Open AccessMicrocalcification clusters processing in mammograms based on relevance vector machine with adaptive kernel learning
Author(s) -
Chen Yao,
Houjin Chen,
Yongyi Yang,
Yanfeng Li,
ZhengFu Han,
Shengjun Zhang
Publication year - 2013
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.62.088702
Subject(s) - computer science , artificial intelligence , pattern recognition (psychology) , microcalcification , support vector machine , image processing , cluster analysis , kernel (algebra) , relevance (law) , computer vision , mammography , image (mathematics) , mathematics , medicine , cancer , combinatorics , breast cancer , political science , law
Using the method of adaptive kernel learning based relevance vector machine (ARVM) and combining the morphological filtering and the clustering criterion recommended by Kallergi, a new algorithm for microcalcification (MC) clusters processing in mammograms is investigated. Firstly, the detection of MC is formulated as a supervised-learning problem. Then the ARVM is used as a classifier to determine whether an MC object is present at each location in the mammogram and a morphological processing is used to remove the isolated spurious pixels. Finally, the identified MC clusters are obtained by Kallergi criterion. To improve the computational speed, a fast processing method based on ARVM is developed, in which the whole image is decomposed first into sub-image blocks for parallel operation. Experimental results indicate that the ARVM method outperforms the RVM method and, in particular, the fast processing method could greatly reduce the testing time.