Computational cancer detection of pathological images based on an optimization method for color-index local auto-correlation feature extraction

Author

Jia Qu ; Nosato, Hirokazu ; Sakanashi, Hidenori ; Takahashi, Eiichi ; Terai, Kensuke ; Hiruta, Nobuyuki

Author_Institution

Dept. of Intell. Interaction Technol., Univ. of Tsukuba, Tsukuba, Japan

fYear

2014

fDate

April 29 2014-May 2 2014

Firstpage

822

Lastpage

825

Abstract

Aiming to lessen the burdens of the pathologist with efficient diagnosis assistance, this paper proposes a cancer detection method for pathological images utilizing color features based on color-index local auto-correlations (CILAC), applied to color-indexed images to utilize co-occurrence information about indexed pixels. Moreover, a method for the automatic optimization of feature extraction is also proposed. Based on a database including both benign and cancerous pathological images, experimental results show enhanced performance compared to prior research, which demonstrate the effectiveness of the proposed cancer detection method.

Keywords

biomedical optical imaging; cancer; feature extraction; medical image processing; optimisation; CILAC; cancerous pathological images; color-index local autocorrelation feature extraction; color-indexed images; computational cancer detection; efficient diagnosis assistance; optimization method; Cancer; Cancer detection; Feature extraction; Image color analysis; Indexes; Pathology; Shape; CILAC; cancer detection; feature extraction; optimization; pathological images;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on

Conference_Location

Beijing

Type

conf

DOI

10.1109/ISBI.2014.6867997

Filename

6867997