Data · dataset · 2020
Graph weighting scheme for lesion segmentation in macroscopic images
Listed in Teesside University Research Data Repository
We present the results obtained by our graph weighting scheme based approach for skin lesion segmentation in macroscopic images on two datasets used by amelard et al. (Amelard, J. Glaister, A. Wong and D. A. Clausi, High-Level Intuitive Features (HLIFs) for Intuitive Skin Lesion Description, IEEE Trans.
Description
Biomed. Eng., 62(3) (2015), 820-831).
The first is a subset of a DermIS database (Dermatology Information System, available online: dermis.net) that contains 43 photographs with lesion diagnosed as melanoma and 26 diagnosed as non melanoma. The second is a subset of the DermQuest database (Available online: dermquest.com) that contains 76 images of melanoma lesions and 61 images of non melanoma lesions. The images are subject to various artifacts like a drastic shadow effect, the variation of illumination and poor contrast between the lesion and skin parts.
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We also provide the ground truth segmentation to evaluate visually the accuracy of our results.
Links
Where it is published
- DOI doi.org/10.17632/xs7yr2jwz7.2 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Biomedical engineering · Computer Science & AI · Earth & Environmental Science · Engineering
- Inferred from text
- Image 65%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/xs7yr2jwz7.2 | 15 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4003 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Biomedical Engineering'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |