Paper Info
Title
Classification Of Pneumoconiosis On Hrct Images For Computer-Aided Diagnosis
Abstract
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.
Year
DOI
Venue
2013
10.1587/transinf.E96.D.836
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
pneumoconiosis, computer-aided diagnosis, HRCT, Hessian matrix, bag-of-features
Computer vision,Pneumoconiosis,Pattern recognition,Computer science,Computer-aided diagnosis,Bag of features,Hessian matrix,Artificial intelligence
Journal
Volume
Issue
ISSN
E96D
4
1745-1361
Citations 
PageRank 
References 
0
0.34
6
Authors
6
Name
Order
Citations
PageRank
Wei Zhao100.68
Rui Xu2234.30
Yasushi Hirano36314.24
Rie Tachibana4418.85
Shoji Kido55316.61
Narufumi Suganuma600.34