Detection of mitotic cells using completed local binary pattern in histopathological images

In this study, detection of mitotic cells and the discrimination of mitotic cells from normal cells in high-resolution histopathological images are investigated. An automated model-based application tried to be developed for the detection of mitosis which is normally difficult to determine even by experts. The main purpose of this study is to assist pathologist in finding mitotic cells as second reader computer aided diagnosis system. On this purpose, firstly, k-means algorithm has been applied to distinguish the cellular structures from noncellular structures. Then, the features of this clustered cellular structures are extracted by using completed local binary pattern (CLBP). Hence, it is aimed to be sure whether the mitotic cells are able to distinguished from nonmitotic cells or not. Finally, an ensemble random Forest (RF) algorithm is used to classify the extracted features by CLBP. According to the result obtained from the study, while number of mitotic and nonmitotic cells are equal, the accuracy is significant. With increasing number of nonmitotic cells periodically cause to decrease of precision and F-measure values due to the unbalanced data distribution.