NDLI: Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique

Content Provider	Springer Nature Link
Author	Vikhe, P. S. Thool, V. R.
Copyright Year	2016
Abstract	Detection of mass in mammogram for early diagnosis of breast cancer is a significant assignment in the reduction of the mortality rate. However, in some cases, screening of mass is difficult task for radiologist, due to variation in contrast, fuzzy edges and noisy mammograms. Masses and micro-calcifications are the distinctive signs for diagnosis of breast cancer. This paper presents, a method for mass enhancement using piecewise linear operator in combination with wavelet processing from mammographic images. The method includes, artifact suppression and pectoral muscle removal based on morphological operations. Finally, mass segmentation for detection using adaptive threshold technique is carried out to separate the mass from background. The proposed method has been tested on 130 (45 + 85) images with 90.9 and 91 % True Positive Fraction (TPF) at 2.35 and 2.1 average False Positive Per Image(FP/I) from two different databases, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). The obtained results show that, the proposed technique gives improved diagnosis in the early breast cancer detection.
Starting Page	1
Ending Page	16
Page Count	16
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	40
Issue Number	4
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2016-01-26
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Pectoral muscle Mass Wavelet transform Enhancement Adaptive thresholding Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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