| Resumo: | Traditional endoscopic methods do not reach the entire Gastrointestinal (GI) tract. Wireless Capsule Endoscopy (CE) is a diagnostic procedure that allows the visualization of the whole GI tract, acquiring video frames, at a rate of two frames per second, while travels through the GI tract, resulting in huge amounts of data per exam. These frames possess rich information about the condition of the stomach and intestine mucosa, encoded as color and texture patterns. It is known for a long time that human perception of texture is based in a multi-scale analysis of patterns, which can be modeled by multi-resolution approaches. Therefore, in the present paper it is proposed a frame classification scheme, based in different combinations of texture descriptors taken at different detail levels of the Discrete Wavelet Transform and Discrete Curvelet Transform domains, in order to compare the classification performance of these multi-resolution representations of the information within the CE frames. The classification step is performed by a multilayer perceptron neural network. The proposed method has been applied in real data taken from several capsule endoscopic exams and reaches 91.7% of sensitivity and 89.4% specificity for features extracted from the DWT domain and 94.1% of sensitivity and 92.4% specificity for features extracted from the DCT domain. These promising results support the feasibility of the proposed method.
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