title: [CT image retrieval of the liver with intrahepatic lesions].
abstract: This paper presents a method for global feature extraction and the application of the boostmetric distance metric method for medical image retrieval. The global feature extraction method used the low frequency subband coefficient of the wavelet decomposition based on the non-tensor product coefficient for piecewise Gaussian fitting. The local features were extracted after semi-automatic segmentation of the lesion areas in the images in the database. The experimental verification of the method using 1688 CT images of the liver containing lesions of liver cancer, liver angioma, and liver cyst confirmed that this feature extraction method improved the detection rate of the lesions with good image retrieval performance.
authors.affiliation: Key Lab for Medical Image Processing, Southern Medical University, Guangzhou 510515, China. meimeiyu009@163.com
authors.forename: Mei
authors.initials: M
authors.lastname: Yu
authors.forename: Zhen-tai
authors.initials: ZT
authors.lastname: Lu
authors.forename: Wu-fan
authors.initials: WF
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D003628:Database Management Systems; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011873:Radiology Information Systems; D014057:Tomography, X-Ray Computed