Characterization of border structure using Fractal Dimension in melanomas
There are many characteristics that differentiate normal moles (nevi) from melanomas. One of them is their boundary irregularity, which can be quantified using Fractal Dimension. In this work, fractal dimension of normal moles and melanoma was computed using the box counting method. These measuremen...
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Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_97814244_v_n_p4088_Carbonetto |
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todo:paper_97814244_v_n_p4088_Carbonetto2023-10-03T16:43:10Z Characterization of border structure using Fractal Dimension in melanomas Carbonetto, S.H. Lew, S.E. Automatic Detection Boundary irregularities Box-counting method Medicine Partial discharges Fractal dimension article fractal analysis human melanoma pathology skin tumor Fractals Humans Melanoma Skin Neoplasms There are many characteristics that differentiate normal moles (nevi) from melanomas. One of them is their boundary irregularity, which can be quantified using Fractal Dimension. In this work, fractal dimension of normal moles and melanoma was computed using the box counting method. These measurements were used to train a linear decoder in order to predict the pathology. The average performance to discriminate normal moles from melanomas reached 85% giving some insights about the power of the fractal dimension as a candidate for automatic detection and diagnosis. © 2010 IEEE. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_97814244_v_n_p4088_Carbonetto |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Automatic Detection Boundary irregularities Box-counting method Medicine Partial discharges Fractal dimension article fractal analysis human melanoma pathology skin tumor Fractals Humans Melanoma Skin Neoplasms |
spellingShingle |
Automatic Detection Boundary irregularities Box-counting method Medicine Partial discharges Fractal dimension article fractal analysis human melanoma pathology skin tumor Fractals Humans Melanoma Skin Neoplasms Carbonetto, S.H. Lew, S.E. Characterization of border structure using Fractal Dimension in melanomas |
topic_facet |
Automatic Detection Boundary irregularities Box-counting method Medicine Partial discharges Fractal dimension article fractal analysis human melanoma pathology skin tumor Fractals Humans Melanoma Skin Neoplasms |
description |
There are many characteristics that differentiate normal moles (nevi) from melanomas. One of them is their boundary irregularity, which can be quantified using Fractal Dimension. In this work, fractal dimension of normal moles and melanoma was computed using the box counting method. These measurements were used to train a linear decoder in order to predict the pathology. The average performance to discriminate normal moles from melanomas reached 85% giving some insights about the power of the fractal dimension as a candidate for automatic detection and diagnosis. © 2010 IEEE. |
format |
CONF |
author |
Carbonetto, S.H. Lew, S.E. |
author_facet |
Carbonetto, S.H. Lew, S.E. |
author_sort |
Carbonetto, S.H. |
title |
Characterization of border structure using Fractal Dimension in melanomas |
title_short |
Characterization of border structure using Fractal Dimension in melanomas |
title_full |
Characterization of border structure using Fractal Dimension in melanomas |
title_fullStr |
Characterization of border structure using Fractal Dimension in melanomas |
title_full_unstemmed |
Characterization of border structure using Fractal Dimension in melanomas |
title_sort |
characterization of border structure using fractal dimension in melanomas |
url |
http://hdl.handle.net/20.500.12110/paper_97814244_v_n_p4088_Carbonetto |
work_keys_str_mv |
AT carbonettosh characterizationofborderstructureusingfractaldimensioninmelanomas AT lewse characterizationofborderstructureusingfractaldimensioninmelanomas |
_version_ |
1782030397486399488 |