Detection of Glomerulosclerosis in Diabetic Nephropathy Using Contour-based Segmentation Article Swipe
Manoj Srinivas Ravi
,
Ravindra S. Hegadi
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2015.03.129
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2015.03.129
We have proposed a method for detection of glomerulosclerosis in diabetic nephropathy using Contour-Based Segmentation. Pathological images of the glomerulosclerosis were acquired from various patients. It is a challenging task as 100% detection of Nephropathy disease with regular pathological procedure is not possible. We propose a solution to the problem of segmentation of the glomerulosclerosis images for the analysis of abnormalities. The proposed method is the modification of the original Chan-Vese algorithm, with the varied values of parameters. The proposed method achieved better segmentation and the results are encouraging.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2015.03.129
- OA Status
- diamond
- Cited By
- 6
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2011743551
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2011743551Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.procs.2015.03.129Digital Object Identifier
- Title
-
Detection of Glomerulosclerosis in Diabetic Nephropathy Using Contour-based SegmentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-01-01Full publication date if available
- Authors
-
Manoj Srinivas Ravi, Ravindra S. HegadiList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2015.03.129Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.procs.2015.03.129Direct OA link when available
- Concepts
-
Segmentation, Computer science, Diabetic nephropathy, Glomerulosclerosis, Artificial intelligence, Nephropathy, Pattern recognition (psychology), Pathological, Image segmentation, Medicine, Diabetes mellitus, Internal medicine, Proteinuria, Kidney, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2020: 1, 2019: 4, 2018: 1Per-year citation counts (last 5 years)
- References (count)
-
12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2011743551 |
|---|---|
| doi | https://doi.org/10.1016/j.procs.2015.03.129 |
| ids.doi | https://doi.org/10.1016/j.procs.2015.03.129 |
| ids.mag | 2011743551 |
| ids.openalex | https://openalex.org/W2011743551 |
| fwci | 0.20873662 |
| type | article |
| title | Detection of Glomerulosclerosis in Diabetic Nephropathy Using Contour-based Segmentation |
| biblio.issue | |
| biblio.volume | 45 |
| biblio.last_page | 249 |
| biblio.first_page | 244 |
| topics[0].id | https://openalex.org/T10052 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9961000084877014 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Medical Image Segmentation Techniques |
| topics[1].id | https://openalex.org/T11438 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.973800003528595 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | Retinal Imaging and Analysis |
| topics[2].id | https://openalex.org/T10862 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9545999765396118 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | AI in cancer detection |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C89600930 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7981486916542053 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[0].display_name | Segmentation |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7506421804428101 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2779922275 |
| concepts[2].level | 3 |
| concepts[2].score | 0.7081958055496216 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1129105 |
| concepts[2].display_name | Diabetic nephropathy |
| concepts[3].id | https://openalex.org/C2777390665 |
| concepts[3].level | 4 |
| concepts[3].score | 0.613652229309082 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q5571239 |
| concepts[3].display_name | Glomerulosclerosis |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5408542156219482 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C2781184683 |
| concepts[5].level | 3 |
| concepts[5].score | 0.4790973961353302 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1054718 |
| concepts[5].display_name | Nephropathy |
| concepts[6].id | https://openalex.org/C153180895 |
| concepts[6].level | 2 |
| concepts[6].score | 0.46961528062820435 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[6].display_name | Pattern recognition (psychology) |
| concepts[7].id | https://openalex.org/C207886595 |
| concepts[7].level | 2 |
| concepts[7].score | 0.44985732436180115 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1456138 |
| concepts[7].display_name | Pathological |
| concepts[8].id | https://openalex.org/C124504099 |
| concepts[8].level | 3 |
| concepts[8].score | 0.4120587706565857 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q56933 |
| concepts[8].display_name | Image segmentation |
| concepts[9].id | https://openalex.org/C71924100 |
| concepts[9].level | 0 |
| concepts[9].score | 0.30446624755859375 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[9].display_name | Medicine |
| concepts[10].id | https://openalex.org/C555293320 |
| concepts[10].level | 2 |
| concepts[10].score | 0.24035558104515076 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q12206 |
| concepts[10].display_name | Diabetes mellitus |
| concepts[11].id | https://openalex.org/C126322002 |
| concepts[11].level | 1 |
| concepts[11].score | 0.14619624614715576 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[11].display_name | Internal medicine |
| concepts[12].id | https://openalex.org/C2779561371 |
| concepts[12].level | 3 |
| concepts[12].score | 0.1173449456691742 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q570197 |
| concepts[12].display_name | Proteinuria |
| concepts[13].id | https://openalex.org/C2780091579 |
| concepts[13].level | 2 |
| concepts[13].score | 0.060907840728759766 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q9377 |
| concepts[13].display_name | Kidney |
| concepts[14].id | https://openalex.org/C134018914 |
| concepts[14].level | 1 |
| concepts[14].score | 0.05023527145385742 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q162606 |
| concepts[14].display_name | Endocrinology |
| keywords[0].id | https://openalex.org/keywords/segmentation |
| keywords[0].score | 0.7981486916542053 |
| keywords[0].display_name | Segmentation |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7506421804428101 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/diabetic-nephropathy |
| keywords[2].score | 0.7081958055496216 |
| keywords[2].display_name | Diabetic nephropathy |
| keywords[3].id | https://openalex.org/keywords/glomerulosclerosis |
| keywords[3].score | 0.613652229309082 |
| keywords[3].display_name | Glomerulosclerosis |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5408542156219482 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/nephropathy |
| keywords[5].score | 0.4790973961353302 |
| keywords[5].display_name | Nephropathy |
| keywords[6].id | https://openalex.org/keywords/pattern-recognition |
| keywords[6].score | 0.46961528062820435 |
| keywords[6].display_name | Pattern recognition (psychology) |
| keywords[7].id | https://openalex.org/keywords/pathological |
| keywords[7].score | 0.44985732436180115 |
| keywords[7].display_name | Pathological |
| keywords[8].id | https://openalex.org/keywords/image-segmentation |
| keywords[8].score | 0.4120587706565857 |
| keywords[8].display_name | Image segmentation |
| keywords[9].id | https://openalex.org/keywords/medicine |
| keywords[9].score | 0.30446624755859375 |
| keywords[9].display_name | Medicine |
| keywords[10].id | https://openalex.org/keywords/diabetes-mellitus |
| keywords[10].score | 0.24035558104515076 |
| keywords[10].display_name | Diabetes mellitus |
| keywords[11].id | https://openalex.org/keywords/internal-medicine |
| keywords[11].score | 0.14619624614715576 |
| keywords[11].display_name | Internal medicine |
| keywords[12].id | https://openalex.org/keywords/proteinuria |
| keywords[12].score | 0.1173449456691742 |
| keywords[12].display_name | Proteinuria |
| keywords[13].id | https://openalex.org/keywords/kidney |
| keywords[13].score | 0.060907840728759766 |
| keywords[13].display_name | Kidney |
| keywords[14].id | https://openalex.org/keywords/endocrinology |
| keywords[14].score | 0.05023527145385742 |
| keywords[14].display_name | Endocrinology |
| language | en |
| locations[0].id | doi:10.1016/j.procs.2015.03.129 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S120348307 |
| locations[0].source.issn | 1877-0509 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1877-0509 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Procedia Computer Science |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Procedia Computer Science |
| locations[0].landing_page_url | https://doi.org/10.1016/j.procs.2015.03.129 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5090351270 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Manoj Srinivas Ravi |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I111575329 |
| authorships[0].affiliations[0].raw_affiliation_string | Research Scholar, Research and Development Centre, Bharathiar University, Coimbatore - 641 046, India |
| authorships[0].institutions[0].id | https://openalex.org/I111575329 |
| authorships[0].institutions[0].ror | https://ror.org/04fht8c22 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I111575329 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Bharathiar University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | M. Ravi |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Research Scholar, Research and Development Centre, Bharathiar University, Coimbatore - 641 046, India |
| authorships[1].author.id | https://openalex.org/A5070470875 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5176-6490 |
| authorships[1].author.display_name | Ravindra S. Hegadi |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I155907036 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Science, Solapur University, Solapur 413255, Maharashtra, India |
| authorships[1].institutions[0].id | https://openalex.org/I155907036 |
| authorships[1].institutions[0].ror | https://ror.org/026tktq31 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I155907036 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Solapur University |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Ravindra S. Hegadi |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Science, Solapur University, Solapur 413255, Maharashtra, India |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.procs.2015.03.129 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Detection of Glomerulosclerosis in Diabetic Nephropathy Using Contour-based Segmentation |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10052 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9961000084877014 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Medical Image Segmentation Techniques |
| related_works | https://openalex.org/W2353211184, https://openalex.org/W4385971958, https://openalex.org/W1974938188, https://openalex.org/W2349667568, https://openalex.org/W1971350538, https://openalex.org/W1522196789, https://openalex.org/W2959730349, https://openalex.org/W2471926966, https://openalex.org/W2325756515, https://openalex.org/W297605000 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2020 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2019 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2018 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1016/j.procs.2015.03.129 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S120348307 |
| best_oa_location.source.issn | 1877-0509 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1877-0509 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Procedia Computer Science |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Procedia Computer Science |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.procs.2015.03.129 |
| primary_location.id | doi:10.1016/j.procs.2015.03.129 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S120348307 |
| primary_location.source.issn | 1877-0509 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1877-0509 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Procedia Computer Science |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Procedia Computer Science |
| primary_location.landing_page_url | https://doi.org/10.1016/j.procs.2015.03.129 |
| publication_date | 2015-01-01 |
| publication_year | 2015 |
| referenced_works | https://openalex.org/W2116040950, https://openalex.org/W2114487471, https://openalex.org/W2026356722, https://openalex.org/W2012118748, https://openalex.org/W1569930776, https://openalex.org/W1987579928, https://openalex.org/W4234235766, https://openalex.org/W1999244633, https://openalex.org/W2123424214, https://openalex.org/W2622520782, https://openalex.org/W2294819727, https://openalex.org/W1523554224 |
| referenced_works_count | 12 |
| abstract_inverted_index.a | 3, 27, 45 |
| abstract_inverted_index.It | 25 |
| abstract_inverted_index.We | 0, 43 |
| abstract_inverted_index.as | 30 |
| abstract_inverted_index.in | 9 |
| abstract_inverted_index.is | 26, 40, 64 |
| abstract_inverted_index.of | 7, 17, 33, 50, 52, 59, 67, 76 |
| abstract_inverted_index.to | 47 |
| abstract_inverted_index.The | 61, 78 |
| abstract_inverted_index.and | 84 |
| abstract_inverted_index.are | 87 |
| abstract_inverted_index.for | 5, 56 |
| abstract_inverted_index.not | 41 |
| abstract_inverted_index.the | 18, 48, 53, 57, 65, 68, 73, 85 |
| abstract_inverted_index.100% | 31 |
| abstract_inverted_index.from | 22 |
| abstract_inverted_index.have | 1 |
| abstract_inverted_index.task | 29 |
| abstract_inverted_index.were | 20 |
| abstract_inverted_index.with | 36, 72 |
| abstract_inverted_index.using | 12 |
| abstract_inverted_index.better | 82 |
| abstract_inverted_index.images | 16, 55 |
| abstract_inverted_index.method | 4, 63, 80 |
| abstract_inverted_index.values | 75 |
| abstract_inverted_index.varied | 74 |
| abstract_inverted_index.disease | 35 |
| abstract_inverted_index.problem | 49 |
| abstract_inverted_index.propose | 44 |
| abstract_inverted_index.regular | 37 |
| abstract_inverted_index.results | 86 |
| abstract_inverted_index.various | 23 |
| abstract_inverted_index.achieved | 81 |
| abstract_inverted_index.acquired | 21 |
| abstract_inverted_index.analysis | 58 |
| abstract_inverted_index.diabetic | 10 |
| abstract_inverted_index.original | 69 |
| abstract_inverted_index.proposed | 2, 62, 79 |
| abstract_inverted_index.solution | 46 |
| abstract_inverted_index.Chan-Vese | 70 |
| abstract_inverted_index.detection | 6, 32 |
| abstract_inverted_index.patients. | 24 |
| abstract_inverted_index.possible. | 42 |
| abstract_inverted_index.procedure | 39 |
| abstract_inverted_index.algorithm, | 71 |
| abstract_inverted_index.Nephropathy | 34 |
| abstract_inverted_index.challenging | 28 |
| abstract_inverted_index.nephropathy | 11 |
| abstract_inverted_index.parameters. | 77 |
| abstract_inverted_index.Pathological | 15 |
| abstract_inverted_index.encouraging. | 88 |
| abstract_inverted_index.modification | 66 |
| abstract_inverted_index.pathological | 38 |
| abstract_inverted_index.segmentation | 51, 83 |
| abstract_inverted_index.Contour-Based | 13 |
| abstract_inverted_index.Segmentation. | 14 |
| abstract_inverted_index.abnormalities. | 60 |
| abstract_inverted_index.glomerulosclerosis | 8, 19, 54 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.59325829 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |