Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature.
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...
| Publicado en: | Journal of Digital Imaging Vol. 25; no. 3; pp. 387 - 400 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104564880&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104564880 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2012 vid: 25 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104564880 75063748 10.1007/s10278-011-9421-y NLM22006275 104564880 ppf: 387 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature. aug: au: Chakraborty, Jayasree Mukhopadhyay, Sudipta Singla, Veenu Khandelwal, Niranjan Bhattacharyya, Pinakpani affil: Computer Vision Lab, Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302 India sug: subj: Mammography Pectoralis Muscles Breast Neoplasms Diagnosis Radiographic Image Enhancement Methods Breast Radiography False Positive Results False Negative Results Algorithms Evaluation Research Human ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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