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Titlebook: Medical Image Understanding and Analysis; 21st Annual Conferen María Valdés Hernández,Víctor González-Castro Conference proceedings 2017 Sp

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楼主: coherent
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Superpixel-Based Line Operator for Retinal Blood Vessel Segmentationguish between lines and the edges, and thus allows more tolerance in the position of the respective contours. The results on three public datasets show superior performance to its competitors, implying its potential for wider applications.
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Automatic Detection and Identification of Retinal Vessel Junctions in Colour Fundus Photographyrossings. We achieve a high accuracy of 94% for junction detection and 88% for classification. Combined with work in segmentation, this method has the potential to facilitate automated localisation of blood clots and other disease symptoms leading to improved management of eye disease through aiding or replacing a clinicians diagnosis.
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Edge Aware Geometric Filter for Ultrasound Image Enhancementc as well as real ultrasound images. It is compared with the state-of-the-art speckle reducing filters. Improvements of 10.46% and 42% are noticed in mean square error and figure of merit, respectively.
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Fast Optic Disc Segmentation in Retinal Images Using Polar Transformisc. The methodology has shown considerable improvement over existing methods in terms of accuracy and processing time. The algorithm is evaluated on a number of publicly available retinal image sets which includes MESSIDOR, DIARETDB1, DRIONS-DB, HRF, DRIVE and RIM-ONE, with average spatial overlap approximately 85%.
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End-to-End Learning of a Conditional Random Field for Intra-retinal Layer Segmentation in Optical Co based methods employ handcrafted cost terms to define their energy and are not robust to the presence of abnormalities. We propose a novel, Linearly Parameterized, Conditional Random Field (LP-CRF) model whose energy is learnt from a set of training images in an end-to-end manner. The proposed LP-C
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Superpixel-Based Line Operator for Retinal Blood Vessel Segmentationovascular disease and many systemic diseases. Here, we propose a new framework for precisely segmenting vasculatures. The proposed framework consists of two steps. Inspired by the Retinex theory, a non-local total variation model is introduced to address the challenges posed by intensity inhomogenei
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