NDLI: Identification of pre-processing technique for enhancement of mammogram images

Content Provider	IEEE Xplore Digital Library
Author	Sharma, J. Rai, J.K. Tewari, R.P.
Copyright Year	2014
Description	Author affiliation: Amity Sch. of Eng. & Technol., Amity Univ., Noida, India (Sharma, J.; Rai, J.K.) \|\| Appl. Mech. Dept., M.N.N.I.T., Allahabad, India (Tewari, R.P.)
Abstract	This paper presents an analysis and identification of image pre-processing techniques suitable for mammogram images. Pre-processing is one of the preliminary stages used for mammogram image enhancement to aid an early identification of suspicious lesions and micro calcifications. Nine image preprocessing techniques are considered here for mammogram images. These techniques are implemented using Matlab. The results obtained are compared on the basis of Peak Signal to Noise Ratio (PSNR) for a set of 30 mammogram images. A high value of PSNR indicates better suitability of the pre-processing technique for further image processing. Anisotropic diffusion and Median filtering are suitable for noise removal. Power-law transformation, morphological processing, and un-sharp masking are giving better enhancement results in terms of achieved PSNR values as compared to other pre-processing techniques analyzed here. These identified pre-processing techniques, chosen carefully may give better results for further identification of masses, calcification, architectural distortion, bilateral asymmetry and aid in early detection of breast cancer.
Starting Page	115
Ending Page	119
File Size	499177
Page Count	5
File Format	PDF
e-ISBN	9781479950973
DOI	10.1109/MedCom.2014.7005987
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-07
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms PSNR Anisotropic magnetoresistance Preprocessiong Filtering Image edge detection Mammogram Breast cancer Image enhancemen
Content Type	Text
Resource Type	Article

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