Artificial Neural Network-Based System for PET Volume Segmentation.
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| Title: | Artificial Neural Network-Based System for PET Volume Segmentation. |
|---|---|
| Authors: | Sharif, Saeed1 mhd.sharif@brunel.ac.uk, Abbod, Maysam1, Amira, Abbes2, Zaidi, Habib3,4 |
| Source: | International Journal of Biomedical Imaging. 2010, p1-11. 11p. 3 Color Photographs, 2 Diagrams, 3 Charts, 4 Graphs. |
| Subjects: | Artificial neural networks, Product usage segmentation, Positron emission tomography, Diagnostic imaging research, Diagnosis, Artificial intelligence research, Monte Carlo method |
| Abstract: | Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Biomedical Imaging is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 56438508 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial Neural Network-Based System for PET Volume Segmentation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sharif%2C+Saeed%22">Sharif, Saeed</searchLink><relatesTo>1</relatesTo><i> mhd.sharif@brunel.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Abbod%2C+Maysam%22">Abbod, Maysam</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Amira%2C+Abbes%22">Amira, Abbes</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zaidi%2C+Habib%22">Zaidi, Habib</searchLink><relatesTo>3,4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Biomedical+Imaging%22">International Journal of Biomedical Imaging</searchLink>. 2010, p1-11. 11p. 3 Color Photographs, 2 Diagrams, 3 Charts, 4 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Product+usage+segmentation%22">Product usage segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Positron+emission+tomography%22">Positron emission tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging+research%22">Diagnostic imaging research</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence+research%22">Artificial intelligence research</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Biomedical Imaging is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/2010/105610 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Product usage segmentation Type: general – SubjectFull: Positron emission tomography Type: general – SubjectFull: Diagnostic imaging research Type: general – SubjectFull: Diagnosis Type: general – SubjectFull: Artificial intelligence research Type: general – SubjectFull: Monte Carlo method Type: general Titles: – TitleFull: Artificial Neural Network-Based System for PET Volume Segmentation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sharif, Saeed – PersonEntity: Name: NameFull: Abbod, Maysam – PersonEntity: Name: NameFull: Amira, Abbes – PersonEntity: Name: NameFull: Zaidi, Habib IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 16874188 Titles: – TitleFull: International Journal of Biomedical Imaging Type: main |
| ResultId | 1 |
