Classification of Pneumoconiosis on HRCT Images for Computer-Aided Diagnosis

Wei ZHAO  Rui XU  Yasushi HIRANO  Rie TACHIBANA  Shoji KIDO  Narufumi SUGANUMA  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.4   pp.836-844
Publication Date: 2013/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.836
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Medical Imaging)
Category: Computer-Aided Diagnosis
pneumoconiosis,  computer-aided diagnosis,  HRCT,  Hessian matrix,  bag-of-features,  

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This paper describes a computer-aided diagnosis (CAD) method to classify pneumoconiosis on HRCT images. In Japan, the pneumoconiosis is divided into 4 types according to the density of nodules: Type 1 (no nodules), Type 2 (few small nodules), Type 3-a (numerous small nodules) and Type 3-b (numerous small nodules and presence of large nodules). Because most pneumoconiotic nodules are small-sized and irregular-shape, only few nodules can be detected by conventional nodule extraction methods, which would affect the classification of pneumoconiosis. To improve the performance of nodule extraction, we proposed a filter based on analysis the eigenvalues of Hessian matrix. The classification of pneumoconiosis is performed in the following steps: Firstly the large-sized nodules were extracted and cases of type 3-b were recognized. Secondly, for the rest cases, the small nodules were detected and false positives were eliminated. Thirdly we adopted a bag-of-features-based method to generate input vectors for a support vector machine (SVM) classifier. Finally cases of type 1,2 and 3-a were classified. The proposed method was evaluated on 175 HRCT scans of 112 subjects. The average accuracy of classification is 90.6%. Experimental result shows that our method would be helpful to classify pneumoconiosis on HRCT.