Computer Vision Pattern Recognition

Medical Image Computing and Computer-Assisted Intervention – by Nassir Navab, Joachim Hornegger, William M. Wells, Alejandro

By Nassir Navab, Joachim Hornegger, William M. Wells, Alejandro Frangi

The three-volume set LNCS 9349, 9350, and 9351 constitutes the refereed court cases of the 18th overseas convention on clinical snapshot Computing and Computer-Assisted Intervention, MICCAI 2015, held in Munich, Germany, in October 2015. in accordance with rigorous peer reports, this system committee conscientiously chosen 263 revised papers from 810 submissions for presentation in 3 volumes. The papers were equipped within the following topical sections: quantitative photo research I: segmentation and size; computer-aided prognosis: computer studying; computer-aided analysis: automation; quantitative snapshot research II: class, detection, gains, and morphology; complicated MRI: diffusion, fMRI, DCE; quantitative photograph research III: movement, deformation, improvement and degeneration; quantitative photo research IV: microscopy, fluorescence and histological imagery; registration: strategy and complicated purposes; reconstruction, photo formation, complex acquisition - computational imaging; modelling and simulation for analysis and interventional making plans; computer-assisted and image-guided interventions.

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Extra resources for Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference Munich, Germany, October 5–9, 2015, Proceedings, Part II

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Different types of cells, connective tissue, etc), and the variable appearances of glands and villi due to morphology, staining, and scale. Fig. 1. (a) A 3-D illustration of the dual glands and villi: Villi (top) are evagination of epithelium (green) into lumen (blue), and glands (bottom) are invagination of epithelium into extracellular material (red); (b) histology tissue images are 2-D slices of the 3-D structures; (c) some areas (black circles) that may cause false positives of glands. Some methods [4,8,10] were proposed for glands detection in H&E histology tissue images, which used a similar framework: (1) Find lumen regions; (2) for each lumen region, perform a region-growing like process to find the epithelium enclosing the lumen which is considered as the boundary of a gland.

These points are fitted with a closed-curve to obtain the neck curve and isolate the aneurysm’s dome, which is used to quantify the aneurysm. Detection and Quantification of Intracranial Aneurysms 7 Fig. 3. (a) Intravascular ray-casting from the centerlines to segmentation surface M yields (b) distance maps dj (ϑ, ϕ). (c) The edges in (b) are tentative points on the neck, which are used in a RANSAC-type fitting of closed-curve to obtain the final neck curve. Segmentation of each aneurysm and neighboring vessels is performed by growcut [9], which requires seed points within the vascular structures and on background.

Med. Image Anal. 13(6) (2009) 8. : Tissue classification based on 3D local intensity structures for volume rendering. IEEE T. Vis. Comput. Gr. 6(2), 160–180 (2000) 9. : Growcut - interactive multi-label n-d image segmentation by cellular automata. In: Proc. GraphiCon (2005) 10. : CTA-based angle selection for diagnostic and interventional angiography of saccular intracranial aneurysms. IEEE Trans. Med. Imag. 17(5), 831–841 (1998) Discriminative Feature Selection for Multiple Ocular Diseases Classification by Sparse Induced Graph Regularized Group Lasso Xiangyu Chen1 , Yanwu Xu1 , Shuicheng Yan2 , Tat-Seng Chua2 , Damon Wing Kee Wong1 , Tien Yin Wong2 , and Jiang Liu1 1 Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 2 National University of Singapore, Singapore Abstract.

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