By Junning Li, Yonggang Shi, Ivo D. Dinov, Arthur W. Toga (auth.), Li Shen, Tianming Liu, Pew-Thian Yap, Heng Huang, Dinggang Shen, Carl-Fredrik Westin (eds.)
This ebook constitutes the refereed lawsuits of the 3rd foreign Workshop on Multimodal mind snapshot research, MBIA 2013, held in Nagoya, Japan, on September 22, 2013 along with the sixteenth foreign convention on scientific snapshot Computing and machine Assisted Intervention, MICCAI. The 24 revised complete papers awarded have been conscientiously reviewed and chosen from 35 submissions. The papers are prepared in topical sections on research, methodologies, algorithms, software program platforms, validation methods, benchmark datasets, neuroscience and medical applications.
Read Online or Download Multimodal Brain Image Analysis: Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings PDF
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Extra resources for Multimodal Brain Image Analysis: Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings
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The second prior is the spatial regularization. To regularize the estimation of the transformation Tk , the gradient magnitude ||∇Tk ||2 over the image is penalized, that is equivalent to the application of a 3D Gaussian filter G(x; σS ) with standard deviation σS to the 44 D. Pilutti, M. Strumia, and S. Hadjidemetriou spatial transformation Tk at iteration k that gives the final estimate of the total transformation Tk = Tk ∗ G(x; σS ). 4 Order of Computational Complexity The complexity of the method developed in this work is significantly lower compared to that of the multicontrast extension of the B-Splines method with the mutual information for the same spatial resolution.
2 Method We propose a framework that constructs 4D models of pathological anatomy starting from a healthy template, to describe changes at different time points accounting for the complete 4D information. Our framework also leverages known domains, such as brain tumors, where we have a rich collection of information in the form of segmented tumor images with varying size, shape, deformations, and appearance. [11]. org/projects/tumorsim Modeling 4D Pathological Anatomy Changes 33
Fig.
Hadjidemetriou Fig. 1. Diagram describing the registration of two images with the proposed registration method. A preliminary rigid and affine registration is performed and the result is used to initialize the iterative non-rigid registration step until the stop criterion is met. 1 Computation of the Joint Intensity Statistics and Their Wiener Restoration Two images Iref and Imov under assumed perfect alignment give rise to the joint histogram Hideal . The joint statistics H0 of the misregistered images are considered to result from the convolution of Hideal with a 2D Gaussian filter GHi,j (σH ): H0 = Hideal ∗ GHi,j (σH ) + nH , (1) where σH is the standard deviation of the Gaussian convolution, ∗ is the convolution, nH is the noise and i, j are the indices for the dynamic ranges of Iref and Imov , respectively.