Study on the classification of capsule endoscopy images

Wireless capsule endoscope allows painless endoscopic imaging of the gastrointestinal track of humans. However, the whole procedure will generate a large number of capsule endoscopy images (CEIs) for reading and recognizing. In order to save the time and energy of physicians, computer-aided analysis methods are imperatively needed. Due to the influence of air bubble, illumination, and shooting angle, however, it is difficult to classify CEIs into healthy and diseased categories correctly for a conventional classification method. To this end, in the paper, a new feature extraction method is proposed based on color histogram, wavelet transform, and co-occurrence matrix. First, an improved color histogram is calculated in the HSV (hue, saturation, value) space. Meanwhile, by using the wavelet transform, the low-frequency parts of the CEIs are filtered out, and then, the characteristic values of the reconstructed CEIs’ co-occurrence matrix are calculated. Next, by employing the proposed feature extraction method and the BPNN (back propagation neural network), a novel computer-aided classification algorithm is developed, where the feature values of color histogram and co-occurrence matrix are normalized as the inputs of the BPNN for training and classification. Experimental results show that the accuracy of the proposed algorithm is up to 99.12% which is much better than the compared conventional methods.