H-maxima transformation based image segmentation of clump nuclei in phase contrast image

The nucleus segmentation is the most important and tedious process in medical image analysis. The proposed method has three stages: K-means clustering, marker controlled watershed segmentation and texture analysis. First, the preprocessing stage uses top-hat filter to increase the contrast of nuclei and reduce the non-uniform illumination, and K-means clustering is used for rough segmentation of the cell image. By using K-means algorihm, image pixel is dividing into three parts: nuclei, cytoplasm and background. In second stage, the segmentation of nuclei consists of a distance transformation, h-maxima transformation and watershed segmentation. The markers are used to obtain segments of the nuclei in the h-TMC watershed segmentation. To detect the single marker in nucleus, we usethese transformations. Due to imaging artifacts, prolonged cell cytoplasm in the contrast image, nuclei may falsely be segmented and it leads to an inaccurate analysis of the cell image. To identify and remove the non-nuclei segments. The third stage of texture analysis is followed. The texture with adaboost algorithm is used for non-nucleus identification.