Study on the classification of capsule endoscopy images

Ji et al. EURASIP Journal on Image and Video Processing https://doi.org/10.1186/s13640-019-0461-4 (2019) 2019:55 EURASIP Journal on Image and Video Processing RESEARCH Open Access Study on the classification of capsule endoscopy images Xiaodong Ji1,2*, Tingting Xu1, Wenhua Li1,3 and Liyuan Liang1 Abstract 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. Keywords: Capsule endoscopy, Classification, Wavelet transform, Color histogram, Co-occurrence matrix 1 Introduction In 2001, the world’s first wireless capsule endoscopy system was approved by the US Food and Drug Administration for use in clinical practice [1], which allows painless endoscopic imaging of the gastrointestinal track of humans. During the inspection process, however, a large number of capsule endoscopy images (CEIs) will be produced. The CEIs used in the paper are provided by the Hangzhou Hitron Technologies Co., Ltd., whose independently developed HT-type wireless capsule endoscope system presents a shooting frequency of 2 frames per second. Therefore, 57,600 CEIs will be generated after 8 h of work. It will cost much time and energy if these CEIs are read and recognized by physicians. Thus, it is imperative to develop an efficient computer-aided analysis method being able to automatically classify CEIs with high correctness. * Correspondence: 1 School of Electronics and Information, Nantong University, No.9, Seyuan Road, Chongchuan District, Nantong 226019, Jiangsu Province, China 2 Nantong Research Institute for Advanced Communication Technologies, No.9, Seyuan Road, Chongchuan District, Nantong 226019, Jiangsu Province, China Full list of author information is available at the end of the article Generally, the features of a CEI include shape, color, and texture. It is noted in [2] that the features employed for classification will directly affect the final discrimination performance. In [2], the author chooses the color moment and gray-level co-occurrence matrix as image features. In [3], the word-based color histogram features are extracted from YCbCr color space, and then, the support vector machine is used as the classifier. In [4], the texture features based on gray-level co-occurrence matrix are employed from the discrete wavelet transform sub-bands in the HSV spaces. In [5], the CEIs are color-rotated so as to boost the chromatic attributes of ulcer areas and the ULBP features of the CEIs are extracted from the RGB space. The authors of [6] propose a method for distinguishing diseased CEIs from healthy CEIs based on contourlet transform and local binary pattern (LBP). The authors of [7] extract five color features in the HSV color space to differentiate between healthy and non-healthy images. In [8], an automatic detection method is proposed based on color statistical features extracted from histogram probability. It is worth mentioning that these feature extraction methods are © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Ji et al. EURASIP Journal on Image and Video Processing (2019) 2019:55 Page 2 of 7 either based on the full image or its low-frequency part, and no consideration is taken into the middle and high-frequency parts of the images that actually contain abundant texture information. Very recently, the authors of [9] investigated the wireless capsule endoscopy video and proposed a detection method based on higher and lower order statistical features. The rest of the paper is organized as follows: Section 2 describes the classification algorithm proposed in the paper. Section 3 details the feature extraction method used for extracting color and texture features, respectively. In Section 4, the construction of the BPNN is explained. Section 5 reports experimental results. Finally, the concluding remarks are presented in Section 6. 2 Methods It is well known that CEIs contain rich color and texture information. The lesion and non-lesion areas have significant color and texture differences. To this end, in the paper, a novel feature extraction method based on the color histogram, wavelet transform, and co-occurrence matrix is developed with the aim of improving the classification accuracy. The color and texture features are, respectively, extracted by the improved color histogram and the co-occurrence matrix based on wavelet transform. The CEIs used in the paper are divided into the training and testing sets. CEIs in the training set are used to train the BPNN (back propagation neural network), and those in the testing set are used for classification. The extracted feature values of the CEIs in the training set are normalized as the inputs of the BPNN and the classification results of the testing set are achieved by the trained BPNN. Simulation experiments show that the proposed algorithm can effectively divide the CEIs into two categories, i.e., healthy and diseased images, with high correctness. As shown in Fig. 1, the algorithm proposed in the paper includes three steps: (1) extracting the color features: (a) transforming the CEIs from the RGB to HSV spaces, (b) calculating the color histogram after quantization, and (c) selecting the appropriate bins and then constructing the color feature vector; (2) extracting the texture features: (a) select (...truncated)