Capillary extraction by detecting polarity in circular profiles. Issue 5 (1st May 2016)

• Record Type: Journal Article
• Authors: Lu, Na; Silva, Jharon N.; Gu, Yu; Wu, Hulin; Gelbard, Harris A.; Dewhurst, Stephen; Miao, Hongyu
• Is Part Of: IET image processing. Volume 10:Issue 5(2016)
• Page Start: 339
• Page End: 348
• Publication Date: 2016-05-01
• Subjects: image segmentation -- biomedical imaging; capillary extraction -- blood vessels -- morphometric analysis -- biomedical problems -- angiogenesis -- image preprocessing algorithms -- vessel image segmentation -- capillary images -- image pixels -- tube‐like objects -- cloud‐like contaminations -- structural noise; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2015.0069
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Quantitative characterisation of blood vessels from images (e.g. morphometric analysis) is important to a variety of biomedical problems such as disease diagnosis and staging or assessment of angiogenesis. However, the accuracy of such characterisation depends heavily on the outcome of image preprocessing algorithms. Therefore, more efficient algorithms for vessel image segmentation or extraction have emerged within the past few years. Nevertheless, such methods may perform poorly or fail entirely for images with large noise, even after a careful tuning of parameters. Moreover, none of these methods intentionally considers the removal of structural noise (such as spots that obscure and/or are brighter than vessels). To address these issues, the authors propose a novel thresholding algorithm for capillary images by detecting the polarity in the circular profiles (PCPs) of image pixels. This can robustly distinguish tube‐like objects from both cloud‐like contaminations and structural noise. Extensive simulation studies based on multiple evaluation criteria suggest that the PCP algorithm typically has a superior performance over other representative approaches. Finally, they also demonstrate the satisfactory performance of the PCP method on real image data.