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

Abstrak

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.