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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. Introduction Wireless capsule gastroscopy system allows painless screening for gastric diseases. During the screening process, however, a large number of wireless capsule gastroscopy images (WCGIs) will be generated, and incur much time and energy costs if these WCGIs are read and recognized by physicians. Thus, it is imperative to develop an efficient computer aided analysis algorithm that can help physicians recognize WCGIs with high efficiency and accuracy. Generally, features of a WCGI consists of shape, color, texture, etc. It is noted in [1] that the features employed for recognition will directly affect the ultimate recognition performance. In [2], the ULBP (Uniform Local Binary Pattern) features are extracted from the discrete wavelet transform sub-band in the RGB (Red, Green, Blue) and HSI (Hue, Saturation, Intensity) spaces, and then the support vector machine is used as the recognizer. The authors of [3] developed an image feature extraction algorithm and an image feature matching technique that are able to discover feature places between two continuous WCGIs. In [4], the Gabor filter based gray-level co-occurrence matrix and the wavelet are adopted to extract texture information of WCGIs. Of note is that most of the existing feature extraction methods are either based on the full image or its low-frequency part, and often ignore the middle and high frequency parts that actually have rich texture information. It is known that WCGIs have rich color and texture information. Thus, for a WCGI, the lesion and non-lesion regions possess major diversity in color and texture. In the paper, a novel feature extraction method based on color histogram, wavelet transform and co-occurrence matrix is studied. Then, based on the framework of the novel feature extraction method, a recognition algorithm for WCGIs is proposed so as to enhance the recognition accuracy. Method Here, the detailed methods employed in the paper are presented. For the feature extraction method, color and texture features are, respectively, extracted by an improved color histogram and the corresponding co-occurrence matrix. The WCGIs used in the paper are grouped into the training and examining sets. WCGIs in the training set are used to train the back propagation neural network

A Recognition Algorithm for Wireless Capsule Gastroscopy Images