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

  • Transactional Processing Systems
  • Published:
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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.

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Acknowledgments

The authors would like to thank Dr. Sushil Kachewar, Associate Professor, Dr. Sham, Assistant Professor and there team, Department of Radiodiagnosis and Imaging of Rural Medical College, Pravara Institute of Medical Science (PIMS), Loni (Deemed University), for providing the information about pectoral muscle, breast cancer and ground truth for MIAS and DDSM databases.

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  1. Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India

    P. S. Vikhe & V. R. Thool

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  1. P. S. Vikhe
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Correspondence to P. S. Vikhe.

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This article is part of the Topical Collection on Transactional Processing Systems

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Vikhe, P.S., Thool, V.R. Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique. J Med Syst 40, 82 (2016). https://doi.org/10.1007/s10916-016-0435-3

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