An efficient multi-level thresholding method for breast thermograms analysis based on an improved BWO algorithm Article Swipe
Simrandeep Singh
,
Harbinder Singh
,
Nitin Mittal
,
Supreet Singh
,
Sameh Askar
,
Ahmad M. Alshamrani
,
Mohamed Abouhawwash
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12880-024-01361-x
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12880-024-01361-x
Breast cancer is a prevalent disease and the second leading cause of death in women globally. Various imaging techniques, including mammography, ultrasonography, X-ray, and magnetic resonance, are employed for detection. Thermography shows significant promise for early breast disease detection, offering advantages such as being non-ionizing, non-invasive, cost-effective, and providing real-time results. Medical image segmentation is crucial in image analysis, and this study introduces a thermographic image segmentation algorithm using the improved Black Widow Optimization Algorithm (IBWOA). While the standard BWOA is effective for complex optimization problems, it has issues with stagnation and balancing exploration and exploitation. The proposed method enhances exploration with Levy flights and improves exploitation with quasi-opposition-based learning. Comparing IBWOA with other algorithms like Harris Hawks Optimization (HHO), Linear Success-History based Adaptive Differential Evolution (LSHADE), and the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and black widow optimization (BWO) using otsu and Kapur's entropy method. Results show IBWOA delivers superior performance in both qualitative and quantitative analyses including visual inspection and metrics such as fitness value, threshold values, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental results demonstrate the outperformance of the proposed IBWOA, validating its effectiveness and superiority.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12880-024-01361-x
- https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-024-01361-x
- OA Status
- gold
- Cited By
- 1
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401115062
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401115062Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s12880-024-01361-xDigital Object Identifier
- Title
-
An efficient multi-level thresholding method for breast thermograms analysis based on an improved BWO algorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-30Full publication date if available
- Authors
-
Simrandeep Singh, Harbinder Singh, Nitin Mittal, Supreet Singh, Sameh Askar, Ahmad M. Alshamrani, Mohamed AbouhawwashList of authors in order
- Landing page
-
https://doi.org/10.1186/s12880-024-01361-xPublisher landing page
- PDF URL
-
https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-024-01361-xDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-024-01361-xDirect OA link when available
- Concepts
-
Computer science, Artificial intelligence, Segmentation, Algorithm, Pattern recognition (psychology), Image segmentation, Thresholding, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
82Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4401115062 |
|---|---|
| doi | https://doi.org/10.1186/s12880-024-01361-x |
| ids.doi | https://doi.org/10.1186/s12880-024-01361-x |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39080591 |
| ids.openalex | https://openalex.org/W4401115062 |
| fwci | 0.81907382 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D000465 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Algorithms |
| mesh[2].qualifier_ui | Q000379 |
| mesh[2].descriptor_ui | D013817 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | methods |
| mesh[2].descriptor_name | Thermography |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D005260 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Female |
| mesh[4].qualifier_ui | Q000000981 |
| mesh[4].descriptor_ui | D001943 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | diagnostic imaging |
| mesh[4].descriptor_name | Breast Neoplasms |
| mesh[5].qualifier_ui | Q000000981 |
| mesh[5].descriptor_ui | D001940 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | diagnostic imaging |
| mesh[5].descriptor_name | Breast |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D006801 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Humans |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D000465 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Algorithms |
| mesh[8].qualifier_ui | Q000379 |
| mesh[8].descriptor_ui | D013817 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | methods |
| mesh[8].descriptor_name | Thermography |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D005260 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Female |
| mesh[10].qualifier_ui | Q000000981 |
| mesh[10].descriptor_ui | D001943 |
| mesh[10].is_major_topic | True |
| mesh[10].qualifier_name | diagnostic imaging |
| mesh[10].descriptor_name | Breast Neoplasms |
| mesh[11].qualifier_ui | Q000000981 |
| mesh[11].descriptor_ui | D001940 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | diagnostic imaging |
| mesh[11].descriptor_name | Breast |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D006801 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Humans |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D000465 |
| mesh[13].is_major_topic | True |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Algorithms |
| mesh[14].qualifier_ui | Q000379 |
| mesh[14].descriptor_ui | D013817 |
| mesh[14].is_major_topic | True |
| mesh[14].qualifier_name | methods |
| mesh[14].descriptor_name | Thermography |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D005260 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Female |
| mesh[16].qualifier_ui | Q000000981 |
| mesh[16].descriptor_ui | D001943 |
| mesh[16].is_major_topic | True |
| mesh[16].qualifier_name | diagnostic imaging |
| mesh[16].descriptor_name | Breast Neoplasms |
| mesh[17].qualifier_ui | Q000000981 |
| mesh[17].descriptor_ui | D001940 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | diagnostic imaging |
| mesh[17].descriptor_name | Breast |
| mesh[18].qualifier_ui | |
| mesh[18].descriptor_ui | D006801 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | |
| mesh[18].descriptor_name | Humans |
| mesh[19].qualifier_ui | |
| mesh[19].descriptor_ui | D000465 |
| mesh[19].is_major_topic | True |
| mesh[19].qualifier_name | |
| mesh[19].descriptor_name | Algorithms |
| mesh[20].qualifier_ui | Q000379 |
| mesh[20].descriptor_ui | D013817 |
| mesh[20].is_major_topic | True |
| mesh[20].qualifier_name | methods |
| mesh[20].descriptor_name | Thermography |
| mesh[21].qualifier_ui | |
| mesh[21].descriptor_ui | D005260 |
| mesh[21].is_major_topic | False |
| mesh[21].qualifier_name | |
| mesh[21].descriptor_name | Female |
| mesh[22].qualifier_ui | Q000000981 |
| mesh[22].descriptor_ui | D001943 |
| mesh[22].is_major_topic | True |
| mesh[22].qualifier_name | diagnostic imaging |
| mesh[22].descriptor_name | Breast Neoplasms |
| mesh[23].qualifier_ui | Q000000981 |
| mesh[23].descriptor_ui | D001940 |
| mesh[23].is_major_topic | False |
| mesh[23].qualifier_name | diagnostic imaging |
| mesh[23].descriptor_name | Breast |
| type | article |
| title | An efficient multi-level thresholding method for breast thermograms analysis based on an improved BWO algorithm |
| awards[0].id | https://openalex.org/G5562180740 |
| awards[0].funder_id | https://openalex.org/F4320321145 |
| awards[0].display_name | |
| awards[0].funder_award_id | RSPD2023R533 |
| awards[0].funder_display_name | King Saud University |
| biblio.issue | 1 |
| biblio.volume | 24 |
| biblio.last_page | 191 |
| biblio.first_page | 191 |
| topics[0].id | https://openalex.org/T12994 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Infrared Thermography in Medicine |
| topics[1].id | https://openalex.org/T11856 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.991100013256073 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2211 |
| topics[1].subfield.display_name | Mechanics of Materials |
| topics[1].display_name | Thermography and Photoacoustic Techniques |
| topics[2].id | https://openalex.org/T12015 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9805999994277954 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2204 |
| topics[2].subfield.display_name | Biomedical Engineering |
| topics[2].display_name | Photoacoustic and Ultrasonic Imaging |
| funders[0].id | https://openalex.org/F4320321145 |
| funders[0].ror | https://ror.org/02f81g417 |
| funders[0].display_name | King Saud University |
| is_xpac | False |
| apc_list.value | 1690 |
| apc_list.currency | GBP |
| apc_list.value_usd | 2072 |
| apc_paid.value | 1690 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 2072 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6530830264091492 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5864696502685547 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C89600930 |
| concepts[2].level | 2 |
| concepts[2].score | 0.549595296382904 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[2].display_name | Segmentation |
| concepts[3].id | https://openalex.org/C11413529 |
| concepts[3].level | 1 |
| concepts[3].score | 0.46444395184516907 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[3].display_name | Algorithm |
| concepts[4].id | https://openalex.org/C153180895 |
| concepts[4].level | 2 |
| concepts[4].score | 0.46369776129722595 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[4].display_name | Pattern recognition (psychology) |
| concepts[5].id | https://openalex.org/C124504099 |
| concepts[5].level | 3 |
| concepts[5].score | 0.43499305844306946 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q56933 |
| concepts[5].display_name | Image segmentation |
| concepts[6].id | https://openalex.org/C191178318 |
| concepts[6].level | 3 |
| concepts[6].score | 0.43477460741996765 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2256906 |
| concepts[6].display_name | Thresholding |
| concepts[7].id | https://openalex.org/C115961682 |
| concepts[7].level | 2 |
| concepts[7].score | 0.19536307454109192 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[7].display_name | Image (mathematics) |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6530830264091492 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.5864696502685547 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/segmentation |
| keywords[2].score | 0.549595296382904 |
| keywords[2].display_name | Segmentation |
| keywords[3].id | https://openalex.org/keywords/algorithm |
| keywords[3].score | 0.46444395184516907 |
| keywords[3].display_name | Algorithm |
| keywords[4].id | https://openalex.org/keywords/pattern-recognition |
| keywords[4].score | 0.46369776129722595 |
| keywords[4].display_name | Pattern recognition (psychology) |
| keywords[5].id | https://openalex.org/keywords/image-segmentation |
| keywords[5].score | 0.43499305844306946 |
| keywords[5].display_name | Image segmentation |
| keywords[6].id | https://openalex.org/keywords/thresholding |
| keywords[6].score | 0.43477460741996765 |
| keywords[6].display_name | Thresholding |
| keywords[7].id | https://openalex.org/keywords/image |
| keywords[7].score | 0.19536307454109192 |
| keywords[7].display_name | Image (mathematics) |
| language | en |
| locations[0].id | doi:10.1186/s12880-024-01361-x |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S6505649 |
| locations[0].source.issn | 1471-2342 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1471-2342 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | BMC Medical Imaging |
| locations[0].source.host_organization | https://openalex.org/P4310320256 |
| locations[0].source.host_organization_name | BioMed Central |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320256, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | BioMed Central, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-024-01361-x |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | BMC Medical Imaging |
| locations[0].landing_page_url | https://doi.org/10.1186/s12880-024-01361-x |
| locations[1].id | pmid:39080591 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | BMC medical imaging |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39080591 |
| locations[2].id | pmh:oai:doaj.org/article:0abd878a787a41289d9c4b79190146b7 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | BMC Medical Imaging, Vol 24, Iss 1, Pp 1-36 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/0abd878a787a41289d9c4b79190146b7 |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:11290159 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | BMC Med Imaging |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11290159 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5024521276 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0850-0635 |
| authorships[0].author.display_name | Simrandeep Singh |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I101407740 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India |
| authorships[0].institutions[0].id | https://openalex.org/I101407740 |
| authorships[0].institutions[0].ror | https://ror.org/05t4pvx35 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I101407740 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Chandigarh University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Simrandeep Singh |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India |
| authorships[1].author.id | https://openalex.org/A5038671093 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-9713-6048 |
| authorships[1].author.display_name | Harbinder Singh |
| authorships[1].countries | ES |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I79189158 |
| authorships[1].affiliations[0].raw_affiliation_string | VISILAB, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain |
| authorships[1].institutions[0].id | https://openalex.org/I79189158 |
| authorships[1].institutions[0].ror | https://ror.org/05r78ng12 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I79189158 |
| authorships[1].institutions[0].country_code | ES |
| authorships[1].institutions[0].display_name | University of Castilla-La Mancha |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Harbinder Singh |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | VISILAB, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain |
| authorships[2].author.id | https://openalex.org/A5060312310 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0758-2755 |
| authorships[2].author.display_name | Nitin Mittal |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210137508 |
| authorships[2].affiliations[0].raw_affiliation_string | Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Palwal, 121102, India |
| authorships[2].institutions[0].id | https://openalex.org/I4210137508 |
| authorships[2].institutions[0].ror | https://ror.org/03kbe9m86 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210137508 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | Shri Vishwakarma Skill University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Nitin Mittal |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Palwal, 121102, India |
| authorships[3].author.id | https://openalex.org/A5006951500 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8383-6799 |
| authorships[3].author.display_name | Supreet Singh |
| authorships[3].countries | IN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I5847235 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Computer Science, UPES, Dehradun, Uttarakhand, India |
| authorships[3].institutions[0].id | https://openalex.org/I5847235 |
| authorships[3].institutions[0].ror | https://ror.org/04q2jes40 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I5847235 |
| authorships[3].institutions[0].country_code | IN |
| authorships[3].institutions[0].display_name | University of Petroleum and Energy Studies |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Supreet Singh |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Computer Science, UPES, Dehradun, Uttarakhand, India |
| authorships[4].author.id | https://openalex.org/A5088545047 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-1167-2430 |
| authorships[4].author.display_name | Sameh Askar |
| authorships[4].countries | SA |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I28022161 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia |
| authorships[4].institutions[0].id | https://openalex.org/I28022161 |
| authorships[4].institutions[0].ror | https://ror.org/02f81g417 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I28022161 |
| authorships[4].institutions[0].country_code | SA |
| authorships[4].institutions[0].display_name | King Saud University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | S. S. Askar |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia |
| authorships[5].author.id | https://openalex.org/A5018298333 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-7103-8872 |
| authorships[5].author.display_name | Ahmad M. Alshamrani |
| authorships[5].countries | SA |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I28022161 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia |
| authorships[5].institutions[0].id | https://openalex.org/I28022161 |
| authorships[5].institutions[0].ror | https://ror.org/02f81g417 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I28022161 |
| authorships[5].institutions[0].country_code | SA |
| authorships[5].institutions[0].display_name | King Saud University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Ahmad M. Alshamrani |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia |
| authorships[6].author.id | https://openalex.org/A5068555962 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-2846-4707 |
| authorships[6].author.display_name | Mohamed Abouhawwash |
| authorships[6].countries | EG |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I159247623 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt |
| authorships[6].institutions[0].id | https://openalex.org/I159247623 |
| authorships[6].institutions[0].ror | https://ror.org/01k8vtd75 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I159247623 |
| authorships[6].institutions[0].country_code | EG |
| authorships[6].institutions[0].display_name | Mansoura University |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Mohamed Abouhawwash |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-024-01361-x |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | An efficient multi-level thresholding method for breast thermograms analysis based on an improved BWO algorithm |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12994 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Infrared Thermography in Medicine |
| related_works | https://openalex.org/W2138983844, https://openalex.org/W1968965685, https://openalex.org/W2012792772, https://openalex.org/W2356573839, https://openalex.org/W2111883783, https://openalex.org/W2009028679, https://openalex.org/W2357424838, https://openalex.org/W2356903262, https://openalex.org/W4237142086, https://openalex.org/W2327601824 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1186/s12880-024-01361-x |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S6505649 |
| best_oa_location.source.issn | 1471-2342 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1471-2342 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | BMC Medical Imaging |
| best_oa_location.source.host_organization | https://openalex.org/P4310320256 |
| best_oa_location.source.host_organization_name | BioMed Central |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320256, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | BioMed Central, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-024-01361-x |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | BMC Medical Imaging |
| best_oa_location.landing_page_url | https://doi.org/10.1186/s12880-024-01361-x |
| primary_location.id | doi:10.1186/s12880-024-01361-x |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S6505649 |
| primary_location.source.issn | 1471-2342 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1471-2342 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | BMC Medical Imaging |
| primary_location.source.host_organization | https://openalex.org/P4310320256 |
| primary_location.source.host_organization_name | BioMed Central |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320256, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | BioMed Central, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-024-01361-x |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | BMC Medical Imaging |
| primary_location.landing_page_url | https://doi.org/10.1186/s12880-024-01361-x |
| publication_date | 2024-07-30 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2887769939, https://openalex.org/W2167445191, https://openalex.org/W2409002119, https://openalex.org/W2918783506, https://openalex.org/W2909615411, https://openalex.org/W3107885361, https://openalex.org/W2044527533, https://openalex.org/W2958483065, https://openalex.org/W2895416280, https://openalex.org/W3107739909, https://openalex.org/W2259436614, https://openalex.org/W4306319857, https://openalex.org/W2892295558, https://openalex.org/W2940815738, https://openalex.org/W3107248243, https://openalex.org/W2985699287, https://openalex.org/W2925456832, https://openalex.org/W3190512233, https://openalex.org/W3168104563, https://openalex.org/W3207365234, https://openalex.org/W2000339329, https://openalex.org/W3118961950, https://openalex.org/W2919979744, https://openalex.org/W2557923043, https://openalex.org/W2605810061, https://openalex.org/W2232317135, https://openalex.org/W2901565516, https://openalex.org/W3096682064, https://openalex.org/W1967943810, https://openalex.org/W4206934092, https://openalex.org/W2894817900, https://openalex.org/W3036906813, https://openalex.org/W3091753001, https://openalex.org/W2980225234, https://openalex.org/W3098577151, https://openalex.org/W2789244585, https://openalex.org/W2155431356, https://openalex.org/W3088189904, https://openalex.org/W4206462383, https://openalex.org/W3163674712, https://openalex.org/W2997797021, https://openalex.org/W2906658447, https://openalex.org/W3029543064, https://openalex.org/W2989010209, https://openalex.org/W3000651604, https://openalex.org/W3040361773, https://openalex.org/W2550064190, https://openalex.org/W4313396313, https://openalex.org/W2474134051, https://openalex.org/W2997838783, https://openalex.org/W2006010966, https://openalex.org/W3173312160, https://openalex.org/W2584641794, https://openalex.org/W2772834494, https://openalex.org/W2120627761, https://openalex.org/W2616699888, https://openalex.org/W2024746652, https://openalex.org/W2135733203, https://openalex.org/W414544266, https://openalex.org/W2795112021, https://openalex.org/W4205138613, https://openalex.org/W2760225670, https://openalex.org/W3176195813, https://openalex.org/W3028182033, https://openalex.org/W3103316265, https://openalex.org/W2972872998, https://openalex.org/W3080890634, https://openalex.org/W2776937175, https://openalex.org/W2083970667, https://openalex.org/W2071836643, https://openalex.org/W2974747703, https://openalex.org/W3183199155, https://openalex.org/W4212785882, https://openalex.org/W2789804916, https://openalex.org/W3193211895, https://openalex.org/W2162745921, https://openalex.org/W2809795042, https://openalex.org/W2010307644, https://openalex.org/W2597197757, https://openalex.org/W3206983901, https://openalex.org/W3048095951, https://openalex.org/W4207032074 |
| referenced_works_count | 82 |
| abstract_inverted_index.a | 4, 64 |
| abstract_inverted_index.as | 43, 167 |
| abstract_inverted_index.in | 14, 57, 155 |
| abstract_inverted_index.is | 3, 55, 81 |
| abstract_inverted_index.it | 87 |
| abstract_inverted_index.of | 12, 191 |
| abstract_inverted_index.The | 97 |
| abstract_inverted_index.and | 7, 24, 48, 60, 92, 95, 105, 128, 138, 145, 158, 164, 181, 198 |
| abstract_inverted_index.are | 27 |
| abstract_inverted_index.for | 29, 35, 83 |
| abstract_inverted_index.has | 88 |
| abstract_inverted_index.its | 196 |
| abstract_inverted_index.the | 8, 70, 78, 129, 189, 192 |
| abstract_inverted_index.BWOA | 80 |
| abstract_inverted_index.Levy | 103 |
| abstract_inverted_index.both | 156 |
| abstract_inverted_index.like | 116 |
| abstract_inverted_index.otsu | 144 |
| abstract_inverted_index.peak | 172 |
| abstract_inverted_index.show | 150 |
| abstract_inverted_index.sine | 134 |
| abstract_inverted_index.such | 42, 166 |
| abstract_inverted_index.this | 61 |
| abstract_inverted_index.with | 90, 102, 108, 113 |
| abstract_inverted_index.(BWO) | 142 |
| abstract_inverted_index.Black | 72 |
| abstract_inverted_index.Hawks | 118 |
| abstract_inverted_index.IBWOA | 112, 151 |
| abstract_inverted_index.While | 77 |
| abstract_inverted_index.Widow | 73 |
| abstract_inverted_index.based | 123 |
| abstract_inverted_index.being | 44 |
| abstract_inverted_index.black | 139 |
| abstract_inverted_index.cause | 11 |
| abstract_inverted_index.death | 13 |
| abstract_inverted_index.early | 36 |
| abstract_inverted_index.image | 53, 58, 66 |
| abstract_inverted_index.index | 178, 184 |
| abstract_inverted_index.other | 114 |
| abstract_inverted_index.ratio | 174 |
| abstract_inverted_index.shows | 32 |
| abstract_inverted_index.study | 62 |
| abstract_inverted_index.using | 69, 143 |
| abstract_inverted_index.whale | 130 |
| abstract_inverted_index.widow | 140 |
| abstract_inverted_index.women | 15 |
| abstract_inverted_index.(HHO), | 120 |
| abstract_inverted_index.(SCA), | 137 |
| abstract_inverted_index.(WOA), | 133 |
| abstract_inverted_index.Breast | 1 |
| abstract_inverted_index.Harris | 117 |
| abstract_inverted_index.IBWOA, | 194 |
| abstract_inverted_index.Linear | 121 |
| abstract_inverted_index.X-ray, | 23 |
| abstract_inverted_index.breast | 37 |
| abstract_inverted_index.cancer | 2 |
| abstract_inverted_index.cosine | 135 |
| abstract_inverted_index.issues | 89 |
| abstract_inverted_index.method | 99 |
| abstract_inverted_index.second | 9 |
| abstract_inverted_index.value, | 169 |
| abstract_inverted_index.visual | 162 |
| abstract_inverted_index.(FSIM). | 185 |
| abstract_inverted_index.(PSNR), | 175 |
| abstract_inverted_index.(SSIM), | 180 |
| abstract_inverted_index.Kapur's | 146 |
| abstract_inverted_index.Medical | 52 |
| abstract_inverted_index.Results | 149 |
| abstract_inverted_index.Various | 17 |
| abstract_inverted_index.complex | 84 |
| abstract_inverted_index.crucial | 56 |
| abstract_inverted_index.disease | 6, 38 |
| abstract_inverted_index.entropy | 147 |
| abstract_inverted_index.feature | 182 |
| abstract_inverted_index.fitness | 168 |
| abstract_inverted_index.flights | 104 |
| abstract_inverted_index.imaging | 18 |
| abstract_inverted_index.leading | 10 |
| abstract_inverted_index.measure | 179 |
| abstract_inverted_index.method. | 148 |
| abstract_inverted_index.metrics | 165 |
| abstract_inverted_index.promise | 34 |
| abstract_inverted_index.results | 187 |
| abstract_inverted_index.values, | 171 |
| abstract_inverted_index.(IBWOA). | 76 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Adaptive | 124 |
| abstract_inverted_index.analyses | 160 |
| abstract_inverted_index.delivers | 152 |
| abstract_inverted_index.employed | 28 |
| abstract_inverted_index.enhances | 100 |
| abstract_inverted_index.improved | 71 |
| abstract_inverted_index.improves | 106 |
| abstract_inverted_index.magnetic | 25 |
| abstract_inverted_index.offering | 40 |
| abstract_inverted_index.proposed | 98, 193 |
| abstract_inverted_index.results. | 51 |
| abstract_inverted_index.standard | 79 |
| abstract_inverted_index.superior | 153 |
| abstract_inverted_index.(LSHADE), | 127 |
| abstract_inverted_index.Algorithm | 75 |
| abstract_inverted_index.Comparing | 111 |
| abstract_inverted_index.Evolution | 126 |
| abstract_inverted_index.algorithm | 68, 132, 136 |
| abstract_inverted_index.analysis, | 59 |
| abstract_inverted_index.balancing | 93 |
| abstract_inverted_index.effective | 82 |
| abstract_inverted_index.globally. | 16 |
| abstract_inverted_index.including | 20, 161 |
| abstract_inverted_index.learning. | 110 |
| abstract_inverted_index.prevalent | 5 |
| abstract_inverted_index.problems, | 86 |
| abstract_inverted_index.providing | 49 |
| abstract_inverted_index.real-time | 50 |
| abstract_inverted_index.threshold | 170 |
| abstract_inverted_index.advantages | 41 |
| abstract_inverted_index.algorithms | 115 |
| abstract_inverted_index.detection, | 39 |
| abstract_inverted_index.detection. | 30 |
| abstract_inverted_index.inspection | 163 |
| abstract_inverted_index.introduces | 63 |
| abstract_inverted_index.resonance, | 26 |
| abstract_inverted_index.similarity | 177, 183 |
| abstract_inverted_index.stagnation | 91 |
| abstract_inverted_index.structural | 176 |
| abstract_inverted_index.validating | 195 |
| abstract_inverted_index.demonstrate | 188 |
| abstract_inverted_index.exploration | 94, 101 |
| abstract_inverted_index.performance | 154 |
| abstract_inverted_index.qualitative | 157 |
| abstract_inverted_index.significant | 33 |
| abstract_inverted_index.techniques, | 19 |
| abstract_inverted_index.Differential | 125 |
| abstract_inverted_index.Experimental | 186 |
| abstract_inverted_index.Optimization | 74, 119 |
| abstract_inverted_index.Thermography | 31 |
| abstract_inverted_index.exploitation | 107 |
| abstract_inverted_index.mammography, | 21 |
| abstract_inverted_index.optimization | 85, 131, 141 |
| abstract_inverted_index.quantitative | 159 |
| abstract_inverted_index.segmentation | 54, 67 |
| abstract_inverted_index.superiority. | 199 |
| abstract_inverted_index.effectiveness | 197 |
| abstract_inverted_index.exploitation. | 96 |
| abstract_inverted_index.non-invasive, | 46 |
| abstract_inverted_index.non-ionizing, | 45 |
| abstract_inverted_index.thermographic | 65 |
| abstract_inverted_index.outperformance | 190 |
| abstract_inverted_index.Success-History | 122 |
| abstract_inverted_index.cost-effective, | 47 |
| abstract_inverted_index.signal-to-noise | 173 |
| abstract_inverted_index.ultrasonography, | 22 |
| abstract_inverted_index.quasi-opposition-based | 109 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5060312310 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I4210137508 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.67350761 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |