Computer Aided Detection of Clustered Microcalcification: A Survey

Author:

Kumar M.N. Arun1,Kumar M.N. Anil2,Sheshadri H.S.3

Affiliation:

1. Department of Computer Science and Engineering, Federal Institute of Science and Technology, Ernakulam, India

2. Department of Electronics and Communication Engineering, Federal Institute of Science and Technology, Ernakulam, India

3. Department of Electronics and Communication Engineering, PES College of Engineering, Mandya, Karnataka, India

Abstract

Background: This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques. </P><P> Discussion: The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized. Conclusion: The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.

Publisher

Bentham Science Publishers Ltd.

Subject

Radiology, Nuclear Medicine and imaging

Reference89 articles.

1. Thomas N, Heinz OP. Scale-Space Signatures for the detection of clustered microcalcifiations in Digital Mammograms.

2. Moti Melloul. Segmentation of microcalcification in X-ray mammogram using entropy thresholding. PhD dissertation, The Hebrew University of Jerusalem 2001

3. Naga RM, Rangaraj MR, Leo JE. Detection of breast masses in mammograms by density slicing and texture flow-field analysis.

4. San KL, Pau CC. Chein-I, et al. Classification of clustered microcalcifications using a shape cognitron neural network.

5. Ferrari RJ, Rangayyan RM, Desautels JEL, Desautels RA. Borges, Frère AF. Automatic identification of the pectoral muscle in mammograms.

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