Publication:
Cuantificación automática de la relación de grasa visceral versus grasa abdominal (o subcutánea) empleando técnicas de procesamiento de imágenes médicas

Thumbnail Image
Files

Files

Primary Sánchez Pinzón, Emilio Alejandro-2024.pdf (1.51 MB)
 Autorización.pdf (531.53 KB)
View Statistics
Reference Managers
Mendeley
Indexers
Google Scholar CORE
QR Code
QR Code
Authors

Authors

Sánchez Pinzón, Emilio Alejandro
Abstract (Spanish)
spa: La identificación del tejido adiposo subcutáneo y del tejido adiposo visceral en imágenes de resonancia magnética de manera automática, para su posterior cuantificación y relacionamiento, fue el principal objetivo del presente trabajo, con la finalidad de disminuir tiempos y errores involuntarios por parte del médico que realiza el diagnóstico. Para lo cual se elaboró un algoritmo implementado en Matlab.
Abstract (English)
The identification of subcutaneous adipose tissue and visceral adipose tissue in magnetic resonance images in an automatic way, for their subsequent quantification and relationship, was the main objective of the present work, with the purpose of reducing time and involuntary errors by the physician who performs the diagnosis. For this purpose, an algorithm implemented in Matlab was elaborated.
Director

Advisors/Directors

Cancino Suarez, Sandra Liliana
Pastrana Rendon, Homero Fernando
Extent
64 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/2843
Collections

Collections

EB - Trabajos de Grado Maestría en Ingeniería Electrónica
References

Gutiérrez Cortez, Edgar Agustín, Evelyn del Socorro Goicochea Ríos, and Edgardo Linares Reyes. 2020. “Definición de Obesidad: Más Allá Del Índice de Masa Corporal.” REVISTA MÉDICA VALLEJIANA/ Vallejian Medical Journal 9(1):61–64. doi: 10.18050/revistamedicavallejiana.v9i1.2425.

Hui, Steve C. N., Teng Zhang, Lin Shi, Defeng Wang, Chei Bing Ip, and Winnie C. W. Chu. 2018. “Automated Segmentation of Abdominal Subcutaneous Adipose Tissue and Visceral Adipose Tissue in Obese Adolescent in MRI.” Magnetic Resonance Imaging 45(January 2017):97–104. doi: 10.1016/j.mri.2017.09.016.

Kavur, A. Emre, Naciye Sinem Gezer, Mustafa Baris, Yusuf Sahin, Savas Ozkan, Bora Baydar, Ulas Yuksel, Caglar Kilikcier, Sahin Olut, Gozde Bozdagi Akar, Gozde Unal, Oguz Dicle, and M. Alper Selver. 2020. “Comparison of Semi-Automatic and Deep Learning-Based Automatic Methods for Liver Segmentation in Living Liver Transplant Donors.” Diagnostic and Interventional Radiology 26(1):11–21. doi: 10.5152/dir.2019.19025.

Mendoza, Ricardo, Andres Sanchez, Luis Felipe Uriza, and Marcela Hernandez Hoyos. 2011. “Automatic Segmentation of Adipose Viseral Tissue from CAT Images, Employing Anatomical Invariants.” Pp. 1–6 in 2011 6th Colombian Computing Congress (CCC). IEEE.

Kavur, A. Emre, Naciye Sinem Gezer, Mustafa Baris, Yusuf Sahin, Savas Ozkan, Bora Baydar, Ulas Yuksel, Caglar Kilikcier, Sahin Olut, Gozde Bozdagi Akar, Gozde Unal, Oguz Dicle, and M. Alper Selver. 2019. “CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data (Version v1.03) .”