Skin Cancer Image Segmentation Based on Midpoint Analysis Approach.
Skin cancer affects people of all ages and is a common disease. The death toll from skin cancer rises with a late diagnosis. An automated mechanism for early-stage skin cancer detection is required to diminish the mortality rate. Visual examination with scanning or imaging screening is a common mech...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2581 - 2597 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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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=181515410&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515410 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515410 181515410 181515410 10.1007/s10278-024-01106-w 181515410 ppf: 2581 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Skin Cancer Image Segmentation Based on Midpoint Analysis Approach. aug: au: Saghir, Uzma Singh, Shailendra Kumar Hasan, Moin affil: https://ror.org/00et6q107 Dept. of Computer Science & Engineering, Lovely Professional University, 144001, Punjab, India sug: subj: Skin Neoplasms Pathology Skin Neoplasms Diagnosis Dermoscopy Image Enhancement Image Processing, Computer Assisted Hair Algorithms Deep Learning Skin Pathology Melanoma Microscopy Neural Networks (Computer) Skin Neoplasms Classification Conceptual Framework ab: Skin cancer affects people of all ages and is a common disease. The death toll from skin cancer rises with a late diagnosis. An automated mechanism for early-stage skin cancer detection is required to diminish the mortality rate. Visual examination with scanning or imaging screening is a common mechanism for detecting this disease, but due to its similarity to other diseases, this mechanism shows the least accuracy. This article introduces an innovative segmentation mechanism that operates on the ISIC dataset to divide skin images into critical and non-critical sections. The main objective of the research is to segment lesions from dermoscopic skin images. The suggested framework is completed in two steps. The first step is to pre-process the image; for this, we have applied a bottom hat filter for hair removal and image enhancement by applying DCT and color coefficient. In the next phase, a background subtraction method with midpoint analysis is applied for segmentation to extract the region of interest and achieves an accuracy of 95.30%. The ground truth for the validation of segmentation is accomplished by comparing the segmented images with validation data provided with the ISIC dataset. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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