Hybrid segmentation of mass in mammograms using template matching and dynamic programming.
Rationale and Objectives: Accurate image segmentation for breast lesions is a critical step in computer-aided diagnosis systems. The objective of this study was to develop a robust method for the automatic segmentation of breast masses on mammograms to extract feasible features for computer-aided di...
| Publicado en: | Academic Radiology Vol. 17; no. 11; pp. 1414 - 1425 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Nov2010
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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=104929266&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104929266 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10766332 T4X jtl: Academic Radiology issn: 10766332 maglogo: N pubinfo: dt: Nov2010 vid: 17 iid: 11 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 104929266 NLM20817575 2010825583 10.1016/j.acra.2010.07.008 NLM20817575 104929266 ppf: 1414 ppct: 11 formats: tig: atl: Hybrid segmentation of mass in mammograms using template matching and dynamic programming. aug: au: Song E Xu S Xu X Zeng J Lan Y Zhang S Hung CC Song, Enmin Xu, Shengzhou Xu, Xiangyang Zeng, Jianye Lan, Yihua Zhang, Shenyi Hung, Chih-Cheng affil: School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China sug: subj: Algorithms Breast Neoplasms Radiography Mammography Methods Information Science Methods Radiographic Image Interpretation, Computer-Assisted Methods Subtraction Technique Female Human Radiographic Image Enhancement Methods Reproducibility of Results Sensitivity and Specificity Female ab: Rationale and Objectives: Accurate image segmentation for breast lesions is a critical step in computer-aided diagnosis systems. The objective of this study was to develop a robust method for the automatic segmentation of breast masses on mammograms to extract feasible features for computer-aided diagnosis systems. Materials and Methods: The data set used in this study consisted of 483 regions of interest extracted from 328 patients. A hybrid method for segmenting breast masses was proposed on the basis of the template-matching and dynamic programming techniques. First, a template-matching technique was used to locate and obtain the rough region of masses. Then, on the basis of this rough region, a local cost function for dynamic programming was defined. Finally, the optimal contour was derived by applying dynamic programming as an optimization technique. The performance of this proposed segmentation method was evaluated using area-based and boundary distance-based similarity measures based on radiologists' manually marked annotations. A comparison with three different segmentation algorithms on the data set was provided. Results: The mean overlap percentage for our proposed hybrid method was 0.727 ± 0.127, whereas those for Timp and Karssemeijer's dynamic programming method, Song et al's plane-fitting and dynamic programming method, and the normalized cut segmentation method were 0.657 ± 0.216, 0.636 ± 0.190, and 0.562 ± 0.199, respectively. All P values for the measure distribution of our proposed method and the other three algorithms were <.001. Conclusions: A hybrid method based on the template-matching and dynamic programming techniques was proposed to segment breast masses on mammograms. Evaluation results indicate that the proposed segmentation method can improve the accuracy of mass segmentation compared to three other algorithms. The proposed segmentation method shows better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in interpreting mammograms. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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