A Recognition Algorithm for Wireless Capsule Gastroscopy Images

Abstract

In order to enhance recognition accuracy, a novel recognition algorithm for wireless capsule gastroscopy images(WCGIs) was developed by using a new proposed feature extraction method and back propagation neural network(BPNN). First, an improved color histogram was computed in the Hue, Saturation, Value space. Meanwhile, by using the wavelet transform, the low frequency parts of WCGIs were filtered out and then the characteristic values of the reconstructed WCGIs’ co-occurrence matrix were computed. Next, the feature values of color moments and co-occurrence matrix were normalized as the inputs of the BPNN for training and recognition. Recognition experiments were conducted and the results demonstrated that the accuracy of the developed algorithm is up to 99.09% that is much better than the compared methods.