Earticle

초록

영어

Breast imaging case not only image low level features but also has image semantic features. In order to implement multimode retrieval of breast imaging, using feature selection algorithm based on association rules to select features, digging out the associated relationship between image low level features and image semantic features, and then taking advantage of association classification algorithm to get image visual semantic features, which reduced the semantic gap between image low level features and visual semantic features, at last, making similarity measure combined with low level features, to make multimode retrieval come true. As the results show, this method improve the performance of breast imaging case retrieval and provide more meaningful decision support for doctors.

목차

Abstract
 1. Introduction
 2. Establishment of Associative Classification Model
  2.1. Related Concepts of Association Rules
  2.2. Features Selection
  2.3. Associative Classification Algorithm
  2.4. Establishment of Breast Masses Shape Classification Model
  2.5. Retrieval System
 3. Retrieval Results
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Qian Wang [ School of Computer Science ]
• Yanan Lv [ School of Electrical and Electronic Engineering, Harbin University of Science and Technology Harbin 150080, China ]
• Lixin Song [ School of Electrical and Electronic Engineering, Harbin University of Science and Technology Harbin 150080, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605