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.
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