Texture enhanced Statistical Region Merging with application to automatic knee bones segmentation from CT
Michael Howes, Mariusz Bajger, Gobert N. Lee, Francesca Bucci, Saulo Martelli · 2021
Statistical Region Merging technique belongs to the portfolio of very successful image segmentation methods across diverse domains and applications. The method is based on a solid probabilistic principle and was extended in various directions to suit specific applications, including those from medical domains. In its basic implementation the technique is based on a merging criterion relying on image pixel intensities. Sufficient to segment well some natural scene images, it often deteriorates dramatically when challenging medical images are segmented. In this study we introduce a new merging criterion into the method which utilizes texture characteristic of the image. We demonstrate that the enhanced criterion allows segmentation of knee bones in CT comparable to state-of-the-art outcomes found in literature while preserving the desirable properties of the original technique.