An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms.
Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation a...
| Published in: | Journal of Digital Imaging Vol. 28; no. 5; pp. 613 - 626 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109465612&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109465612 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2015 vid: 28 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109465612 109465612 109465612 10.1007/s10278-015-9778-4 NLM25776767 PMC4570896 109465612 ppf: 613 ppct: 13 formats: fmt: @attributes: type: P tig: atl: An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms. aug: au: Dong, Min Lu, Xiangyu Ma, Yide Guo, Yanan Ma, Yurun Wang, Keju affil: School of Information Science and Engineering, Lanzhou University, No. 222, South Tianshui Road Lanzhou 730000 People's Republic of China sug: subj: Mammography Breast Neoplasms Diagnosis Diagnosis, Computer Assisted Radiographic Image Interpretation, Computer-Assisted Mammography Classification Radiographic Image Enhancement Algorithms Decision Trees Evaluation Research Sensitivity and Specificity Predictive Value of Tests ROC Curve Human ab: Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew's correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73 %, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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