A DBMS-Based Framework for Content-Based Retrieval and Analysis of Skin Ulcer Images in Medical Practice
Resumo
Bedridden patients with skin lesions (ulcers) often do not have access to specialized clinic equipment. It is important to allow healthcare practitioners to use their smartphones to leverage information regarding the proper treatment to be carried. Existing applications require special equipment, such as heat sensors, or focus only on general information. To fulfill this gap, we propose ULEARn, a DBMS-based framework for the processing of ulcer images, providing tools to store and retrieve similar images of past cases. The proposed mobile application ULEARn-App allows healthcare practitioners to send a photo from a patient to ULEARn, and obtain a timely feedback that allows the improvement of procedures on therapeutic interventions. Experimental results of ULEARn and ULEARn-App using a real-world dataset showed that our tool can quickly respond to the required analysis and retrieval tasks, being up to 4.6 times faster than the specialist’ expected execution time.
Palavras-chave:
A DBMS-Based Framework, Image Content-Based Retrieval, mobile application, image retrieval tasks, image retrieval analysis
Referências
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Marchione, F., Araújo, L., and Araújo, L. (2015). Approaches that use software to support the prevention of pressure ulcer: A systematic review. IJMI, 84(10):725–736. DOI: https://doi.org/10.1016/j.ijmedinf.2015.05.013
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Pedro, L. M. C. C., Rodrigues, J. J. P. C., and Martins, H. M. G. (2011). mUlcer – a mobile ulcer care record approach for integrative care. In EIS, pages 392–401. DOI: https://doi.org/10.1007/978-3-642-24352-3_41
Traina-Jr., C., Traina, A. J. M., Seeger, B., and Faloutsos, C. (2000). Slim-trees: High performance metric trees minimizing overlap between nodes. In EDBT, pages 51–65. DOI: https://doi.org/10.1007/3-540-46439-5_4
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Barioni, M. C. N., Razente, H. L., Traina, A. J. M., and Traina-Jr., C. (2006). SIREN: A similarity retrieval engine for complex data. In VLDB, pages 1155–1158.
Chino, D. Y. T. et al. (2018). Icarus: Retrieving skin ulcer images through bag-ofsignatures. In CBMS, pages 82–87. DOI: https://doi.org/10.1109/CBMS.2018.00022
Chittoria, R. K. (2012). Telemedicine for wound management. Indian J Plast Surg, 45(2):412–417. DOI: https://doi.org/10.4103/0970-0358.101330
Ciaccia, P., Patella, M., and Zezula, P. (1997). M-tree: An efficient access method for similarity search in metric spaces. In VLDB, pages 426–435.
Dorileo, E. A. G., Frade, M. A. C., Rangayyan, R. M., and Azevedo-Marques, P. M. (2010). Segmentation and analysis of the tissue composition of dermatological ulcers. In CCECE, pages 1–4. DOI: https://doi.org/10.1109/CCECE.2010.5575143
Fraiwan, L., Ninan, J., and Al-Khodari, M. (2018). Mobile application for ulcer detection. TOBEJ, 12:16–26. DOI: https://doi.org/10.2174/1874120701812010016
Gamus, A., Keren, E., Kaufman, H., and Chodick, G. (2019). Synchronous video telemedicine in lower extremities ulcers treatment: A real-world data study. IJMI, 124:31–36. DOI: https://doi.org/10.1016/j.ijmedinf.2019.01.009
García-Zapirain, B., Elmogy, M., El-Baz, A., and Elmaghraby, A. S. (2018). Classification of pressure ulcer tissues with 3d convolutional neural network. MBEC, 56(12):2245–2258. DOI: https://doi.org/10.1007/s11517-018-1835-y
Goyal, M., Yap, M. H., Reeves, N. D., Rajbhandari, S., and Spragg, J. (2017). Fully convolutional networks for diabetic foot ulcer segmentation. In SMC, pages 618–623. DOI: https://doi.org/10.1109/SMC.2017.8122675
Kaster, D. S., Bugatti, P. H., Traina, A. J. M., and Traina-Jr., C. (2010). FMI-SiR: A flexible and efficient module for similarity searching on oracle database. JIDM, 1(2):229–244.
Lu, W., Hou, J., Yan, Y., Zhang, M., Du, X., and Moscibroda, T. (2017). MSQL: efficient similarity search in metric spaces using SQL. VLDB J., 26(6):829–854. DOI: https://doi.org/10.1007/s00778-017-0481-6
Marchione, F., Araújo, L., and Araújo, L. (2015). Approaches that use software to support the prevention of pressure ulcer: A systematic review. IJMI, 84(10):725–736. DOI: https://doi.org/10.1016/j.ijmedinf.2015.05.013
MultiMedia, I. (2002). Mpeg-7: The generic multimedia content description standard, part 1. IEEE MultiMedia, 9(2):78–87. DOI: https://doi.org/10.1109/93.998074
Nesso-Jr., M. R. et al. (2018). RAFIKI: retrieval-based application for imaging and knowledge investigation. In CBMS, pages 71–76. DOI: https://doi.org/10.1109/CBMS.2018.00020
Pedro, L. M. C. C., Rodrigues, J. J. P. C., and Martins, H. M. G. (2011). mUlcer – a mobile ulcer care record approach for integrative care. In EIS, pages 392–401. DOI: https://doi.org/10.1007/978-3-642-24352-3_41
Traina-Jr., C., Traina, A. J. M., Seeger, B., and Faloutsos, C. (2000). Slim-trees: High performance metric trees minimizing overlap between nodes. In EDBT, pages 51–65. DOI: https://doi.org/10.1007/3-540-46439-5_4
Zaki, M. J. and Meira Jr., W. (2014). Data Mining and Analysis - Fundamental Concepts and Algorithms. Cambridge University Press, New York, NY, USA.
Publicado
07/10/2019
Como Citar
CAZZOLATO, Mirela T. et al.
A DBMS-Based Framework for Content-Based Retrieval and Analysis of Skin Ulcer Images in Medical Practice. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 34. , 2019, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 109-120.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2019.8812.
