Hair detection and lesion segmentation in dermoscopic images using domain knowledge.
Automated segmentation and dermoscopic hair detection are one of the significant challenges in computer-aided diagnosis (CAD) of melanocytic lesions. Additionally, due to the presence of artifacts and variation in skin texture and smooth lesion boundaries, the accuracy of such methods gets hampered....
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 11; pp. 2051 - 2066 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2018
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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=132461121&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132461121 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2018 vid: 56 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132461121 132461121 NLM29761315 10.1007/s11517-018-1837-9 NLM29761315 132461121 ppf: 2051 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Hair detection and lesion segmentation in dermoscopic images using domain knowledge. aug: au: Pathan, Sameena Prabhu, K. Gopalakrishna Siddalingaswamy, P. C. affil: Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal-576104, MAHE, India sug: subj: Microscopy Methods Diagnosis, Computer Assisted Methods Hair Skin Neoplasms Melanoma Image Interpretation, Computer Assisted Methods Algorithms Artifacts Sensitivity and Specificity Information Science Methods Ways of Coping Questionnaire ab: Automated segmentation and dermoscopic hair detection are one of the significant challenges in computer-aided diagnosis (CAD) of melanocytic lesions. Additionally, due to the presence of artifacts and variation in skin texture and smooth lesion boundaries, the accuracy of such methods gets hampered. The objective of this research is to develop an automated hair detection and lesion segmentation algorithm using lesion-specific properties to improve the accuracy. The aforementioned objective is achieved in two ways. Firstly, a novel hair detection algorithm is designed by considering the properties of dermoscopic hair. Second, a novel chroma-based geometric deformable model is used to effectively differentiate the lesion from the surrounding skin. The speed function incorporates the chrominance properties of the lesion to stop evolution at the lesion boundary. Automatic initialization of the initial contour and chrominance-based speed function aids in providing robust and flexible segmentation. The proposed approach is tested on 200 images from PH2 and 900 images from ISBI 2016 datasets. Average accuracy, sensitivity, specificity, and overlap scores of 93.4, 87.6, 95.3, and 11.52% respectively are obtained for the PH2 dataset. Similarly, the proposed method resulted in average accuracy, sensitivity, specificity, and overlap scores of 94.6, 82.4, 97.2, and 7.20% respectively for the ISBI 2016 dataset. Statistical and quantitative analyses prove the reliability of the algorithm for incorporation in CAD systems. Graphical Abstract Overview of proposed system. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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