Content-Based Mammogram Retrieval Using Mixed Kernel PCA and Curvelet Transform

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• 关键词：Mammography ; CBIR ; Kernel PCA ; Curvelet transform ; Mixed gaussian kernel
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10016
• 期：1
• 页码：582-590
• 全文大小：895 KB
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  9.Dhahbi, S., Barhoumi, W., Zagrouba, E.: Breast cancer diagnosis in digitized mammograms using curvelet moments. Comput. Biol. Med. 64, 79–90 (2015)CrossRef
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• 作者单位：Sami Dhahbi (18)
  Walid Barhoumi (18)
  Ezzeddine Zagrouba (18)
  
  18. Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LimTic Laboratory, Institut Supérieur d’Informatique (ISI), Université de Tunis El Manar, 2 Rue Abou Rayhane Bayrouni, 2080, Ariana, Tunisia
  
• 丛书名：Advanced Concepts for Intelligent Vision Systems
• ISBN：978-3-319-48680-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10016

文摘

Content-based image retrieval (CBIR) has recently emerged as a promising method to assist radiologists in diagnosing mammographic masses by displaying pathologically similar cases. In this paper, a CBIR system using curvelet transform and kernel principal component analysis (KPCA) is proposed. Thanks to its improved direction and edge representation abilities, curvelet transform first provides desirable mammographic features. Once the region of interest (ROI) is curvelet transformed, the KPCA is then applied and the first components are used as descriptors. Bearing in mind that neighbor points are the most important but faraway points may contain useful information in mammogram retrieval, we propose a new mixed kernel that overcomes the shortcoming of Gaussian kernels and emphasis neighbor points without neglecting faraway ones. The proposed mixed kernel is a mixture of two gaussian kernels with high and low sigma values. Experiments performed on a large dataset of mammograms showed the superiority of the proposed kernel over single gaussian kernels.