Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12494/41424
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Title: Study of cervical cancer through fractals and a method of clustering based on quantum mechanics
Author: Torres Hoyos, Francisco Jose
Martín-Landrove M.
Baena Navarro, Ruben Enrique
Vergara Villadiego, Juan Raul
Cardenas J.C.
Email autor: ruben.baena@campusucc.edu.co
Issue Date: 2019
Keywords: 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
Abstract: Tumor growth in the cervix is a complex process. Understanding this phenomena is quite relevant in order to establish proper diagnosis and therapy strategies and a possible startpoint is to evaluate its complexity through the scaling analysis, which define the tumor growth geometry. In this work, tumor interface from primary tumors of squamous cells and adenocarcinomas for cervical cancer were extracted. Fractal dimension and local roughness exponent (Barabási and Stanley (1996)), aloc, were calculated to characterize the in vivo 3-D tumor growth. Image acquisition was carried out according to the standard protocol used for cervical cancer radiotherapy, i.e., axial, magnetic resonance T1 - weighted contrast enhanced images comprising the cervix volume for image registration. Image processing was carried out by a classification scheme based on quantum clustering algorithm (Mussa et al. (2015))combined with the application of the K-means procedure upon contrasted images (Demirkaya et al. (2008)). The results show significant variations of the parameters depending on the tumor stage and its histological origin. © 2019
Type: Artículo
Citation: Hoyos FT,Martín M,Baena R,Vergara J,Cardenas JC. Study of cervical cancer through fractals and a method of clustering based on quantum mechanics. Appl Radiat Isot. 2019. 150. p. 182-191. .
Other Identifiers: https://doi.org/10.1016/j.fluid.2012.02.009
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062487040&doi=10.1088%2f1742-6596%2f1160%2f1%2f012019&partnerID=40&md5=6e2018f40b33214f2223fe5c736f2276
Appears in Collections:Artículos Científicos

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