10.46243/jstj.2018.v3.i6.143 registered
Log-Gaussian Fuzzy C-Means Clustering Algorithm for Skin Lesion Segmentation
Resolves to https://www.jst.org.in/index.php/pub/article/view/336
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jstj.2018.v3.i6.143
Registered 29 Sep 2026 via crossref · record version 2 · last change 29 Sep 2026, 11:59 PM · record sha256 bb107c936b79ef06…
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JournalArticle — an article in a journal · Digital · Visual · en
Log-Gaussian Fuzzy C-Means Clustering Algorithm for Skin Lesion Segmentation (PrincipalTitle)
Published 2018
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 03 · issue 06 · pages 18–27
Agents
- Ch. Kranthi Rekha (author)
- K. Manjunathachari (author)
- B.L. Prakash (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jstj.2018.v3.i6.143
Abstract
n most recent applications of image analysis, the difficulty faced is to detect proper structure of an irregularly shaped object. This is mostly seen in the applications of medical field such as skin lesion segmentation. It is also a critical task to determine the exact border line of the lesion. Also early detection of skin cancer is an essential problem in the recent years of development in image processing. There are many types of skin lesion appearances. Some of them are blurred, some are irregular in shape, some are on dark skin, and some are seen with lots of hair on the skin. The main aim of this research paper is to detect the malignant region of skin lesion, identify its stage, and segment it out from the skin image. During the process, it is essential to preprocess the input image by performing conversion of a color image to gray scale image, removal of blur, removal of noise, smoothing of images, etc. It also involves grouping the similar pixels into one cluster, likewise obtaining various clusters based on similarities. Then it is essential to perform the extraction of features of the lesion and displaying the segmented lesion. In the current research a novel approach is developed to obtain the clusters of any shape and is tested on skin lesion images for detecting the cancer cells. Performance of developed clustering algorithm is tested by measuring various parameters based on distance metric and few similarity indexes. The proposed method is also compared to other approaches that are developedearlier.
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| Element | Value | In the record |
|---|---|---|
| DOI Name DOI name | 10.46243/jstj.2018.v3.i6.143 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | Log-Gaussian Fuzzy C-Means Clustering Algorithm for Skin Lesion Segmentation (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: Ch. Kranthi Rekha author: K. Manjunathachari author: B.L. Prakash publisher: Longman Publishers published: 2018 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 03 · no. 06 · pp. 18–27 language: en form: Digital · Visual · Language | agents, dates, container, language, structural_type, modes, characters |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI | identifiers, relations (IsSameAs) |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy | record.source_agency (our code, ra_doi_name, for names issued here once appointed) |
| Created Date issueDate | 2026-09-07 | record.registered (when the DOI name was first registered) |
| relatedIdentifiers | none needed — the descriptive metadata is in this record | container, relations (only where the descriptive metadata lives at another identifier) |
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| # | When | What | By | Changes |
|---|---|---|---|---|
| 1 | 29 Sep 2026, 10:00 PM | register registered at Crossref; record read from api.crossref.org | Administrator (admin) | 72 fields set · sha256 72c34baa6b1c… |
| 2 | 29 Sep 2026, 11:59 PM | update record re-read from api.crossref.org | Administrator (admin) | container.titles.0.value: |
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