Discriminative Pattern Measure and Classification of Hook Worm WCE Images

Abstract

Wireless capsule endoscopy (WCE) has become a widely used diagnostic technique to examine several life time diseases and disorders. Careful microscopic examination of WCE images is the only way to effective detection of hook worm. Automatic hookworm detection is a challenging task due to poor quality of images, presence of extraneous matters, complex structure of gastrointestinal, and diverse appearances in terms of color and texture. To discriminate the unique visual features for different components of gastrointestinal, the histogram of average intensity is proposed to represent their properties. This paper shows that a properly selected subset of patterns encoded in LTP forms along with wavelet transform based frequency domain parameters extraction is an efficient and robust texture description which can achieve better classification rates in comparison with the existing methods. In order to deal with the problem of imbalance data, Rusboost is deployed to classify WCE images. Experiments on a diverse and large scale dataset with large set of WCE images demonstrate that the proposed approach achieves a promising performance and outperforms the stateof-the-art methods. Moreover, the high sensitivity in detecting hookworms indicates the potential of our approach for future clinical application. Computer simulation involved the following tests: comparing the impact of dimension on the system before and after the influence of local ternary pattern, comparing the performance of the proposed algorithms with ANN classifier  on subimages and whole images, and comparing the results of some of the existing systems with the proposed system