Abnormal Image Detection in Endoscopy Videos Using a Filter Bank and Local Binary Patterns.

Finding mucosal abnormalities (e.g., erythema, blood, ulcer, erosion, and polyp) is one of the most essential tasks during endoscopy video review. Since these abnormalities typically appear in a small number of frames (around 5% of the total frame number), automated detection of frames with an abnormality can save physician's time significantly. In this paper, we propose a new multi-texture analysis method that effectively discerns images showing mucosal abnormalities from the ones without any abnormality since most abnormalities in endoscopy images have textures that are clearly distinguishable from normal textures using an advanced image texture analysis method. The method uses a "texton histogram" of an image block as features. The histogram captures the distribution of different "textons" representing various textures in an endoscopy image. The textons are representative response vectors of an application of a combination of Leung and Malik (LM) filter bank (i.e., a set of image filters) and a set of Local Binary Patterns on the image. Our experimental results indicate that the proposed method achieves 92% recall and 91.8% specificity on wireless capsule endoscopy (WCE) images and 91% recall and 90.8% specificity on colonoscopy images.

SAPPHIRE: a toolkit for building efficient stream programs for medical video analysis.

This paper describes the design and implementation of SAPPHIRE--a novel middleware and software development kit for stream programing on a heterogeneous system of multi-core multi-CPUs with optional hardware accelerators such as graphics processing unit (GPU). A stream program consists of a set of tasks where the same tasks are repeated over multiple iterations of data (e.g., video frames). Examples of such programs are video analysis applications for computer-aided diagnosis and computer-assisted surgeries. Our design goal is to reduce the implementation efforts and ease collaborative software development of stream programs while supporting efficient execution of the programs on the target hardware. To validate the toolkit, we implemented EM-Automated-RT software with the toolkit and reported our experience. EM-Automated-RT performs real-time video analysis for quality of a colonoscopy procedure and provides visual feedback to assist the endoscopist to achieve optimal inspection of the colon during the procedure. The software has been deployed in a hospital setting to conduct a clinical trial.