Edmund Wadge

The usage of soft-computing methodologies in interpreting capsule endoscopy

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 and regions affected by diseases, such as ulcer or coli, may have different texture features. In this paper schemes have been developed to extract 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 M2A Swallowable Imaging Capsule. The implementation of neural network 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 method.

The use of gas-sensor arrays to diagnose urinary tract infections.

Sensorial analysis based on the utilisation of human senses, is one of the most important and straightforward investigation methods in food and chemical analysis. An electronic nose has been used to detect in vivo Urinary Tract Infections from 45 suspected cases that were sent for analysis in a UK Health Laboratory environment. These samples were analysed by incubation in a volatile generation test tube system for 4-5 h. The volatile production patterns were then analysed using an electronic nose system with 14 conducting polymer sensors. An intelligent model consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on learning techniques has been considered. The implementation of an Extended Normalised Radial Basis Function network with advanced features for determining its size and parameters and the concept of fusion of multiple classifiers dedicated to specific feature parameters has been also adopted in this study. The proposed scheme achieved a very high classification rate of the testing dataset, demonstrating in this way the efficiency of the proposed scheme compared with other approaches. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology.

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.

Optical pH measurement using chromatic modulation

A distimulus chromatic detection system allied with fibre optic light transmission has been used in the development of a low cost and accurate pH measurement system. The performance of the chromatic pH measurement system is compared with a number of optical measurement techniques, which are based on intensity modulation. The chromatic modulation technique has been shown to have advantages over intensity modulation, such as greater immunity to fibre bending, and maintaining calibration when extending the length of the optical fibres used to address the modulator.

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.