Automatic Bleeding Frame and Region Detection for GLCM Using Artificial Neural Network

 Wireless capsule endoscopy is a device that inspects the direct visualization of patient’s gastrointestinal tract without invasiveness. Analyzing the WCE video is a time- consuming task hence computer aided technique is used to reduce the burden of medical clinicians. This paper proposes a novel color feature extraction method to detect the bleeding frame. First, we perform word based histogram for rapid bleeding detection in WCE images. Classification of bleeding WCE frame is performed by applying for glcm using  Artificial Neural Network and K-nearest neighbour method. Second we propose a two-stage saliency map extraction method. In first stage saliency, we inspect the bleeding images under different color components to highlight the bleeding regions. From second stage saliency red color in the bleeding frame reveals that the region is affected. Then, by using algorithm we fuse the two-stage of saliency to detect the bleeding area. Experimental results show that the proposed method is very efficient in detecting the bleeding frames and the region

An efficient method to classify GI tract images from WCE using visual words

The digital images made with the Wireless Capsule Endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the CS-LBP (Center Symmetric Local Binary Pattern) and the ACC (Auto Color Correlogram) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve this issue, we suggested a Visual Bag of Features(VBOF) method by incorporating Scale Invariant Feature Transform (SIFT), Center-Symmetric Local Binary Pattern (CS-LBP) and Auto Color Correlogram (ACC). This combination of features is able to detect the interest point, texture and color information in an image. Features for each image are calculated to create a descriptor with a large dimension. The proposed feature descriptors are clustered by K- means referred to as visual words, and the Support Vector Machine (SVM) method is used to automatically classify multiple disease abnormalities from the GI tract. Finally, post-processing scheme is applied to deal with final classification results i.e. validated the performance of multi-abnormal disease frame detection