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Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature

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Abstract

In medio-lateral oblique view of mammogram, pectoral muscle may sometimes affect the detection of breast cancer due to their similar characteristics with abnormal tissues. As a result pectoral muscle should be handled separately while detecting the breast cancer. In this paper, a novel approach for the detection of pectoral muscle using average gradient- and shape-based feature is proposed. The process first approximates the pectoral muscle boundary as a straight line using average gradient-, position-, and shape-based features of the pectoral muscle. Straight line is then tuned to a smooth curve which represents the pectoral margin more accurately. Finally, an enclosed region is generated which represents the pectoral muscle as a segmentation mask. The main advantage of the method is its’ simplicity as well as accuracy. The method is applied on 200 mammographic images consisting 80 randomly selected scanned film images from Mammographic Image Analysis Society (mini-MIAS) database, 80 direct radiography (DR) images, and 40 computed radiography (CR) images from local database. The performance is evaluated based upon the false positive (FP), false negative (FN) pixel percentage, and mean distance closest point (MDCP). Taking all the images into consideration, the average FP and FN pixel percentages are 4.22%, 3.93%, 18.81%, and 6.71%, 6.28%, 5.12% for mini-MIAS, DR, and CR images, respectively. Obtained MDCP values for the same set of database are 3.34, 3.33, and 10.41 respectively. The method is also compared with two well-known pectoral muscle detection techniques and in most of the cases, it outperforms the other two approaches.

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Authors and Affiliations

  1. Computer Vision Lab, Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, West Bengal, 721302, India

    Jayasree Chakraborty & Sudipta Mukhopadhyay

  2. Department of Radiodiagnosis, Postgraduate Institute of Medical Education and Research, Chandigarh, Chandigarh, 160 012, India

    Veenu Singla & Niranjan Khandelwal

  3. Department of Radiology, Quadra Medical Services Pvt. Ltd. Kolkata, West Bengal, 700019, India

    Pinakpani Bhattacharyya

Authors
  1. Jayasree Chakraborty
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  2. Sudipta Mukhopadhyay
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  3. Veenu Singla
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  4. Niranjan Khandelwal
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  5. Pinakpani Bhattacharyya
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Correspondence to Jayasree Chakraborty.

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Chakraborty, J., Mukhopadhyay, S., Singla, V. et al. Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature. J Digit Imaging 25, 387–399 (2012). https://doi.org/10.1007/s10278-011-9421-y

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  • DOI: https://doi.org/10.1007/s10278-011-9421-y

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