Study of cervical cancer through fractals and a method of clustering based on quantum mechanics
Fecha
2019
Autores
Torres Hoyos, Francisco José
Martín-Landrove, M.
Baena Navarro, Rubén Enrique
Vergara Villadiego, Juan Raul
Cardenas, J. C.
Título de la revista
ISSN de la revista
Título del volumen
Editor
Elsevier Ltd
Resumen
Palabras clave
Algorithms , Diagnosis , Diseases , Fractal dimension , Fractals , Image enhancement , Magnetic resonance , Quantum theory , Tumors , 05 , 45 , Df , 68 , 35 , Ct , Cervix , K-means , Local roughness , K-means clustering , Article , cancer staging , clinical protocol , cluster analysis , contrast enhancement , controlled study , female , fractal analysis , human , image analysis , image processing , image segmentation , in vivo study , major clinical study , nuclear magnetic resonance imaging , oncological parameters , priority journal , quantum mechanics , three dimensional imaging , tumor growth , tumor volume , uterine cervix adenocarcinoma , uterine cervix cancer , adenocarcinoma , algorithm , cluster analysis , computer assisted diagnosis , diagnostic imaging , fractal analysis , pathology , procedures , quantum theory , squamous cell carcinoma , uterine cervix tumor , Adenocarcinoma , Algorithms , Carcinoma , Squamous Cell , Cluster Analysis , Female , Fractals , Humans , Image Interpretation , Computer-Assisted , Imaging , Three-Dimensional , Magn