A Local Flow Phase Stretch Transform for Robust Retinal Vessel Detection
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Gao, Y
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Auckland, New Zealand
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A wide range of applications needs the analysis of biomedical images as a fundamental task to extract meaningful information and allow high throughput measurements. A new method for the detection of curve-like structures in biomedical images is presented by exploiting local phase vector and the structural anisotropy information at various directions. We introduce an oriented gaussian derivative quadrature filter not only for estimating the local phase vectors, which include line features, but also for its immunity to inhomogeneous intensity and its capability to enhance curved structures having various diameters, leading to more reliable hessian analysis. A novel measure function-based hessian tensor is proposed to detect curvilinear patterns by incorporating the anisotropic indices (coherence and linearity) of curved features, producing a uniform and strong response. Over multiple orientations, the responses are maximized to achieve a rotationally invariant response, and to detect target structures with different widths and illuminations. The evaluation of the proposed method on the extraction of retinal vessels and leaf venation patterns exhibits its superior performance against state-of-the-art methods.
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Lecture Notes in Computer Science
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12002
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© 2020 Springer International Publishing AG. This is an electronic version of an article published in Lecture Notes In Computer Science (LNCS), 12002, 2020. Lecture Notes In Computer Science (LNCS) is available online at: http://link.springer.com// with the open URL of your article.
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Artificial intelligence
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Challoob, M; Gao, Y, A Local Flow Phase Stretch Transform for Robust Retinal Vessel Detection, Lecture Notes in Computer Science, 2020, 12002, pp. 251-261