The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Automatic Imaging System With Decision Support for Inspection of Pigmented Skin Lesions and Melanoma Diagnosis
Open accessAbstract
In this paper, we describe an automatic system for inspection of pigmented skin lesions and melanoma diagnosis, which supports images of skin lesions acquired using a conventional (consumer level) digital camera. More importantly, our system includes a decision support component, which combines the outcome of the image classification with context knowledge such as skin type, age, gender, and affected body part. This allows the estimation of the personal risk of melanoma, so as to add confidence to the classification. We found that our system classified images with an accuracy of 86%, with a sensitivity of 94%, and specificity of 68%. The addition of context knowledge was indeed able to point to images that were erroneously classified as benign, albeit not to all of them.
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“In the medical field, a skin inspection system was developed to search for pigments that indicate skin cancer [71]. The software utilised image-processing techniques, such as threshold-based feature segmentation. After the detection of skin anomalies, a machine learning based decision support tool was introduced to help the classification of those findings and determine whether the anomaly was Ben”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [71]
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Cites
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Hall, M. A. (2000) · Cited by 1,715
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Hall, M. A., & Holmes, G. (2003) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2003.1245283 · Cited by 1,237
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Diepgen, T. L., & Mahler, V. (2002) · British Journal of Dermatology · doi:10.1046/j.1365-2133.146.s61.2.x · Cited by 1,235
Watkinson, J. (2012) · Routledge eBooks · doi:10.4324/9780080495828-8 · Cited by 1,143
Research works printed but not linked: 14
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- stolz, 2002, Color Atlas of Dermatoscopy - 2nd enlarged and completely revised edition
- sobel, 1968, A 3 � 3 isotropic gradient operator for image processing, Stanford Artificial Project
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- aucouturier, 2008, Second-order statistics are less important for audio textures than for image textures
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Cited by
Items 201–210 of 210
Melanoma Skin Cancer Detection Using Wavelet Transform and Local Ternary Pattern
Ragumadhavan, R., Britto, K. R. A., & Vimala, R. (2022) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2022.3856
Flores, E. S., & Scharcanski, J. (2023) · doi:10.5753/sbcas_estendido.2023.229522
Flores, E. S., & Scharcanski, J. (2023) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.5753/sibgrapi.est.2023.27452
Dogga, A., Aurchana, P., & Shanthi, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-8666-4_35
A Comparative Study on Melanoma Detection Using Machine Learning Paradigm
Lalitha, G., & Riyazuddin, Y. M. (2024) · Advanced technologies and societal change · doi:10.1007/978-981-99-2832-3_1
Automated Breslow thickness prediction for better therapeutic management of melanoma patients
Melissa, E., Riabitch, D., Lazzeri, L., Fidanzi, C., Rosa, F. L., Benvenuti, C., D’Acunto, M., Bagnoni, G., Massi, D., & Laurino, M. (2025) · International Symposium on Medical Information and Communication Technology · doi:10.1109/ismict64722.2025.11059398
Extração de características de lesões de pele a partir de imagens
Barbosa, R. B., Júnior, C. R. D. C., Pereira, A. S., Marranghello, N., de Araújo, A. F., Tavares, J. M. R. S., & Rossetti, R. B. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Metadata
- Authors
- Fernandez Alcon, J., Ciuhu, C., ten Kate, W., Heinrich, A., Uzunbajakava, N., Krekels, G., Siem, D., & de Haan, G.
- Affiliations
- Philips (Netherlands) (NL)
- Catharina Ziekenhuis (NL)
- Venue
- IEEE Journal of Selected Topics in Signal Processing
- Published
- 2009-02
- Volume
- 3
- Issue
- 1
- Pages
- 14-25
- ISSN
- 1932-4553 (ISSN-L)
- 1941-0484
- OpenAlex
- W2131264832
- Cited by
- 210
- References
- 29
- Citations by year
- 2025: 4 · 2024: 9 · 2023: 19 · 2022: 10 · 2021: 28 · 2020: 19 · 2019: 13 · 2018: 14 · 2017: 13 · 2016: 25 · 2015: 16 · 2014: 16 · 2013: 12 · 2012: 8
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- green
- research.tue.nl
- Locations
- IEEE Journal of Selected Topics in Signal Processing (publishedVersion)
- TU/e Research Portal (submittedVersion, other-oa)
- TU/e Research Portal (submittedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › melanin and skin pigmentation
- Keywords
- pigmented skin lesions, melanoma diagnosis, computer-aided decision support, skin lesion classification, contextual knowledge, skin cancer screening
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
