Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and CorrentropyFeatures Extracted from Fundus Images

Automatic Hookworm Detection in Wireless Capsule Endoscopy Images

Abstract

Wireless capsule endoscopy (WCE) has become a widely used diagnostic technique to examine inflammatory bowel diseases and disorders. As one of the most common human helminths, hookwormis a kind of small tubular structure with grayish white or pinkish semi-transparent body, which is with a number of 600 million people infection around the world. 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. This is the first few works to comprehensively explore the automatic hookworm detection for WCE images. To capture the properties of hookworms, the multi scale dual matched filter is first applied to detect the location of tubular structure. Piecewise parallel region detection method is then proposed to identify the potential regions having hookworm bodies. To discriminate the unique visual features for different components of gastrointestinal, the histogram of average intensity is proposed to represent their properties. 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 440 K WCE images demonstrate that the proposed approach achieves a promising performance and outperforms the state-of-the-art methods. Moreover, the high sensitivity in detecting hookworms indicates the potential of our approach for future clinical application.

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