The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Lesion border detection in dermoscopy images
Open accessAbstract
Background Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Methods In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects. Conclusion Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.
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A methodological approach to the classification of dermoscopy images
Celebi, M. E., Kingravi, H. A., Uddin, B., Iyatomi, H., Aslandogan, Y. A., Stoecker, W. V., & Moss, R. H. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.01.003 · Cited by 693
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Border detection in dermoscopy images using statistical region merging
Emre Celebi, M., Kingravi, H. A., Iyatomi, H., Alp Aslandogan, Y., Stoecker, W. V., Moss, R. H., Malters, J. M., Grichnik, J. M., Marghoob, A. A., Rabinovitz, H. S., et al. (2008) · Skin Research and Technology · doi:10.1111/j.1600-0846.2008.00301.x · Cited by 366
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Fast anisotropic gauss filtering
Geusebroek, J., Smeulders, A. W. M., & van de Weijer, J. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.812429 · Cited by 331
Median Filtering in Constant Time
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Cited by
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620
Self-citationAutomatic skin lesion segmentation using deep fully convolutional networks with jaccard distance.
Yuan, Y., Chao, M., & Lo, Y.-C. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2695227 · Cited by 687
Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms
Masood, A., & Ali Al-Jumaily, A. (2013) · International Journal of Biomedical Imaging · doi:10.1155/2013/323268 · Cited by 347
Fusing fine-tuned deep features for skin lesion classification
Mahbod, A., Schaefer, G., Ellinger, I., Ecker, R., Pitiot, A., & Wang, C. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.10.007 · Cited by 264
Self-citationA Benchmark for Automatic Visual Classification of Clinical Skin Disease Images
Sun, X., Yang, J., Sun, M., & Wang, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_13 · Cited by 197
Metadata
- Authors
- Celebi, M. E., Iyatomi, H., Schaefer, G., & Stoecker, W. V.
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2009-03
- Volume
- 33
- Issue
- 2
- Pages
- 148-153
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- OpenAlex
- W2139888281
- Cited by
- 422
- References
- 19
- FWCI
- 10.74
- Type
- journal article
- Language
- en
- Open access
- green
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