Feature Extraction and Classification for Human Brain CT Images

Author

Zhang, Wei-Li ; Wang, Xi-Zhao

Author_Institution

Hebei Univ., Baoding

Volume

2

fYear

2007

fDate

19-22 Aug. 2007

Firstpage

1155

Lastpage

1159

Abstract

In this paper, computer aided diagnosis (CAD) is applied to the brain CT image processing. In addition to the 3 classical types of features, i.e. gray scale, shape and texture, the symmetric feature based on the characteristics of human-brain CT image is extracted. Inductive learning techniques, See5 and RBFNN (radial basis function of nerve network) are used to build classifiers for normal and abnormal brain CT images. Experimental results show that CAD system of human-brain CT image processing can assist doctors to correctly classify the CT images.

Keywords

brain; computerised tomography; feature extraction; image classification; learning by example; medical image processing; radial basis function networks; CAD system; brain CT image processing; computer aided diagnosis; feature classification; feature extraction; human brain CT images; inductive learning techniques; radial basis function of nerve network; Biomedical imaging; Computed tomography; Cybernetics; Diseases; Feature extraction; Humans; Image processing; Machine learning; Medical diagnostic imaging; Shape; CAD; Classification; Feature extraction; Medical image;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2007 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-0973-0

Electronic_ISBN

978-1-4244-0973-0

Type

conf

DOI

10.1109/ICMLC.2007.4370318

Filename

4370318