Download e-book for kindle: Medical Content-Based Retrieval for Clinical Decision by Henning Müller, Hayit Greenspan (auth.), Hayit Greenspan,

By Henning Müller, Hayit Greenspan (auth.), Hayit Greenspan, Henning Müller, Tanveer Syeda-Mahmood (eds.)

ISBN-10: 3642366775

ISBN-13: 9783642366772

ISBN-10: 3642366783

ISBN-13: 9783642366789

This booklet constitutes the refereed complaints of the 3rd MICCAI Workshop on clinical Content-Based Retrieval for scientific determination aid, MCBR-CBS 2012, held in great, France, in October 2012. the ten revised complete papers offered including 2 invited talks have been rigorously reviewed and chosen from 15 submissions. The papers are divided on numerous issues on picture research of visible or multimodal clinical information (X-ray, MRI, CT, echo movies, time sequence data), computer studying of disorder correlations in visible or multimodal information, algorithms for indexing and retrieval of knowledge from visible or multimodal scientific databases, disorder model-building and medical determination aid structures according to visible or multimodal research, algorithms for clinical photo retrieval or category, structures of retrieval or type utilizing the ImageCLEF collection.

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Additional resources for Medical Content-Based Retrieval for Clinical Decision Support: Third MICCAI International Workshop, MCBR-CDS 2012, Nice, France, October 1, 2012, Revised Selected Papers

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1. Surface points extraction: the first three modules are the three steps for extracting surface points; the last image is the distance matrix generated from the surface points The opisthion is the mid-point of the posterior margin of the foramen magnum on the occipital bone [8]. The two points were chosen because of their locations at the front and back of the head, and because they are stable during the human growth process The nasion and opisthion are detected as follows. First, the plane of symmetry of the left and right sides of the skull on which the landmarks are expected to be is extracted.

While craniosynostoses of multiple different types can be easily diagnosed, quantifying the severity of the abnormality is much more subjective and not a standard part of clinical practice. For this purpose we have developed a severity-based retrieval system that uses a logistic regression approach to quantify the severity of the abnormality of each of three types of craniosynostoses. We compare several different sparse feature selection techniques: L1 regularized logistic regression, fused lasso, and clustering lasso (cLasso).

2 Related Literature Calvarial (skull) abnormalities are frequently associated with severely impaired central nervous system functions due to brain abnormalities, increased intracranial pressure and abnormal build-up of cerebrospinal fluid. In [3], Shapiro et al. 2 deletion syndrome (a genetic disorder) and deformational plagiocephaly/brachycephaly. They provided feature extraction tools for the study of craniofacial anatomy from 3D mesh data Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models 35 obtained from the 3dMD active stereo photogrammetry system.

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Medical Content-Based Retrieval for Clinical Decision Support: Third MICCAI International Workshop, MCBR-CDS 2012, Nice, France, October 1, 2012, Revised Selected Papers by Henning Müller, Hayit Greenspan (auth.), Hayit Greenspan, Henning Müller, Tanveer Syeda-Mahmood (eds.)


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