title: [Method of automatic detection of brain lesion based on wavelet feature vector].
abstract: A new method of automatic detection of brain lesion based on wavelet feature vector of CT images has been proposed in the present paper. Firstly, we created training samples by manually segmenting normal CT images into gray matter, white matter and cerebrospinal fluid sub images. Then, we obtained the cluster centers using FCM clustering algorithm. When detecting lesions, the CT images to be detected was automatically segmented into sub images, with a certain degree of over-segmenting allowed under the premise of ensuring accuracy as much as possible. Then we extended these sub images and extracted the features to compute the distances with the cluster centers and to determine whether they belonged to the three kinds of normal samples, or, otherwise, belonged to lesions. The proposed method was verified by experiments.
authors.affiliation: Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China.
authors.forename: Ya
authors.initials: Y
authors.lastname: Fan
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.forename: Huanqing
authors.initials: H
authors.lastname: Feng
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D001330:Electronic Data Processing / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D020300:Intracranial Hemorrhages / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*; D058067:Wavelet Analysis