Detection of clusters of microcalcifications using a K-nearest neighbour rule with locally optimum distance metrics

Hojjatoleslami, Ali and Kittler, Josef (1996) Detection of clusters of microcalcifications using a K-nearest neighbour rule with locally optimum distance metrics. Digital Mammography, IEE Colloquium on . pp. 267-272. (The full text of this publication is not available from this repository)

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Abstract

A method is proposed for the detection of clusters of microcalcifications. The method first segments the image into suspected regions using morphological filters and a new region growing to derive two boundaries for each region. Then a KNN classifier with two different distance measures, Euclidean distance and locally optimum distance measures, is considered for the task of classifying the regions as normal or MC. The last step of the algorithm uses a hierarchical nearest mean clustering method to find the location of clusters of MCs. The performance of the method on a set of normal and abnormal images is then presented

Item Type: Article
Subjects: R Medicine > R Medicine (General)
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Q Science > QA Mathematics (inc Computing science) > QA297 Numerical analysis
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Faculties > Science Technology and Medical Studies > School of Biosciences > Biomedical Research Group
Faculties > Science Technology and Medical Studies > Kent Institute of Medicine and Health Sciences (KIMHS)
Depositing User: Sayed Ali Hojjatoleslami
Date Deposited: 19 May 2011 09:13
Last Modified: 22 May 2014 09:52
Resource URI: http://kar.kent.ac.uk/id/eprint/27757 (The current URI for this page, for reference purposes)
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