Pectoral Muscle Detection in Mammograms Using Local Statistical Features.
Mammography is a primary imaging method for breast cancer diagnosis. It is an important issue to accurately identify and separate pectoral muscles (PM) from breast tissues. Hough-transform-based methods are commonly adopted for PM detection. But their performances are susceptible when PM edges canno...
| Publicado en: | Journal of Digital Imaging Vol. 27; no. 5; pp. 633 - 642 |
|---|---|
| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2014
|
| 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=103894384&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103894384 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2014 vid: 27 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103894384 98419620 10.1007/s10278-014-9676-1 NLM24482043 PMC4171434 103894384 ppf: 633 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Pectoral Muscle Detection in Mammograms Using Local Statistical Features. aug: au: Liu, Li Liu, Qian Lu, Wei affil: School of Electronic Information Engineering, Tianjin University, Tianjin 300072 China sug: subj: Mammography Pectoralis Muscles Radiographic Image Enhancement Methods Algorithms Evaluation Evaluation Research Interrater Reliability Intrarater Reliability Human ab: Mammography is a primary imaging method for breast cancer diagnosis. It is an important issue to accurately identify and separate pectoral muscles (PM) from breast tissues. Hough-transform-based methods are commonly adopted for PM detection. But their performances are susceptible when PM edges cannot be depicted by straight lines. In this study, we present a new pectoral muscle identification algorithm which utilizes statistical features of pixel responses. First, the Anderson-Darling goodness-of-fit test is used to extract a feature image by assuming non-Gaussianity for PM boundaries. Second, a global weighting scheme based on the location of PM was applied onto the feature image to suppress non-PM regions. From the weighted image, a preliminary set of pectoral muscles boundary components is detected via row-wise peak detection. An iterative procedure based on the edge continuity and orientation is used to determine the final PM boundary. Our results on a public mammogram database were assessed using four performance metrics: the false positive rate, the false negative rate, the Hausdorff distance, and the average distance. Compared to previous studies, our method demonstrates the state-of-art performance in terms of four measures. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|