Automatic detection of colonoscopic anomalies using capsule endoscopy

Colon cancer and precancerous colon lesions are major health problems. The most frequent precancerous cancer lesions are polyps characterized by abnormal tissue growth in the human bowel. There are also anomalies such as inflammation and bleeding area. Colon capsule endoscopy (CCE) is a recent promising technology which enables to obtain videos of the inside of the intestine via an on board digital camera. The small capsule ingested by the patient is recuperated after it passes through the whole gastrointestinal track. Expert gastroenterologists analyze the video sequence visually in order to find out frames containing abnormalities related to cancer. This process is labor intensive and time consuming. In this paper we propose an algorithm that provides automatic video analysis in order to classify tissue regions in images into two categories: normal and abnormal. In order to achieve high correct classification rates, we first pre-process the image data by removing the noise in it and by normalizing the intensity values of the image pixels. Next, we use the well-known SIFT descriptor algorithm with Bag of Feature (BoF) approach for feature extraction. For training, Support Vector Machine (SVM) is trained on the extracted features using the training dataset. Finally a testing data set is used to assess the performance of the proposed algorithm. The early experimental results are very encouraging and show high correct classification rates, reaching up to 98.25% for images with polyps. The novelty of the proposed algorithm is the combination of using specific SIFT features with Bof and the vignetting correction to capture the local characteristics of the abnormalities in capsule video endoscopy images.