Maria Boulougoura

A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images

Wireless capsule endoscopy (WCE) constitutes a recent technology in which a capsule with micro-camera attached to it, is swallowed by the patient. This paper presents an integrated methodology for detecting abnormal patterns in WCE images. Two issues are being addressed, including the extraction of texture features from the texture spectra in the chromatic and achromatic domains from each colour component histogram of WCE images and the concept of a fusion of multiple classifiers. The implementation of an advanced neuro-fuzzy learning scheme has been also adopted in this paper. The high detection accuracy of the proposed system provides thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in WCE.

Neural network-based approach for the classification of wireless-capsule endoscopic images

The importance of computer-assisted diagnosis in endoscopy is to assist the physician in detecting the status of tissues by characterising the features from the endoscopic image. In this paper schemes have been developed to extract new texture features from the texture spectra in the chromatic and achromatic domains for a selected region of interest from each colour component histogram of images acquired by the new M2A swallowable imaging capsule. The concept of fusion of multiple classifiers dedicated to specific feature parameters and the implementation of an advanced intelligent scheme have been also adopted in this study. The high detection accuracy of the proposed systems provides thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in capsule endoscopy.

Computer-assisted diagnosis of wireless-capsule endoscopic images using neural network based techniques

Computerised processing of medical images can ease the search of the representative features in the images. The endoscopic images possess rich information expressed by texture. In this paper schemes have been developed to extract texture features from the texture spectra in the chromatic and achromatic domains for a selected region of interest from each colour component histogram of images acquired by the new M2A Swallowable Capsule. The implementation of advanced learning-based schemes and the concept of fusion of multiple classifiers have been also adopted in this paper. The preliminary test results support the feasibility of the proposed methodology.

Detecting abnormalities in capsule endoscopic images by textural description and neural networks

In this paper, a detection system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images is presented. The endoscopic images possess rich information expressed by texture. Schemes have been developed to extract texture features from the texture spectra in the chromatic and achromatic domains for a selected region of interest from each colour component histogram of images acquired by the new M2A Swallowable Capsule. The implementation of an advanced neural network scheme and the concept of fusion of multiple classifiers have been also adopted in this paper. The preliminary test results support the feasibility of the proposed method.