An Efficient Method for Automated Breast Mass Segmentation and Classification in Digital Mammograms.
Background: Automatic detectionandclassification of breast masses inmammogramsare still challenging tasks. Today, computeraided diagnosis (CAD) systems are being developed to assist radiologists in interpreting mammograms. Objectives: This study aimed to provide a novel method for automatic segmenta...
| Publicado en: | Iranian Journal of Radiology Vol. 18; no. 3; pp. 1 - 13 |
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| Autores principales: | , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Tehran University of Medical Sciences
Jul2021
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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=153439816&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153439816 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17351065 54ZL jtl: Iranian Journal of Radiology issn: 17351065 maglogo: N pubinfo: dt: Jul2021 vid: 18 iid: 3 pid: 21783 pub: Tehran University of Medical Sciences artinfo: ui: 153439816 153439816 153439816 10.5812/iranjradiol.106717 153439816 ppf: 1 ppct: 12 formats: tig: atl: An Efficient Method for Automated Breast Mass Segmentation and Classification in Digital Mammograms. aug: au: Fam, Behrouz Niroomand Nikravanshalmani, Alireza Khalilian, Madjid affil: Department of Computer, Karaj Branch, Islamic Azad University, Karaj, Iran sug: subj: Image Interpretation, Computer Assisted Breast Neoplasms Classification Mammography Digital Imaging Breast Neoplasms Diagnosis Human Radiologists Algorithms Image Processing, Computer Assisted Automation ab: Background: Automatic detectionandclassification of breast masses inmammogramsare still challenging tasks. Today, computeraided diagnosis (CAD) systems are being developed to assist radiologists in interpreting mammograms. Objectives: This study aimed to provide a novel method for automatic segmentation and classification of masses in mammograms to help radiologists make an accurate diagnosis. MaterialsandMethods: Foranefficientmassdiagnosis inmammograms,weproposedanautomaticschemeto perform bothmass detection and classification. First, a combination of several image enhancement algorithms, including contrast-limited adaptive histogram equalization (CLAHE), guided imaging, and median filtering, was investigated to enhance the visual features of breast area and increase the accuracy of segmentation outcomes. Second, the density of discrete wavelet coefficient density (DDWCs), based on the quincunx lifting scheme (QLS), was proposed to find suspicious mass regions or regions of interest (ROIs). Finally, mass lesions that appeared in the mammogram were classified into four categories of benign, probably benign, malignant, and probably malignant, based on the morphological shape. The proposed method was evaluated among 1593 images from the Curated Breast Imaging Subset-Digital Database for Screening Mammography (CBIS-DDSM) dataset. Results: The experimental results revealed that the suspected region localization had 100% sensitivity, with amean of 6.4-4.5 false positive (FP) detections per image. Moreover, the results showed an overall accuracy of 85.9% and an area under the curve (AUC) of 0.901 for the mass classification algorithm. Conclusion: The present results showed the comparable performance of our proposed method to that of the state-of-the-art methods. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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