Early Detection of Skin Cancer Using Melanoma Segmentation technique.
The significance of pattern recognition techniques is widely enhanced in image processing and medical applications. Thus, lesion segmentation method is an essential technique of pattern recognition algorithms to detect the melanoma skin cancer in patients at earliest stage, otherwise, in further sta...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 7 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2019
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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=137182939&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137182939 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2019 vid: 43 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137182939 137182939 137182939 10.1007/s10916-019-1334-1 137182939 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Early Detection of Skin Cancer Using Melanoma Segmentation technique. aug: au: Sreelatha, Tammineni Subramanyam, M. V. Prasad, M. N. Giri affil: Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Ananthapuramu, Ananthapuramu, Andhra Pradesh, India sug: subj: Early Detection of Cancer Skin Neoplasms Diagnosis Melanoma Diagnosis Image Processing, Computer Assisted Methods Diagnosis, Computer Assisted Automation Algorithms Signal Processing, Computer Assisted Computer Simulation ab: The significance of pattern recognition techniques is widely enhanced in image processing and medical applications. Thus, lesion segmentation method is an essential technique of pattern recognition algorithms to detect the melanoma skin cancer in patients at earliest stage, otherwise, in further stages it becomes one of the deadliest disease and its mortality rate is very high. Therefore, a precise melanoma segmentation technique is introduced based on the Gradient and Feature Adaptive Contour (GFAC) model to detect melanoma skin cancer in earliest stage and diagnosis of dermoscopic images. In the proposed image segmentation technique pre-processing and noise elimination techniques are introduced to decrease noise and make execution faster. This technique helps in separating the required entity from the background and gather the information from the adjacent pixels of similar classes. Multiple Gaussian distributed patterns are adopted to extract efficient features and to get precise segmentation. The proposed GFACmodel is noise free and consist of smoother border. The segmentation model efficiency is tested on PH2 dataset. The superiority of the proposed modified gradient and feature adaptive contour model can be verified against various state-of-art-techniques in terms of segmented image, error reduction and efficient feature extraction. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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