Literature DB >> 19828291

A comparison of two methods for the segmentation of masses in the digital mammograms.

R B Dubey1, M Hanmandlu, S K Gupta.   

Abstract

An accurate and standardized technique for breast tumor segmentation is a critical step for monitoring and quantifying breast cancer. The fully automated tumor segmentation in mammograms presents many challenges related to characteristics of an image. In this paper, a comparison of two different semi-automated methods, viz., level set and marker controlled watershed methods that perform an accurate and fast segmentation of tumor is made. The robustness of the proposed methods is demonstrated by the segmentation of a set of 17 mammogram images. Numerical validation of the results is also provided. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

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Year:  2009        PMID: 19828291     DOI: 10.1016/j.compmedimag.2009.09.002

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  5 in total

1.  A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen.

Authors:  Abhinav Gupta; Bhuvan Gosain; Sunanda Kaushal
Journal:  J Clin Monit Comput       Date:  2010-08-17       Impact factor: 2.502

2.  Mammogram segmentation using maximal cell strength updation in cellular automata.

Authors:  J Anitha; J Dinesh Peter
Journal:  Med Biol Eng Comput       Date:  2015-04-05       Impact factor: 2.602

3.  Marker-controlled watershed for lesion segmentation in mammograms.

Authors:  Shengzhou Xu; Hong Liu; Enmin Song
Journal:  J Digit Imaging       Date:  2011-10       Impact factor: 4.056

4.  An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms.

Authors:  Min Dong; Xiangyu Lu; Yide Ma; Yanan Guo; Yurun Ma; Keju Wang
Journal:  J Digit Imaging       Date:  2015-10       Impact factor: 4.056

5.  A Semi-Supervised Method for Tumor Segmentation in Mammogram Images.

Authors:  Hanie Azary; Monireh Abdoos
Journal:  J Med Signals Sens       Date:  2020-02-06
  5 in total

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