Paper Info
Title
Detection of mitotic cells using completed local binary pattern in histopathological images
Abstract
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.
Year
DOI
Venue
2015
10.1109/SIU.2015.7130020
Signal Processing and Communications Applications Conference
Keywords
Field
DocType
Histopathological images,classification,completed local binary pattern,mitosis detection,segmentation
Computer vision,Mitosis,Pattern recognition,Computer science,Local binary patterns,Computer-aided diagnosis,Artificial intelligence,Random forest
Conference
ISSN
Citations 
PageRank 
2165-0608
1
0.38
References 
Authors
0
3
Name
Order
Citations
PageRank
Ibrahim Onur Sigirci111.39
Abdulkadir Albayrak262.86
Gökhan Bilgin3145.14