Computer Based Melanocytic and Nevus Image Enhancement and Segmentation.
Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the mos...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
9/28/2016
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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=118405767&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118405767 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/28/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 118405767 118405767 118405767 10.1155/2016/2082589 118405767 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Computer Based Melanocytic and Nevus Image Enhancement and Segmentation. aug: au: Jamil, Uzma Akram, M. Usman Khalid, Shehzad Abbas, Sarmad Saleem, Kashif affil: Department of Computer Engineering, Bahria University, Islamabad, Pakistan sug: subj: Melanoma Prevention and Control Melanoma Diagnosis Nevus Diagnosis Diagnosis, Computer Assisted Image Enhancement Microscopy Methods Early Diagnosis Melanoma Classification Nevus Classification Automation Human Algorithms Hair Descriptive Statistics Predictive Value of Tests Reference Values Dermatology ab: Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the most critical yet tricky part in computerized lesion detection and classification. The effectiveness and accuracy of lesion classification are critically dependent on the quality of lesion segmentation. In this paper, we have proposed a novel approach that can automatically preprocess the image and then segment the lesion. The system filters unwanted artifacts including hairs, gel, bubbles, and specular reflection. A novel approach is presented using the concept of wavelets for detection and inpainting the hairs present in the cancer images. The contrast of lesion with the skin is enhanced using adaptive sigmoidal function that takes care of the localized intensity distribution within a given lesion’s images. We then present a segmentation approach to precisely segment the lesion from the background. The proposed approach is tested on the European database of dermoscopic images. Results are compared with the competitors to demonstrate the superiority of the suggested approach. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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