J Med Syst - Comparison of several texture features for tumor detection in CE images.

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Resumo

Capsule endoscopy (CE) has been widely used as a new technology to diagnose gastrointestinal tract diseases, especially for small intestine. However, the large number of images in each test is a great burden for physicians. As such, computer aided detection (CAD) scheme is needed to relieve the workload of clinicians. In this paper, automatic differentiation of tumor CE image and normal CE image is investigated through comparative textural feature analysis. Four different color textures are studied in this work, i.e., texture spectrum histogram, color wavelet covariance, rotation invariant uniform local binary pattern and curvelet based local binary pattern. With support vector machine being the classifier, the discrimination ability of these four different color textures for tumor detection in CE images is extensively compared in RGB, Lab and HSI color space through ten-fold cross-validation experiments on our CE image data. It is found that HSI color space is the most suitable color space for all these texture based CAD systems. Moreover, the best performance achieved is 83.50% in terms of average accuracy, which is obtained by the scheme based on rotation invariant uniform local binary pattern.

Resumo Limpo

capsul endoscopi ce wide use new technolog diagnos gastrointestin tract diseas especi small intestin howev larg number imag test great burden physician comput aid detect cad scheme need reliev workload clinician paper automat differenti tumor ce imag normal ce imag investig compar textur featur analysi four differ color textur studi work ie textur spectrum histogram color wavelet covari rotat invari uniform local binari pattern curvelet base local binari pattern support vector machin classifi discrimin abil four differ color textur tumor detect ce imag extens compar rgb lab hsi color space tenfold crossvalid experi ce imag data found hsi color space suitabl color space textur base cad system moreov best perform achiev term averag accuraci obtain scheme base rotat invari uniform local binari pattern

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