Main Article Content
Medical images are essential for diagnosis, planning of surgery and evolution of pathology. The advances in technology have developed new techniques to obtain digital images with more details, in return this has also led to disadvantages, such as: the analysis of large volumes of information, long time to determine an affected region and difficulty in defining the malignant tissue for its later extirpation, among the most relevant. This article presents an image segmentation strategy and the optimization of a method for generating three-dimensional models. A prototype was implemented in which it was possible to evaluate the segmentation algorithms and 3D reconstruction technique, allowing to visualize the tumor model from different points of view through virtual reality. In this investigation, we evaluate the computational cost and user experience, the parameters selected in terms of computational cost are the time and consumption of RAM, we used 140 MRI images each with dimensions 260x320 pixel, and as a result, we obtained an approximate time of 37.16s and consumption in RAM of 1.3GB. Another experiment carried out is the segmentation and reconstruction of a tumor, this model is formed by a three-dimensional mesh made up of 151 vertices and 318 faces. Finally, we evaluate the application, with a usability test applied to a sample of 20 people with different areas of knowledge. The results show that the graphics presented by the software are pleasant, they also show that the application is intuitive and easy to use. Additionally, it is mentioned that it helps improve the understanding of medical images.
 R. León-Barua, and R. Berendson-Seminario, “Theorical Medicine. Definition of medicine and its relation to biology,” Rev. Med. Hered., vol. 7(1), pp. 1-3, 1996. https://doi.org/10.20453/rmh.v7i1.499
 S. Sierre, D. Teplisky, and J. Lipsich, “Vascular malformations: an update on imaging and treatment,” Arch. Argent. Pediatr., vol. 114(2), pp. 167-176, 2016. http://doi.org/10.5546/aap.2016.167
 P. Soffia, C. Ubeda, P. Miranda, and L. Rodríguez, “Radioprotección al día en radiología diagnóstica: Conclusiones de la Conferencia Iberoamericana de Protección Radiológica en Medicina (CIPRaM) 2016,” Rev. Chil. Radiol., vol. 23(1), pp. 15-19, 2017. http://doi.org/10.4067/S0717-93082017000100004
 E. Bosch, “Sir Godfrey Newbold Hounsfield y la tomografía computada, su contribución a la medicina moderna,” Rev. Chil. Radiol., vol. 10(4), pp. 183-185, 2004. http://doi.org/10.4067/S0717-93082004000400007
 I. D. Aristizábal, “The magnetic resonance and its agro-industry applications, a review,” Rev. Fac. Nal. Agr., vol. 60(2), pp. 4037-4066, 2007.
 G. Schmidt, “Introducción,” Ecografía: De la imagen al diagnóstico. Spain: Panamericana, 2007.
 J. A. Ruiz-Guijarro, “Tomografía por emisión de positrones (PET): evolución y futuro,” Radiobiología, vol. 7, pp. 148-156, 2007.
 G. Sakas, “Trends in medical imaging: From 2D to 3D,” Computer & Graphics, vol. 26(4), pp. 577-587, Aug. 2002. https://doi.org/10.1016/S0097-8493(02)00103-6
 G. Li, D. Citrin, K. Camphausen, B. Mueller, C. Burman, B. Mychalczak, R. W. Miller, and Y. Song, “Advances in 4D medical Imaging and 4D Radiation Therapy,” Technology in Cancer Research an Treatment, vol. 7(1), pp. 67-81, Feb. 2008. https://doi.org/10.1177/153303460800700109
 D. R. Ortega, and A. M. Iznaga, “Técnicas de Segmentación de Imágenes Médicas,” in 14 Convención científica de ingeniería y arquitectura, Habana, Cuba, 2008, pp. 1-7.
 A. L. Bokde, S. J. Teipel, R. Schwarz, G. Leinsinger, K. Buerger, T. Moeller, H. J. Möller, and H. A. Hampel, “Reliable manual segmentation of the frontal, parietal, temporal, and occipital lobes on magnetic resonance images of healthy subjects,” Brain research protocols, vol. 14(3), pp. 135-145, Mar. 2005. https://doi.org/10.1016/j.brainresprot.2004.10.001
 S. L. Cichosz, S. Vangsgaard, A. S. Jørgensen, K. E. Kannik, E. Steffensen, and S. F. Eskildsen, “Brain tumor segmentation from MRI: a comparative study,” in IADIS Multi Conference on Computer Science and Information Systems, Germany, 2010, pp. 401-406.
 J. Jaya, and K. Thanushkodi, “Certain investigation on MRI segmentation for the implementation of CAD system,” WSEAS Transactions on Computers, vol. 10(6), pp. 189-198, Jun. 2011.
 K. Selvanayaki, and M. Karnan, “CAD system for automatic detection of brain tumor through magnetic resonance image-A review,” International Journal of Engineering Science and Technology, vol. 2(10), pp. 5890-5901, Oct. 2010.
 K. P. Wong, “Medical image segmentation: methods and applications in functional imaging”, in Handbook of biomedical image analysis. New York: Springer, 2005, pp. 111-182. https://doi.org/10.1007/0-306-48606-7_3
 J. Weese, and C. Lorenz, “Four challenges in medical image analysis from an industrial perspective,” Medical Image Analysis, vol. 33, pp. 44-49, Oct. 2016. https://doi.org/10.1016/j.media.2016.06.023
 M. AI-Ayyoub, S. AIZu’bi, Y. Jararweh, M. A. Shehab, and B. B. Gupta, “Accelerating 3D medical volume segmentation using GPUs,” Multimedia Tools and Applications, vol. 77(3), pp. 4939-4958, Dec. 2016. https://doi.org/10.1007/s11042-016-4218-0
 I. Scholl, T. Aach, T. M. Deserno, and T. Kuhlen, “Challenges of medical image processing,” Comput. Sci. Res. Dev., vol. 26(1-2), pp. 5-13, Dec. 2011. https://doi.org/10.1007/s00450-010-0146-9
 A. Fedorov, D. Clunie, E. Ulrch, C. Bauer, A. Wahle, B. Brown, M. Onken, J. Riesmeier, S. Pieper, R. Kikinis, J. Buatti, and R. R. Beichel, “DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research,” PeerJ, vol. 4, pp. 2057-2081, May. 2016. https://doi.org/10.7717/peerj.2057
 C. J. Roth, L. M. Lannum, and C. L. Joseph, “Enterprise Imaging Governance: HIMSS-SIIM Collaborative White Paper,” J. Digit. Imaging, vol. 29(5), pp. 539-546, Jun. 2016. https://doi.org/10.1007/s10278-016-9883-z
 S. Sornapudi, R. J. Stanley, W. V. Stoecker, H. Almubarak, R. Long, S. Antani, G. Thoma, R. Zuna, and S. R. Frazier, “Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels,” J. Pathol. Inform., vol. 8(38), pp. 1-12, Mar. 2017. https://doi.org/10.4103/jpi.jpi_74_17
 P. Mildenberger, M. Eichelberg, and E. Martin, “Introduction to the DICOM standard,” Eur Radiol., vol. 12(4), pp. 920-927, Apr. 2002. https://doi.org/10.1007/s003300101100
 J. C. Ramírez-Giraldo, C. Arboleda-Clavijo, and C. H. McCollough, “Tomografía computarizada por rayos X: fundamentos y actualidad,” Revista Ingeniería Biomédica, vol. 2(4), pp. 54-66, Nov. 2008.
 N. Pebet, “Resonancia Nuclear Magnética,” in XIII Seminario de Ingeniería Biomédica, Montevideo, Uruguay, 2004, pp. 1-5.
 A. Marangoni, “A.I. Arrival on Radiology-Threat or Challenge to Update?,” Rev. Argent. Radiol., vol. 82(2), pp. 55-56, Jun. 2018. https://doi.org/10.1055/s-0038-1656546
 L. Caponetti, G. Castellano, and V. Corsini, “MR Brain Image Segmentation: A Framework to Compare Different Clustering Techniques,” Information, vol. 8(4), pp. 138-159, Nov. 2017. https://doi.org/10.3390/info8040138
 B. Gharnali, and S. Alipour, “MRI Image Segmentation Using Conditional Spatial FCM Based on Kernel-Induced Distance Measure,” Engineering, Rechnology and Applied Science Research, vol. 8(3), pp. 2985-2990, Jun. 2018.
 R. Ahmmed, A. Rahman, and F. Hossain, “Fuzzy Logic Based Algorithm to Classify Tumor Categories with Position from Brain MRI Images,” in 3rd International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 2017, pp. 1-6. https://doi.org/10.1109/eict.2017.8275232
 M. Jaros, P. Strakos, T. Karásek, L. Ríha, A. Vasatová, M. Jarosová, and T. Kozubek, “Implementation of K-means segmentation algorithm on Intel Xeon Phi and GPU: Application in medical imaging,” Advances in Engineering Software, vol. 103, pp. 21-28, Jan. 2017. https://doi.org/10.1016/j.advengsoft.2016.05.008
 K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl, “Constrained k-means clustering with background knowledge,” in ICML 01 Proceedings of the Eighteenth International Conference on Machine Learning, San Francisco, United States, 2001, pp. 577-584.
 D. Boening, A. Gauthier-Kemper, B. Gmeiner and J. Klingauf, “Cluster Recognition by Delaunay Triangulation of Synaptic Proteins in 3D,” Adv. Biosys., vol 1(10), pp. 1700091(1-8), Aug. 2017. https://doi.org/10.1002/adbi.201700091
 S. Jaramillo, W. Osorio, and J. C. Espitia. “Avances en el tratamiento del glioblastoma multiforme,” Univ. Méd., vol. 51(2), pp. 186-203, Apr. 2010. https://doi.org/10.11144/Javeriana.umed51-2.atgm
 K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, and F. Prior, “The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository”, Journal of Digital Imaging, vol. 26 (6), Dec. 2013, pp 1045-1057. https://doi.org/10.1007/s10278-013-9622-7
 E. R. Lara, I. María, and A. Barrera, “RENTOL: A Clustering Algorithm Based on K-means,” Research in Computing Science, vol. 128, pp. 149-157, 2016. https://doi.org/10.13053/rcs-128-1-14
 M. A. Barajas, R. M. Reyes, A. A. Maldonado, A. I. García, and J. D. Rivera, “Análisis de cuestionarios para la evaluación de la usabilidad en programas de computadora,” E-Gnosis, vol. 16, pp. 158-162, 2018.
 R. Indraswari, T. Kurita, A. Z. Arifin, N. Suciati, and E. R. Astuti, “Multi-projection deep learning network for segmentation of 3D medical images,” Pattern Recognition Letters, vol. 125, pp. 791-797, Aug. 2019. https://doi.org/10.1016/j.patrec.2019.08.003
 H. Imai, S. Matzek, T. D. Le, Y. Negishi, and K. Kawachiya, “Fast and accurate 3D medical image segmentation with data-swapping method,” Arxiv, Pre-print, pp. 1-13, 2019.
 M. Maitra, and K. Jaman, “3D unsupervised modified spatial fuzzy c-means method for segmentation of 3D brain MR image,” Pattern Analysis and Applications, vol. 22(4), pp. 1561-1571, Mar. 2019. https://doi.org/10.1007/s10044-019-00806-2
 X. Zhang, H. Xhao, X. Li, Y. Feng, and H. Li, “A multi-scale 3D Otsu thresholding algorithm for medical image segmentation,” Digital Signal Processing, vol. 60, pp. 186-199, Aug. 2017. http://doi.org/10.1016/j.dsp.2016.08.003
 Y. Zhang, S. Miao, T. Mansi, and R. Liao, “Unsupervised X-ray Image Segmentation with Task Driven Generative Adversarial Networks,” Medical Image Analysis, vol. 62, pp. 1-20 Feb. 2020. http://doi.org/10.1016/j.media.2020.101664
 W. Zhao. and Z. Zeng, “Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation”, Arxiv, Pre-print, 2019. https://doi.org/10.24926/548719.007
 Q. Dou, L. Yu, H. Chen, Y. Jin, X. Yang, and P. A. Heng, “3D deeply supercised network for automated segmentation of volumetric medical images,” Medical Image Anslysis, vol. 41, pp. 40-54, May. 2017. https://doi.org/10.1016/j.media.2017.05.001
 H. R. Roth, H. Oda, X Zhow, N. Shimizu, Y. Yang, Y. Hayashi, and K. Mori, “An application of cascade 3D fully convolutional networks for medical image segmentation,” Compterized Medical Imaging and Graphics, vol. 66, pp. 90-99, Mar. 2018. https://doi.org/10.1016/j.compmedimag.2018.03.001