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Automatic Imaging System With Decision Support for Inspection of Pigmented Skin Lesions and Melanoma Diagnosis
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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
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,398
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,350
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001) · Advanced textbooks in control and signal processing · doi:10.1007/978-1-4471-0347-9_3 · Cited by 5,053
Probabilistic Reasoning in Intelligent Systems
(1988) · Elsevier eBooks · doi:10.1016/c2009-0-27609-4 · Cited by 3,411
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Hall, M. A. (2000) · Cited by 1,715
The Epidemiology of Skin Cancer
Gloster, H. M., & Brodland, D. G. (1996) · Dermatologic Surgery · doi:10.1111/j.1524-4725.1996.tb00312.x · Cited by 1,265
Benchmarking attribute selection techniques for discrete class data mining
Hall, M. A., & Holmes, G. (2003) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2003.1245283 · Cited by 1,237
The epidemiology of skin cancer
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
- talbot, 2003, An overview of the polartechnics solarscan melanoma diagnosis algorithms, Proc APRS Workshop on Digital Image Computing, 33
- 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
- 2001, Color Image Segmentation A State-of-the-art Survey, 67
- aucouturier, 2008, Second-order statistics are less important for audio textures than for image textures
- 0, Melanoma Project
- 0, Dermnet Skin Disease Image Atlas
- 0, Skin Cancer Basal Cell Carcicoma Squamous Cell Carcicoma and Malingnant Melanoma
- symptoms, 0, http //www cancerhelp org/help/default asp?page=3009 National Institute for Health and Clinical Exce-lence
- 0, Who is at Risk of Melanoma
- 0, WEKA
- 0, Cancer Facts and Figures 2008
- 2001, U S Emerging Melanoma Therapeutics Market
- 0, 2007 Skin Cancer Facts
Cited by
Items 101–200 of 210
Medical image learning from a few/few training samples: Melanoma segmentation study
Asaeikheybari, G., Green, J., Qian, X., Jiang, H., & Huang, M.-C. (2019) · Smart Health · doi:10.1016/j.smhl.2019.100088 · Cited by 10
Automatic Diagnosis of Melanoid Skin Lesions Using Machine Learning Methods
Grzesiak-Kopeć, K., Nowak, L. A., & OGORZAŁEK, M. J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19324-3_51 · Cited by 10
Graph-based pigment network detection in skin images
Sadeghi, M., Razmara, M., Ester, M., Lee, T. K., & Atkins, M. S. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.844602 · Cited by 10
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Khan, A. H., Latif, G., Iskandar, D. N. F. A., Alghazo, J. M., & Butt, M. (2018) · International Conference on Biomedical Engineering and Technology (ICBET) · doi:10.1145/3208955.3208961 · Cited by 7
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Warsi, M. F., Chauhan, U., Gupta, S. N., & Tiwari, P. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.10.137 · Cited by 7
Σπυρίδωνος, Π. Π., Gaitanis, G., Likas, A. C., & Bassukas, I. D. (2019) · Skin Research and Technology · doi:10.1111/srt.12684 · Cited by 7
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Liberman, G., Acevedo, D. G., & Mejail, M. E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-13469-3_85 · Cited by 6
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Riaz, F., Hassan, A., & Zeb, J. (2014) · doi:10.1109/icosp.2014.7015166 · Cited by 6
Naeem, S., Riaz, F., Hassan, A., & Nisar, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24834-9_50 · Cited by 6
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Vardasca, R., & Magalhaes, C. (2021) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-7998-7709-7.ch021 · Cited by 6
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Ananna, M. E., Nayeem, J., Alam, M. J., & Islam, S. (2023) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei56765.2023.10125640 · Cited by 5
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Barros, R. S., & Walter, M. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12943 · Cited by 5
Computational Classification of Melanocytic Skin Lesions
Grzesiak-Kopeć, K., OGORZAŁEK, M. J., & Nowak, L. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39384-1_15 · Cited by 5
Pigmented Skin Lesion Diagnosis by Automated Imaging System
Sheha, M. A. (2015) · Journal of Bioengineering and Biomedical Sciences · doi:10.4172/2155-9538.1000170 · Cited by 5
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Ghali, F. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1279/1/012020 · Cited by 5
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Hasan, M. Z., Shoumik, S., & Zahan, N. (2019) · International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) · doi:10.1109/ic4me247184.2019.9036653 · Cited by 5
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Wijesinghe, L. A. D. C. N., Kulasekera, D., & Ilmini, W. M. K. S. (2019) · National Information Technology Conference (NITC) · doi:10.1109/nitc48475.2019.9114507 · Cited by 5
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Naronglerdrit, P., & Mporas, I. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-6321-8_5 · Cited by 5
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Li, L., Clark, A., & Wang, J. Z. (2014) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2014.26 · Cited by 5
Automatic change detection in multiple pigmented skin lesions
Korotkov, K. (2014) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 5
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Vardasca, R., & Magalhaes, C. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-7544-7.ch067 · Cited by 4
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Mei, X.-F., Xie, F., & Jiang, Z. (2016) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip.2016.8079847 · Cited by 4
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Gupta, K., Jaiprasad, Krishnan, M., Narayanan, A., & Narayan, N. S. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413193 · Cited by 4
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Mia, M. S., Mim, S. M., Alam, M. J., Razib, M., Mahamudullah, & Bilgaiyan, S. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128274 · Cited by 4
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Thamizhamuthu, R., & Maniraj, S. P. (2023) · Traitement du signal · doi:10.18280/ts.400330 · Cited by 5
Drulytė, I., Ruzgas, T., Raišutis, R., Valiukevičienė, S., & Linkevičiūtė, G. (2018) · Libyan Journal of Medicine · doi:10.1080/19932820.2018.1479600 · Cited by 5
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Cavalcanti, P. G., & Scharcanski, J. (2013) · Series in bioengineering · doi:10.1007/978-3-642-39608-3_8 · Cited by 4
Total dermoscopy score calculation using quantitative measurements in digital dermoscopy
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Aljanabi, M. H. A., Ajel, A. R., Alazawi, A., & Abdul-Nab, R. A. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/881/1/012133 · Cited by 3
Shrimali, S. (2022) · International Conference of Computer and Informatics Engineering (IC2IE) · doi:10.1109/ic2ie56416.2022.9970048 · Cited by 3
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hammo, A., & Younis, M. C. (2022) · Maǧallaẗ al-rāfidayn li-ʿulūm al-ḥāsibāt wa-al-riyāḍiyyāẗ/Al-Rafidain journal for computer sciences and mathematics · doi:10.33899/csmj.2022.176589 · Cited by 5
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Nadiger, P. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.53390 · Cited by 3
Wamane, N., Yadav, A., Bhoir, J., Shelke, D., & Kadam, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36402-0_72 · Cited by 3
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Garg, S., & Jindal, B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17143-6 · Cited by 3
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Parekh, P., Oyeleke, R. O., & Vishwanath, T. (2024) · JMIR Dermatology · doi:10.2196/59839 · Cited by 3
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Rosado, L., João, M., Vasconcelos, M. J. M., & Ferreira, M. (2015) · Spring Conference on Computer Graphics · doi:10.1145/2788539.2788553 · Cited by 3
WITHDRAWN: Computational methods for the image segmentation of pigmented skin lesions: A Review
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Kaur, K., & Ada, A. (2015) · International Journal of Computer Applications · doi:10.5120/20398-2699 · Cited by 2
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Naronglerdrit, P., Mporas, I., Paraskevas, M., & Kapoulas, V. (2020) · International Conference on Biomedical Innovations and Applications (BIA) · doi:10.1109/bia50171.2020.9244493 · Cited by 2
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Zhou, Y., & Song, Z. (2013) · Series in bioengineering · doi:10.1007/978-3-642-39608-3_10 · Cited by 1
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Wahba, M. A., & Ashour, A. S. (2020) · doi:10.1002/9781119644316.ch10 · Cited by 1
Characterization of texture in image of skin lesions by support vector machine
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This is to certify that the thesis/dissertation prepared
Hoblitzell, A., Mukhopadhyay, S., & Xia, Y. (2010)
Uma Abordagem em Data Mining para Classificação de Lesões de Pele por meio de Imagens
Oliveira, R. B. (2013)
Non-Invasive Skin Cancer Classification from Surface Scanned Lesion Images
Dhinagar, N. J. (2013) · OhioLink ETD Center (Ohio Library and Information Network)
Computer Aided Diagnosis System for Melanoma Lesion Detection
Endalkachew, W. (2018) · National Academic Digital Repository of Ethiopia · doi:10.20372/nadre/5265
Digital watermarking and segmentation of macroscopic pigmented skin lesions images
Pirnog, I., Preda, R. O., Oprea, C. C., & Dobre, R. A. (2017) · International Symposium on Signals, Circuits and Systems · doi:10.1109/isscs.2017.8034904
Effective Classification Techniques for Melanoma Classification on Dermoscopy Images
M, M. A. K. (2018) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2018.7089
Melanoma Diagnostic System using Non-Shannon Havrda Measure and Harris Corner Detector
Agrawal, N., & Khanna, V. (2018) · International Journal of Computer Applications · doi:10.5120/ijca2018917729
Possibilities of Melanoma by Extracting All Asymmetric Features
Kumar, S., Sharma, D. K., Yadav, G. K., Singh, H. P., & Gupta, A. (2018) · International Conference on Intelligent Circuits and Systems (ICICS) · doi:10.1109/icics.2018.00057
Towards the Performance Investigation of Automatic Melanoma Diagnosis Applications
Asif, A., Fatima, I., Anjum, A., & Saif, U. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100351
Comparative study of classification methods for diagnosis of Skin Cancer
Baheti, M. (2020) · Journal of Emerging Technologies and Innovative Research
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
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.
