Bleeding Detection in Wireless Capsule Endoscopy Images Using Texture and Color Features

Technology development enables progress in numerous areas and one of the relatively recent examples is wireless capsule endoscopy. It is used for detailed examination of a digestive track. Capsule size camera is swallowed by a patient and it takes thousands of images during the travel through digestive tract. The obtained images are used to detect different anomalies such as bleedings. In this paper we propose a method for automatic bleeding detection in capsule endoscopy images based on color and texture features. The proposed method is region based and it uses HSI and CIE Lab color spaces along with uniform local binary pattern for describing each region. Based on these features, regions are classified by support vector machine into three groups: background, bleeding or non-bleeding region. The proposed method was tested on benchmark dataset and the results were compared with other state-of-the-art method. Our proposed method shows competitive results based on the Dice similarity coefficient and misclassification error.

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