Quality assessment for wireless capsule endoscopy videos compressed via HEVC: From diagnostic quality to visual perception.

Maintaining the quality of medical images and videos is an essential part of the e-services provided by the healthcare sector. The convergence of modern communication systems and the healthcare industry necessitates the provision of better quality of service and experience by the service provider. Recent inclusion and standardization of the high efficiency video coder (HEVC) has made it possible for medical data to be compressed and transmitted over wireless networks with minimal compromise of the quality. Quality evaluation and assessment of these medical videos transmitted over wireless networks is another important research area that requires further exploration and attention. In this paper, we have conducted an in-depth study of video quality assessment for compressed wireless capsule endoscopy (WCE) videos. Our study includes the performance evaluation of several state-of-the-art objective video quality metrics in terms of determining the quality of compressed WCE videos. Subjective video quality experiments were conducted with the assistance of experts and non-experts in order to predict the diagnostic and visual quality of these medical videos, respectively. The evaluation of the metrics is based on three major performance metrics that include, correlation between the subjective and objective scores, relative statistical performance and computation time. Results show that the metrics information fidelity criterion (IFC), visual information fidelity-(VIF) and especially pixel based VIF stand out as best performing metrics. Furthermore, our paper reports the performance of HEVC compression on medical videos and according to the results, it performs optimally in preserving the diagnostic and visual quality of WCE videos at Quantization Parameter (QP) values of up to 35 and 37 respectively.

Detection of small colon bleeding in wireless capsule endoscopy videos.

In the recent years, wireless capsule endoscopy (WCE) technology has played a very important role in diagnosing diseases within the gastro intestinal (GI) tract of human beings. The WCE device captures images of the GI tract of patient with a certain frame rate. Physicians examine these images in order to find abnormalities in the GI tract. This examination process is very time consuming and hectic for the physician as a WCE device captures around 60,000 images on the average. At present, there are no standards defined for the WCE image classification. Computer aided methods help reducing the burden on the physicians by automatically detecting the abnormalities in the GI tract such as small colon bleeding. In this paper, a pixel based approach to detect bleeding regions in the WCE videos by using a support vector classifier is proposed. Threshold analysis in HSV color space is performed to compute the features for training an optimal support vector machine. The HSV features of the WCE images are fed to the trained support vector classifier for classification. Also, our method includes image enhancement and edge removal in WCE images, which is done prior to classification, for robust results. The method offers high sensitivity, specificity and accuracy in terms of correctly classifying images that contain bleeding regions as compared to another contemporary method. A detailed experimental analysis is also provided for the purpose of method evaluation.