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Multi-level fusion in ultrasound for cancer detection based on uniform LBP features

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an en...

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Main Authors: Qader Zeebaree, Diyar, Abdulazeez, Adnan Mohsin, Zebari, Dilovan Asaad, Haron, Habibollah, Abdull Hamed, Haza Nuzly
Format: Article
Language:English
Published: Tech Science Press 2021
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/95948/1/HabibollahHaron2021_MultiLevelFusioninUltrasoundforCancerDetection.pdf
http://eprints.utm.my/id/eprint/95948/
http://dx.doi.org/10.32604/cmc.2021.013314
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spelling my.utm.959482022-07-01T04:44:26Z http://eprints.utm.my/id/eprint/95948/ Multi-level fusion in ultrasound for cancer detection based on uniform LBP features Qader Zeebaree, Diyar Abdulazeez, Adnan Mohsin Zebari, Dilovan Asaad Haron, Habibollah Abdull Hamed, Haza Nuzly QA75 Electronic computers. Computer science Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed. This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer, which was attained in two stages. First, several images were generated from a single image using the pre-processing method. The median and Wiener filters were utilized to lessen the speckle noise and enhance the ultrasound image texture. This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes. Second, the fusion mechanism allowed the production of diverse features from different filtered images. The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated. The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images, respectively. The proposed method achieved very high accuracy (98%), sensitivity (98%), and specificity (99%). As a result, the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy, sensitivity, and specificity. Tech Science Press 2021 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95948/1/HabibollahHaron2021_MultiLevelFusioninUltrasoundforCancerDetection.pdf Qader Zeebaree, Diyar and Abdulazeez, Adnan Mohsin and Zebari, Dilovan Asaad and Haron, Habibollah and Abdull Hamed, Haza Nuzly (2021) Multi-level fusion in ultrasound for cancer detection based on uniform LBP features. Computers, Materials and Continua, 66 (3). pp. 3363-3382. ISSN 1546-2218 http://dx.doi.org/10.32604/cmc.2021.013314
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Qader Zeebaree, Diyar
Abdulazeez, Adnan Mohsin
Zebari, Dilovan Asaad
Haron, Habibollah
Abdull Hamed, Haza Nuzly
Multi-level fusion in ultrasound for cancer detection based on uniform LBP features
description Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed. This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer, which was attained in two stages. First, several images were generated from a single image using the pre-processing method. The median and Wiener filters were utilized to lessen the speckle noise and enhance the ultrasound image texture. This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes. Second, the fusion mechanism allowed the production of diverse features from different filtered images. The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated. The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images, respectively. The proposed method achieved very high accuracy (98%), sensitivity (98%), and specificity (99%). As a result, the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy, sensitivity, and specificity.
format Article
author Qader Zeebaree, Diyar
Abdulazeez, Adnan Mohsin
Zebari, Dilovan Asaad
Haron, Habibollah
Abdull Hamed, Haza Nuzly
author_facet Qader Zeebaree, Diyar
Abdulazeez, Adnan Mohsin
Zebari, Dilovan Asaad
Haron, Habibollah
Abdull Hamed, Haza Nuzly
author_sort Qader Zeebaree, Diyar
title Multi-level fusion in ultrasound for cancer detection based on uniform LBP features
title_short Multi-level fusion in ultrasound for cancer detection based on uniform LBP features
title_full Multi-level fusion in ultrasound for cancer detection based on uniform LBP features
title_fullStr Multi-level fusion in ultrasound for cancer detection based on uniform LBP features
title_full_unstemmed Multi-level fusion in ultrasound for cancer detection based on uniform LBP features
title_sort multi-level fusion in ultrasound for cancer detection based on uniform lbp features
publisher Tech Science Press
publishDate 2021
url http://eprints.utm.my/id/eprint/95948/1/HabibollahHaron2021_MultiLevelFusioninUltrasoundforCancerDetection.pdf
http://eprints.utm.my/id/eprint/95948/
http://dx.doi.org/10.32604/cmc.2021.013314
_version_ 1738510303449055232
score 13.211869

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