Ozturk, SabanAkdemir, Bayram2020-03-262020-03-2620182277-34952319-5592https://dx.doi.org/10.29042/2018-3321-3325https://hdl.handle.net/20.500.12395/36427Cell segmentation and counting has a very important role in diagnosing diseases and in the treatment process. But the complexity of the histopathological images and the differences in cell groups make this process very difficult, even for an expert. In order to facilitate this process, analysis of histopathological images is performed by using computer vision methods. This paper presents the use of different feature extraction methods for cell detection in histopathological images and the comparison of the results of these algorithms. For this reason, HOG, MSER, SIFT, FAST, LBP and CANNY feature extraction algorithms are used. The aim of the study is to determine cells using different feature extraction methods and to determine which of these feature extraction algorithms will be more successful. Firstly, image pre-processing has been applied to clear the noises in the histopathological images. Then, feature extraction algorithms are applied to image, respectively. Finally, the successes of different feature extraction algorithms have been compared.en10.29042/2018-3321-3325info:eu-repo/semantics/openAccessHOGMSERSIFTFASTLBPcannyhistopathological imagecell countingArticle8333213325#YOKWOS:000433225200002N/A