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Recent trends with histopathological imagery led to rapid progress towards quantifying the perceptive issues, while prognostic, due to subjective variability among readers. This variability leads to distinguished prognosis reports and generates variability in treatment as well. Latest advancements in image analysis tools have allowed the powerful computer-assisted diagnostic system to assist oncologist in their diagnosis process on radiological data. The main goal of this study is to understand and address the challenges associated with the development of image analysis techniques for computer-aided interpretation of histopathology imagery. We are analyzing indicative characteristics like texture heterogeneity and morphological characteristic on a various scale of lymphomas like Follicular, Neuroblastoma, Breast, and Prostate tissue images for classifying them into respective grades. The study shows a systematic survey of the computational steps, which includes a recent scenario of diagnosis process to classify these lymphomas into respective grades along with its limitations, followed by it shows the pre-requisite of the computer-assisted diagnosis system and finally explains various segmentation techniques based on image descriptor and subsequent classification of biopsy into respective grades. This paper reviews recent state of the art technology for histopathology and briefly describes the recent development in histology and its application towards quantifying the perceptive issue in the domain of histopathology being pursued in the United State and India.Povzetek: Študij tehnik računalniške segmentacije in klasifikacije: uporaba na histopatoloških posnetkih.

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