Twin support vector machines and subspace learning methods for microcalcification clusters detection
发布时间：2025-09-07
点击次数：
- DOI码：
- 10.1016/j.engappai.2012.04.003
- 发表刊物：
- ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- 摘要：
- This paper presents a novel framework for microcalcification clusters (MCs) detection in mammograms. The proposed framework has three main parts: (1) first, MCs are enhanced by using a simple-but-effective artifact removal filter and a well-designed high-pass filter; (2) thereafter, subspace learning algorithms can be embedded into this framework for subspace (feature) selection of each image block to be handled; and (3) finally, in the resulted subspaces, the MCs detection procedure is formulated as a supervised learning and classification problem, and in this work, the twin support vector machine (TWSVM) is developed in decision-making of MCs detection. A large number of experiments are carried out to evaluate and compare the MCs detection approaches, and the effectiveness of the proposed framework is well demonstrated. (C) 2012 Elsevier Ltd. All rights reserved.
- 论文类型：
- 期刊论文
- 卷号：
- 25
- 期号：
- 5
- ISSN号：
- 0952-1976
- 是否译文：
- 否
- 发表时间：
- 2012-01-01
- 收录刊物：
- SCI