Mammogram image segmentation for improving the diagnosis of dense breast issues

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Dense breast tissues can raise the hazard of breast cancer for women because breast of that type has more dense tissues than normal do. It also reduces the effective Mammogram when the produced diagnosed images present lapping intensities for multiple different tissues. However, image segmentation algorithms present the property of separation a whole image into sub sections, which is called regions, and reduce the effects of additive noise caused by artifacts. This paper presents the performance evaluation of Fuzzy segmentation using C-mean algorithms (FCM). Clustering classifier pursued by feature enhancement was the effective way to establish the required results. The last are analyzed to emphasize the accurate segment for diagnosis. The implementation consists of three image samples from real patients executed by the main algorithm with and without adding special information.

    The final results confirmed the influence of the investigated work for dense breast diagnosis. The performance evaluation is measured by the statistical significance of mean square error (MSE) for both cases of algorithms. Some specialists have supported the results and promoted it since it offered better visible dense images. In addition, the accuracy of segmentation can be achieved by increasing the number of clustering. Correspondingly, the segmentation besides clustering of dense breast has aided in making a good diagnosis.

     

     


  • Keywords


    Dense Breast; Mammogram; Image Segmentation; Fuzzy C-Mean; Competitive Learning Clustering.

  • References


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Article ID: 19689
 
DOI: 10.14419/ijet.v8i1.19689




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