Incidencia de los filtros de suavizado en las herramientas de diagnóstico asistido por computador

Sergio Alexander Castro Casadiego; Diego Andrés Castellano Carvajal; Carlos Vicente Niño Rondón; Byron Medina Delgado; Dinael Guevara Ibarra; Miguel Eduardo Posada Haddad

doi:10.24054/rcta.v1i39.1376

Authors

Sergio Alexander Castro Casadiego Universidad Francisco de Paula Santander https://orcid.org/0000-0003-0962-9916
Diego Andrés Castellano Carvajal Universidad Francisco de Paula Santander https://orcid.org/0000-0002-4530-1136
Carlos Vicente Niño Rondón Universidad Francisco de Paula Santander https://orcid.org/0000-0002-3781-4564
Byron Medina Delgado Universidad Francisco de Paula Santander https://orcid.org/0000-0003-0754-8629
Dinael Guevara Ibarra Universidad Francisco de Paula Santander https://orcid.org/0000-0003-3007-8354
Miguel Eduardo Posada Haddad Servicio Nacional de Aprendizaje SENA https://orcid.org/0000-0002-8918-1770

DOI:

https://doi.org/10.24054/rcta.v1i39.1376

Keywords:

Medical Image Processing, smoothing filters, skin lesions, correlation

Abstract

Image processing in the biomedical field refers to the initial stage aimed at improving image quality by removing irrelevant noise and unwanted sections in the image background. This paper evaluates the incidence of smoothing filters (bilateral edge-preserving filter, median filter and Gaussian filter) in digital processing of dermoscopic medical images in support of computer-aided diagnosis tools. The method was tested with images from the HAM10000 Dataset, composed of images of pigmented skin lesions. Open source tools such as Python language and the specialized computer vision library OpenCV were used for digital processing. Validation was performed by the correlation method between the original grayscale image and the image filtered by each of the smoothing filters, obtaining a percentage mean change ratio of 99.460 % for the bilateral filter, 99.396 % for the Gaussian filter, and 99.335 % for the median filter.

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References

Al-abayechi, A. A. A., & Abu-Almash, F. S. (2020). Skin Lesion Border Detection Based on Best Statistical Model Using Optimal Colour Channel. Journal of Autonomous Intelligence, 3(1), 26. https://doi.org/10.32629/JAI.V3I1.131

Asokan, A., & Anitha, J. (2020). Adaptive Cuckoo Search based optimal bilateral filtering for denoising of satellite images. ISA Transactions, 100, 308–321. https://doi.org/10.1016/J.ISATRA.2019.11. 008

Castellanos, W. A., Suarez, O. J., & Garcia, A. P. (2018). Usability in virtual learning environments, an approach to the integrated grid (IG) application. Paper presented at the Proceedings of the LACCEI International Multi-Conference for Engineering, Education and Technology, , 2018-July doi:10.18687/LACCEI2018.1.1.497

Garg, B., & Sharma, G. K. (2016). A quality- aware Energy-scalable Gaussian Smoothing Filter for image processing applications. Microprocessors and Microsystems, 45, 1–9. https://doi.org/10.1016/J.MICPRO.2016.02.012

Garcia, A. P., Suarez, O., & Castellanos, W. (2016). ERAAE virtual library. CHILECON 2015 - 2015 IEEE Chilean Conference on Electrical, Electronics Engineering, Information and Communication Technologies, Proceedings of IEEE Chilecon 2015, 911-916. doi:10.1109/Chilecon.2015.7404681

Huang, H.-W., Hsu, B. W.-Y., Lee, C.-H., & Tseng, V. S. (2021). Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers. The Journal of Dermatology, 48(3), 310–316. https://doi.org/10.1111/1346-8138.15683 Ibrahim, E., Ewees, A. A., & Eisa, M. (2020).

Proposed Method for Segmenting Skin Lesions Images. Lecture Notes in Electrical Engineering, 569, 13–23. https://doi.org/10.1007/978-981-13-8942-9_2

Kang, X., Zhang, X., Li, S., Li, K., Li, J., & Benediktsson, J. A. (2017). Hyperspectral Anomaly Detection with Attribute and Edge-Preserving Filters. IEEE Transactions on Geoscience and Remote Sensing, 55(10), 5600–5611. https://doi.org/10.1109/TGRS.2017.27101 45

Lynn, N. C., & Kyu, Z. M. (2018). Segmentation and classification of skin cancer Melanoma from skin lesion images. Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, 117– 122. https://doi.org/10.1109/PDCAT.2017.0002 8

Mane, S., & Shinde, S. (2018). A Method for Melanoma Skin Cancer Detection Using Dermoscopy Images. 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018. https://doi.org/10.1109/ICCUBEA.2018.86 97804

Márquez Díaz, J. E. (2020). Deep Artificial Vision Applied to the Early Identification of Non-Melanoma Cancer and Actinic Keratosis. Computación y Sistemas, 24(2), 751–766. https://doi.org/10.13053/cys-24-2-2901

Neshatpour, K., Koohi, A., Farahmand, F., Joshi, R., Rafatirad, S., Sasan, A., & Homayoun, H. (2016). Big biomedical image processing hardware acceleration: A case study for K- means and image filtering. Proceedings - IEEE International Symposium on Circuits and Systems, 1134–1137. https://doi.org/10.1109/ISCAS.2016.75274 45

Ottom, M. A. (2019). Convolutional neural network for diagnosing skin cancer. International Journal of Advanced Computer Science and Applications, 10(7), 333–338. https://doi.org/10.14569/IJACSA.2019.010 0746

Padmavathi, K., & Thangadurai, K. (2016). Implementation of RGB and Grayscale Images in Plant Leaves Disease Detection – Comparative Study. Indian Journal of Science and Technology, 9(6), 1–6. https://doi.org/10.17485/IJST/2016/V9I6/7 7739

Singhal, P., Verma, A., & Garg, A. (2017). A study in finding effectiveness of Gaussian blur filter over bilateral filter in natural scenes for graph based image segmentation. 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017,4–9. https://doi.org/10.1109/ICACCS.2017.8014612

Xu, Z., Sheykhahmad, F. R., Ghadimi, N., & Razmjooy, N. (2020). Computer-aided diagnosis of skin cancer based on soft computing techniques. Open Medicine (Poland), 15(1), 860–871. https://doi.org/10.1515/MED-2020- 0131/MACHINEREADABLECITATION/ RIS

Zhu, F., Liang, Z., Jia, X., Zhang, L., & Yu, Y. (2019). A Benchmark for Edge-Preserving Image Smoothing. IEEE Transactions on Image Processing, 28(7), 3556–3570. https://doi.org/10.1109/TIP.2019.290877