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Segmenting curvilinear structures in retinal images is important in early diagnosing of some diseases and monitoring their progress. In this work, we proposed an automatic segmentation method to extract vascular network in CHASE data set. We utilized deep learning framework to build our layers that accept image patches as input and produce the segmented image as output. Our work characterized by its...
Human epithelial (HEp-2) cell specimens are obtained from indirect immunofluorescence (IIF) imaging for diagnosis and management of autoimmune diseases. Analysis of HEp2 cells is important and in this work we consider automatic cell segmentation and classification using spatial and texture pattern features and random forest classifiers. In this paper, we summarize our efforts in classification and...
In this paper, we consider confocal microscopy based vessel segmentation with optimized features and random forest classification. By utilizing multi-scale vessel-specific features tuned to capture curvilinear structures such as Frobenius norm of the Hessian eigenvalues, Laplacian of Gaussians (LoG), oriented second derivative, line detector and intensity masked with LoG scale map. we obtain better...
Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly...
Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for segmenting thin vessel structures using multiscale features based on Hessian, oriented second derivatives, Laplacian of Gaussian and line features. The...
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