Local features in biomedical image clusters extracted with independent component analysis

Author

Bauer, Christoph ; Theis, Fabian J. ; Bäumler, Wolfgang ; Lang, Elmar W.

Author_Institution

Inst. of Biophys., Regensburg Univ., Germany

Volume

1

fYear

2003

fDate

20-24 July 2003

Firstpage

81

Abstract

A neural network model for the identification and classification of malign and benign skin lesions from ALA-induced fluorescence images is presented. A self-organizing feature map or generative topographic mapping is used to cluster images patches according to their inherent local features, which then can be extracted with ICA. These components are used to distinguish skin cancer from benign lesions achieving an average classification rate of 70% so far.

Keywords

biomedical imaging; cancer; image classification; independent component analysis; pattern clustering; self-organising feature maps; skin; ALA-induced fluorescence images; benign skin lesions; biomedical image clusters; generative topographic mapping; independent component analysis; malign skin lesions; neural network; self-organizing feature map; skin cancer; Biomedical imaging; Data mining; Fluorescence; Image analysis; Image reconstruction; Independent component analysis; Lesions; Principal component analysis; Skin; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223300

Filename

1223300