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27 February 2009 Classification of patterns for diffuse lung diseases in thoracic CT images by AdaBoost algorithm
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 726037 (2009) https://doi.org/10.1117/12.811497
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
CT images are considered as effective for differential diagnosis of diffuse lung diseases. However, the diagnosis of diffuse lung diseases is a difficult problem for the radiologists, because they show a variety of patterns on CT images. So, our purpose is to construct a computer-aided diagnosis (CAD) system for classification of patterns for diffuse lung diseases in thoracic CT images, which gives both quantitative and objective information as a second opinion, to decrease the burdens of radiologists. In this article, we propose a CAD system based on the conventional pattern recognition framework, which consists of two sub-systems; one is feature extraction part and the other is classification part. In the feature extraction part, we adopted a Gabor filter, which can extract patterns such like local edges and segments from input textures, as a feature extraction of CT images. In the recognition part, we used a boosting method. Boosting is a kind of voting method by several classifiers to improve decision precision. We applied AdaBoost algorithm for boosting method. At first, we evaluated each boosting component classifier, and we confirmed they had not enough performances in classification of patterns for diffuse lung diseases. Next, we evaluated the performance of boosting method. As a result, by use of our system, we could improve the classification rate of patterns for diffuse lung diseases.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Masayuki Kuwahara, Shoji Kido M.D., and Hayaru Shouno "Classification of patterns for diffuse lung diseases in thoracic CT images by AdaBoost algorithm", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726037 (27 February 2009); https://doi.org/10.1117/12.811497
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