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Detection of clusters of microcalcifications using a K-nearest neighbour classifier

Hojjatoleslami, Ali, Kittler, Josef (1996) Detection of clusters of microcalcifications using a K-nearest neighbour classifier. Digital Mammography, IEE Colloquium on, (10/1). (doi:10.1049/ic:19960493) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:27758)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
https://doi.org/10.1049/ic:19960493

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
DOI/Identification number: 10.1049/ic:19960493
Subjects: R Medicine > R Medicine (General)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Divisions > Division of Natural Sciences > Biosciences
Depositing User: S.A. Hojjatoleslami
Date Deposited: 19 May 2011 09:06 UTC
Last Modified: 16 Nov 2021 10:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/27758 (The current URI for this page, for reference purposes)

University of Kent Author Information

Hojjatoleslami, Ali.

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