Earticle

초록

영어

This paper proposes a novel approach for processing digital mammograms to detect micro-calcifications. They may be so small that they are almost undetectable visually, but it could be indicators of a possible malignancy. An analysis algorithm based on optical holographic property of images and clustering principles are proposed to detect the micro-calcifications. This process consists of three stages. In the first stage the mammographic patterns are subjected to optical holographic analysis. The resulting images are passed to the second stage, in which morphological operations are performed. The third stage detects the malignant portions of the mammographic pattern using unsupervised texture classification by extracting laws features. Texture classification is an important image processing task with a broad application range. Many different techniques for texture classification have been explored. This paper explores the unsupervised classifications of digital mammograms using K-means and Fuzzy C-means approaches. Results show that the proposed techniques detect the malignant portions of the breast very well thus enabling earlier detection of tumor.

목차

Abstract
 1. Introduction
 2. Motivation
 3. Methodology
  3.1. Optical Scanning Holography and Reconstruction
  3.2. Dilation
  3.3. Laws Features Extraction
  3.4 Classification
 4. Calcification of Soft Tissue Lesions/Masses in Mammograms and Performance Evaluation
 5. Conclusion
 References

키워드

저자

• Dr. A. Brintha Therese [ School of Electronics, VIT University ]
• Dr. S. Sundaravadivelu [ Department of ECE, SSN College of Engineering ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605