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DWT based Feature Extraction for Classification of Untreated MRI Mammogram of Breast Cells and Normal Cells

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Sushma S., Balasubramanian S., Latha K. C.
10.5120/ijca2017912797

Sushma S., Balasubramanian S. and Latha K C.. DWT based Feature Extraction for Classification of Untreated MRI Mammogram of Breast Cells and Normal Cells. International Journal of Computer Applications 157(8):37-40, January 2017. BibTeX

@article{10.5120/ijca2017912797,
	author = {Sushma S. and Balasubramanian S. and Latha K. C.},
	title = {DWT based Feature Extraction for Classification of Untreated MRI Mammogram of Breast Cells and Normal Cells},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {8},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {37-40},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume157/number8/26855-2017912797},
	doi = {10.5120/ijca2017912797},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

A standout amongst the most effective strategies for bosom malignancy early discovery is mammography. Another strategy for identification and arrangement of miniaturized scale calcifications is displayed. It should be possible in four phases: in the first place, pre processing stage manages clamour expulsion, and standardized the picture. Second stage, K-Means bunching (KMC) is utilized for division and pectoral muscle extraction utilizing territory figuring lastly smaller scale calcifications identification. Third stage comprises of two dimensional discrete wavelet changes are separated from the discovery of miniaturized scale calcifications. And after that, nine measurable components are figured from the LL band of wavelet change.

References

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Keywords

DWT (Discrete Wavelet Transform), K-nearest neighbor, mean, standard deviation, MRI Mammogram