A novel pre-processing approach based on colour space assessment for digestive neuroendocrine tumour grading in immunohistochemical tissue images.

Introduction: The complexity of histopathological images remains a challenging issue in cancer diagnosis. A pathologist analyses immunohistochemical images to detect a colour-based stain, which is brown for positive nuclei with different intensities and blue for negative nuclei. Several issues emerge during the eyeballing tissue slide analysis, such as colour variations caused by stain inhomogeneity, non-uniform illumination, irregular cell shapes, and overlapping cell nuclei.

Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images.

Background And Objective: This paper presents an improved scheme able to perform accurate segmentation and classification of cancer nuclei in immunohistochemical (IHC) breast tissue images in order to provide quantitative evaluation of estrogen or progesterone (ER/PR) receptor status that will assist pathologists in cancer diagnostic process.

Methods: The proposed segmentation method is based on adaptive local thresholding and an enhanced morphological procedure, which are applied to extract all stained nuclei regions and to split overlapping nuclei. In fact, a new segmentation approach is presented here for cell nuclei detection from the IHC image using a modified Laplacian filter and an improved watershed algorithm.