Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.

Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influence the treatment strategy. Although several diagnostic methods based on different modalities exist, a pre-operative method for determining tumor mal...

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Autores principales: Shaswati Roy, Pradipta Maji
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/20cba771399348aeafcf5fff2926a248
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spelling oai:doaj.org-article:20cba771399348aeafcf5fff2926a2482021-11-25T06:23:32ZMultispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.1932-620310.1371/journal.pone.0250964https://doaj.org/article/20cba771399348aeafcf5fff2926a2482021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0250964https://doaj.org/toc/1932-6203Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influence the treatment strategy. Although several diagnostic methods based on different modalities exist, a pre-operative method for determining tumor malignancy state still remains as an active research area. In this regard, the paper presents a new method for the assessment of tumor grades using conventional MR sequences namely, T1, T1 with contrast enhancement, T2 and FLAIR. The proposed method for tumor gradation is mainly based on feature extraction using multiresolution image analysis and classification using support vector machine. Since the wavelet features of different tumor subregions, obtained from single MR sequence, do not carry equally important information, a wavelet fusion technique is proposed based on the texture information content of each voxel. The concept of texture gradient, used in the proposed algorithm, fuses the wavelet coefficients of the given MR sequences. The feature vector is then derived from the co-occurrence of fused wavelet coefficients. As each wavelet subband contains distinct detail information, a novel concept of multispectral co-occurrence of wavelet coefficients is introduced to capture the spatial correlation among different subbands. It enables to convey more informative features to characterize the tumor type. The effectiveness of the proposed method is analyzed, with respect to six classification performance indices, on BRATS 2012 and BRATS 2014 data sets. The classification accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under curve assessed by the ten-fold cross-validation are 91.3%, 96.8%, 66.7%, 92.4%, 88.4%, and 92.0%, respectively, on real brain MR data.Shaswati RoyPradipta MajiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0250964 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Shaswati Roy
Pradipta Maji
Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.
description Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influence the treatment strategy. Although several diagnostic methods based on different modalities exist, a pre-operative method for determining tumor malignancy state still remains as an active research area. In this regard, the paper presents a new method for the assessment of tumor grades using conventional MR sequences namely, T1, T1 with contrast enhancement, T2 and FLAIR. The proposed method for tumor gradation is mainly based on feature extraction using multiresolution image analysis and classification using support vector machine. Since the wavelet features of different tumor subregions, obtained from single MR sequence, do not carry equally important information, a wavelet fusion technique is proposed based on the texture information content of each voxel. The concept of texture gradient, used in the proposed algorithm, fuses the wavelet coefficients of the given MR sequences. The feature vector is then derived from the co-occurrence of fused wavelet coefficients. As each wavelet subband contains distinct detail information, a novel concept of multispectral co-occurrence of wavelet coefficients is introduced to capture the spatial correlation among different subbands. It enables to convey more informative features to characterize the tumor type. The effectiveness of the proposed method is analyzed, with respect to six classification performance indices, on BRATS 2012 and BRATS 2014 data sets. The classification accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under curve assessed by the ten-fold cross-validation are 91.3%, 96.8%, 66.7%, 92.4%, 88.4%, and 92.0%, respectively, on real brain MR data.
format article
author Shaswati Roy
Pradipta Maji
author_facet Shaswati Roy
Pradipta Maji
author_sort Shaswati Roy
title Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.
title_short Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.
title_full Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.
title_fullStr Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.
title_full_unstemmed Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.
title_sort multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/20cba771399348aeafcf5fff2926a248
work_keys_str_mv AT shaswatiroy multispectralcooccurrenceofwaveletcoefficientsformalignancyassessmentofbraintumors
AT pradiptamaji multispectralcooccurrenceofwaveletcoefficientsformalignancyassessmentofbraintumors
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