Segmentation of Bleeding Regions in Wireless Capsule Endoscopy Images an Approach for inside Capsule Video Summarization
This work addresses the need for efficient video summarization in WCE systems to aid diagnosis, but it is incremental as it builds on existing methods with hardware-aware optimizations.
The authors tackled the problem of detecting bleeding regions in wireless capsule endoscopy images to enable video summarization, achieving segmentation using a quantized multi-layer perceptron without multiplications, tested via simulation on a WCE bleeding dataset.
Wireless capsule endoscopy (WCE) is an effective means of diagnosis of gastrointestinal disorders. Detection of informative scenes by WCE could reduce the length of transmitted videos and can help with the diagnosis. In this paper we propose a simple and efficient method for segmentation of the bleeding regions in WCE captured images. Suitable color channels are selected and classified by a multi-layer perceptron (MLP) structure. The MLP structure is quantized such that the implementation does not require multiplications. The proposed method is tested by simulation on WCE bleeding image dataset. The proposed structure is designed considering hardware resource constrains that exist in WCE systems.