Download Clinical Image-Based Procedures. From Planning to by Martin Hoßbach, Johannes Gregori (auth.), Klaus Drechsler, PDF

By Martin Hoßbach, Johannes Gregori (auth.), Klaus Drechsler, Marius Erdt, Marius George Linguraru, Cristina Oyarzun Laura, Karun Sharma, Raj Shekhar, Stefan Wesarg (eds.)

This publication constitutes the completely refereed post-workshop complaints of the foreign Workshop on scientific Image-based systems: From making plans to Intervention, CLIP 2012, held in great, France, along side the fifteenth overseas convention on clinical snapshot Computing and Computer-Assisted Intervention, MICCAI 2012. This profitable workshop was once a efficient and fascinating discussion board for the dialogue and dissemination of clinically confirmed, state of the art equipment for image-based making plans, tracking and review of clinical strategies. The sixteen papers awarded during this quantity have been rigorously reviewed and chosen from 24 submissions.

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Read or Download Clinical Image-Based Procedures. From Planning to Intervention: International Workshop, CLIP 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 5, 2012, Revised Selected Papers PDF

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Extra resources for Clinical Image-Based Procedures. From Planning to Intervention: International Workshop, CLIP 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 5, 2012, Revised Selected Papers

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Constructing Mitral Valve Model. Given the previously estimated mitral annulus and closure line, a patient-specific mitral valve model is constructed in the final stage of our framework to visualize the anatomical structure. First, a statistical shape model of the mitral valve MMV was built from a training set, including the posterior leaflet, the anterior leaflet, and the annulus [7]. Then, we extract a small number (typically 13) of 3D annulus and closure line points from both the valve model MMV and the anatomy shape model M ac in Sec.

For each sub-volume Vi , pre-aligned 3D Haar-like features are computed and used to train a detector Di which yields the target posterior probability: p(Mi |tx , ty , tz , Vi ) = Di (tx , ty , tz , Vi ), (tx , ty , tz ) ∈ Vi (3) where p(Mi |tx , ty , tz , Vi ) is the probability of having a 3D annulus or closure line point at the location (tx , ty , tz ) within Vi . Consequently, we can optimize together the valve region θ and the anatomy shape M ac on an input image I by maximizing the following joint probability, arg max p(θ, M ac |I) = arg max p(θ|I)p(M ac |θ, I) θ,M ac θ,M ac p(M (i) ac |θ, I) = arg max p(θ|I) (i) θ,M ac (4) i (i) where M ac is the i-th point of the anatomy shape model M ac .

Assuming a Gaussian distribution for the probability of a feature fi corresponding to normal appearing, the probability of being an FCD lesion voxel is its complement: P (F CD|fi (x)) = 1 − P (N ormal|fi (x)) = 1 − G(fi (x); μ(fi (x)), σ(fi (x))) where fi ∈ {fth , fF A , f∠∇I , f||∇I||2 }. The probability of being a lesion in the 2D intensity joint distribution of fF LAIR and fT 1 is expressed as: F LAIR P (F CD|fF , fT 1 )(x) = 1 − G(fk (x); μ(fk (x)), σ(fk (x))) k≡T 1 The features are assumed to be statistically independent and a classification is obtain with the Naive Bayesian classifier [17].

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