Segmentation of clusters nuclei based on intensity and texture in phase contrast image using h-maxima transformation
A. Vinothini, B. Prasad · 2016
The nucleus segmentation is the most important and tedious process in medical image analysis. The proposed method has three stages: preprocessing, h-maxima transformation based 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, imaging artifacts in the input image. 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 use these 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.