NDLI: Supervised segmentation by iterated contextual pixel classification

Content Provider	IEEE Xplore Digital Library
Author	Loog, M. van Ginneken, B.
Copyright Year	2002
Description	Author affiliation: Image Sci. Inst., Univ. Med. Center Utrecht, Netherlands (Loog, M.; van Ginneken, B.)
Abstract	We propose a general iterative contextual pixel classifier for supervised image segmentation. The iterative procedure is statistically well-founded and can be considered a variation on the iterated conditional modes (ICM) of Besag (1983). Having an initial segmentation, the algorithm iteratively updates it by reclassifying every pixel, based on the original features and, additionally, contextual information. This contextual information consists of the class labels of pixels in the neighborhood of the pixel to be reclassified. Three essential differences with the original ICM are: (1) our update step is merely based on a classification result, hence a voiding the explicit calculation of conditional probabilities; (2) the clique formalism of the Markov random field framework is not required; (3) no assumption is made w.r.t. the conditional independence of the observed pixel values given the segmented image. The important consequence of properties 1 and 2 is that one can easily incorporate rate common pattern recognition tools in our segmentation algorithm. Examples are different classifiers-e.g. Fisher linear discriminant, nearest-neighbor classifier, or support vector machines-and dimension reduction techniques like LDA, or PCA. We experimentally compare a specific instance of our general method to pixel classification, using simulated data and chest radiographs, and show that the former outperforms the latter.
Starting Page	925
Ending Page	928
File Size	320697
Page Count	4
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1048456
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Pixel Iterative algorithms Probability Markov random fields Pattern recognition Vectors Linear discriminant analysis Principal component analysis Radiography
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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