Visual saliency models for summarization of diagnostic hysteroscopy videos in healthcare systems

In clinical practice, diagnostic hysteroscopy (DH) videos are recorded in full which are stored in long-term video libraries for later inspection of previous diagnosis, research and training, and as an evidence for patients' complaints. However, a limited number of frames are required for actual diagnosis, which can be extracted using video summarization (VS). Unfortunately, the general-purpose VS methods are not much effective for DH videos due to their significant level of similarity in terms of color and texture, unedited contents, and lack of shot boundaries. Therefore, in this paper, we investigate visual saliency models for effective abstraction of DH videos by extracting the diagnostically important frames. The objective of this study is to analyze the performance of various visual saliency models with consideration of domain knowledge and nominate the best saliency model for DH video summarization in healthcare systems. Our experimental results indicate that a hybrid saliency model, comprising of motion, contrast, texture, and curvature saliency, is the more suitable saliency model for summarization of DH videos in terms of extracted keyframes and accuracy.