NDLI: Biomedical image analysis using markov random fields & efficient linear programing

Content Provider	IEEE Xplore Digital Library
Author	Komodakis, N. Besbes, A. Glocker, B. Paragios, N.
Copyright Year	2009
Description	Author affiliation: Department of Applied Mathematics of Ecole Centrale de Paris, France (Besbes, A.; Paragios, N.) \|\| Department of Computer Science of the University of Crete, Greece (Komodakis, N.) \|\| Computer Science Department of the Technische Universita¿t Mu¿nchen, Germany (Glocker, B.)
Abstract	Computer-aided diagnosis through biomedical image analysis is increasingly considered in health sciences. This is due to the progress made on the acquisition side, as well as on the processing one. In vivo visualization of human tissues where one can determine both anatomical and functional information is now possible. The use of these images with efficient intelligent mathematical and processing tools allows the interpretation of the tissues state and facilitates the task of the physicians. Segmentation and registration are the two most fundamental tools in bioimaging. The first aims to provide automatic tools for organ delineation from images, while the second focuses on establishing correspondences between observations inter and intra subject and modalities. In this paper, we present some recent results towards a common formulation addressing these problems, called the Markov Random Fields. Such an approach is modular with respect to the application context, can be easily extended to deal with various modalities, provides guarantees on the optimality properties of the obtained solution and is computationally efficient.
Starting Page	6628
Ending Page	6631
File Size	863047
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5332535
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biomedical imaging Image analysis Markov random fields Mathematical model In vivo Visualization Anatomical structure Biological systems Computer science Mathematics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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