A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the...

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Autores principales: Xu, Min, Qian, Pengjiang, Zheng, Jiamin, Ge, Hongwei, Muzic, Raymond F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222610/
https://www.ncbi.nlm.nih.gov/pubmed/32454883
http://dx.doi.org/10.1155/2020/4519483
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author Xu, Min
Qian, Pengjiang
Zheng, Jiamin
Ge, Hongwei
Muzic, Raymond F.
author_facet Xu, Min
Qian, Pengjiang
Zheng, Jiamin
Ge, Hongwei
Muzic, Raymond F.
author_sort Xu, Min
collection PubMed
description We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the ε-insensitive loss function, the structural risk term, and the core-set principle. We apply this algorithm to the identification of specific target areas in MR images. (2) For each abdominal MRI case, we use four MR sequences (fat, water, in-phase (IP), and opposed-phase (OP)) and the position coordinates (x, y) of each pixel as the input of the algorithm. We use three classifiers to identify the liver and kidneys in the MR images. Experiments show that the proposed method achieves a higher precision in the recognition of specific regions of medical images and has better adaptability in the case of large-scale datasets than the traditional RBF algorithm.
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spelling pubmed-72226102020-05-23 A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images Xu, Min Qian, Pengjiang Zheng, Jiamin Ge, Hongwei Muzic, Raymond F. Comput Math Methods Med Research Article We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the ε-insensitive loss function, the structural risk term, and the core-set principle. We apply this algorithm to the identification of specific target areas in MR images. (2) For each abdominal MRI case, we use four MR sequences (fat, water, in-phase (IP), and opposed-phase (OP)) and the position coordinates (x, y) of each pixel as the input of the algorithm. We use three classifiers to identify the liver and kidneys in the MR images. Experiments show that the proposed method achieves a higher precision in the recognition of specific regions of medical images and has better adaptability in the case of large-scale datasets than the traditional RBF algorithm. Hindawi 2020-05-05 /pmc/articles/PMC7222610/ /pubmed/32454883 http://dx.doi.org/10.1155/2020/4519483 Text en Copyright © 2020 Min Xu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Xu, Min
Qian, Pengjiang
Zheng, Jiamin
Ge, Hongwei
Muzic, Raymond F.
A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images
title A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images
title_full A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images
title_fullStr A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images
title_full_unstemmed A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images
title_short A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images
title_sort novel radial basis neural network-leveraged fast training method for identifying organs in mr images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222610/
https://www.ncbi.nlm.nih.gov/pubmed/32454883
http://dx.doi.org/10.1155/2020/4519483
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