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--- netscape_list [2014/05/31 17:33]
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-!!!Evaluation of Appearance Models of the Brain
+!!!Netscape candidates
-!!Abstract
-Appearance models are an applicable approach to the
+* jeremytoday (+3) - http://www.netscape.com/member/jeremytoday
-analysis of anatomical variability. They are able to
-distinguish between groups, e.g. normal and diseased,
-as a model encapsulates the properties of a group from
-which it was derived. The construction of such models
-is closely-related to the task of registration and they
-require the correspondence, which registration is able
-to obtain.
-We developed a framework which evaluates appearance
+* pagey (+2/-1) - http://www.netscape.com/member/pagey
-models, based on the statistics of large sets of
-images. The framework is capable of distinguishing
-between good models of the brain and worse ones.
-Furthermore, it provides a method of validating models.
-It does so by measuring how well a model and its data
-fit together.
-Two measures are defined which reflect on the quality
-of a model. The first of these -- specificity --
-approximates the level to which data generated by the
-model fits data from which the model was constructed.
-The complementary measure -- generalisation -- is able
-to quantify 'distance' between data from which the
-model was constructed and model-generated data.
-Results show that as models degrade in quality, their
+!!!Digg candidates
-specificity and generalisation ability rise, as
-expected. The algorithms are used to compare models of
-the brains, which were built automatically by
-independent approaches of registration. This greatly
-helps in identifying better model construction
-algorithms, which are analogous to registration algorithms.
-!!1 Introduction
-A powerful method for the modelling of anatomy was
+* aidenag ( +4 ) - http://digg.com/users/aidenag
-introduced by Edwards et al. [Edwards] and it is known as
-appearance models -- a natural successor to shape
-models [Cootes]. This method requires a large enough set of
-data, which is representative of a population and
-ideally spans its full variability. Appearance models
-are able to learn what characterises inter-subject
-changes and determine the prominence of the main
-characteristics. Hence, it is able to identify changes
-and derive a model that encapsulates change -- all in a
-data-driven manner.
-Non-rigid image registration is ubiquitously used as
+* curtissthompson ( + 3 ) - http://digg.com/users/curtissthompson
-the basis for analysis of medical images. The results
-of registration can be used for structural analysis,
-atlas matching, and analysis of change. Methods for
-obtaining registration are are well-established and
-quite uniform. The goal is achieved by warping pairs of
-images so that they appear more similar. The similarity
-leads to overlap, which allows corresponding structures
-to be identified. This problem is complementary to that
-of modelling groups of images. A statistical model of a
-group of images needs dense correspondence to be
-defined across the group; non-rigid registration
-provides exactly that.
-Since the emergence of appearance models, attempts have
+* GregD (+2) - http://www.digg.com/users/gregd  &    http://www.treelimb.org
-been made to reproduce and improve it. To name a few
-such efforts, Stegmann [Stegmann] built 4-dimensional cardiac
-models and Reuckert et al. [Rueckert] derived statistical
-deformation models from several registrations of the
-brain. Models have been built in a variety of ways, but
-what is yet lacked is the ability to compare them. It
-becomes clear from experience that attempts to
-distinguish between them by eyesight is hopeless. More
-recently, appearance models were built automatically
-using piece-wise affine registration [IPMI - YET TO
-ADD]. Evaluation of models in this particular case
-enables evaluation of registration algorithms.
-The idea of evaluating models is not unexampled. Davies
+!!!Newsvine candidates
-et al. [Davies] explored the evaluation of shape models and
-ultimately developed a robust framework. This paper
+*
-outlines a principled approach to the evaluation of
-appearance models, which is a challenging task since
+!!!Reddit candidates
-their complexity is very high. The approach is shown to
-be reliable in evaluation of brain models (FOOTER: Examples from non-medical domains are beyond the remit
+* Jeff (+5) - http://reddit.com/user/Fedquip/
-of this paper, but they have been very successful.) and it is then used to learn about registration
-algorithms, from which appearance models have been derived.
+* Jeroen (+3) - http://reddit.com/user/BioGeek/
+!!!Blogger candidates
+* Steven(insidesocialnews.blogspot.com) (+1/-1)
+* Chris (http://www.windingroad.com/) (+2)
+!!!Delicious candidates
+*

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