title: [Computerized classification of pneumoconiosis radiographs based on grey level co-occurrence matrices].
abstract: Pneumoconiosis is diagnosed as categories 0-4 according to the Pneumoconiosis Law. Physicians have difficulty precisely categorizing many chest images. Therefore, we have developed a computerized method for automatically categorizing pneumoconiosis from chest radiographs. First, we extracted the rib edge regions from lung ROIs. Second, texture features were extracted using a dot enhancement filter, line enhancement filter, and grey level co-occurrence matrix. Third, the rib edge regions were removed from these processed images. Finally, we used a support vector machine for feature analysis. In a consistency test, 56 cases (69.7%) were classified correctly, and 45 cases (61.8%) were classified correctly in a validation test. These results show that the proposed features and removal of the rib edge are effective in classifying the profusion of opacities that indicate pneumoconiosis.
authors.affiliation: Major in Medical Engineering and Technology, Graduate School of Medical Technology & Health Welfare Sciences, Hiroshima International University.
authors.forename: Yoshifumi
authors.initials: Y
authors.lastname: Masumoto
authors.forename: Ikuo
authors.initials: I
authors.lastname: Kawashita
authors.forename: Yasuhiko
authors.initials: Y
authors.lastname: Okura
authors.forename: Masamitu
authors.initials: M
authors.lastname: Nakajima
authors.forename: Eiichiro
authors.initials: E
authors.lastname: Okumura
authors.forename: Takayuki
authors.initials: T
authors.lastname: Ishida
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D011009:Pneumoconiosis / Q000145:classification / Q000000981:diagnostic imaging*; D013902:Radiography, Thoracic