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Characterization of Tumor Region Using Som and Neuro Fuzzy Techniques in Digital Mammography


Affiliations
1 Department of Computer Application, Madhav Institute of Technology and Science, Gwalior, India
 

Nowadays the most common type of cancer in women is breast cancer. This is the second main cause of cancer deaths in women. Digital mammography is the technique which is used to examine the breast. This is very much useful for the detection of breast diseases in women. The automatic detection of tumor or some type of deformity in the medical imaging is done by many researchers to develop some algorithms and methods. In this paper we are using SOM and Fuzzy c-means clustering techniques for tumor detection in digital mammography images. We then further calculate the statistical features of tumor like location of tumor, area, energy, entropy, idm, mean, contrast, mean and standard deviation which helps the radiologist to study the statistical information regarding breast cancer, so that the doctors can give better treatment to patients. For calculating these statistical properties we use region growing and region merging techniques.

Keywords

Gray Level Cooccurrence Matrix (GLCM), best Matching Unit (BMU), Epoch Number, Idm (iInverse Difference Moment), Micro-Calcifications.
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Abstract Views: 370

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  • Characterization of Tumor Region Using Som and Neuro Fuzzy Techniques in Digital Mammography

Abstract Views: 370  |  PDF Views: 160

Authors

Anamika Ahirwar
Department of Computer Application, Madhav Institute of Technology and Science, Gwalior, India
R. S. Jadon
Department of Computer Application, Madhav Institute of Technology and Science, Gwalior, India

Abstract


Nowadays the most common type of cancer in women is breast cancer. This is the second main cause of cancer deaths in women. Digital mammography is the technique which is used to examine the breast. This is very much useful for the detection of breast diseases in women. The automatic detection of tumor or some type of deformity in the medical imaging is done by many researchers to develop some algorithms and methods. In this paper we are using SOM and Fuzzy c-means clustering techniques for tumor detection in digital mammography images. We then further calculate the statistical features of tumor like location of tumor, area, energy, entropy, idm, mean, contrast, mean and standard deviation which helps the radiologist to study the statistical information regarding breast cancer, so that the doctors can give better treatment to patients. For calculating these statistical properties we use region growing and region merging techniques.

Keywords


Gray Level Cooccurrence Matrix (GLCM), best Matching Unit (BMU), Epoch Number, Idm (iInverse Difference Moment), Micro-Calcifications.