Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification
The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2012/327861 |
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| _version_ | 1832553631773097984 |
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| author | Mhd Saeed Sharif Maysam Abbod Abbes Amira Habib Zaidi |
| author_facet | Mhd Saeed Sharif Maysam Abbod Abbes Amira Habib Zaidi |
| author_sort | Mhd Saeed Sharif |
| collection | DOAJ |
| description | The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell
carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions. |
| format | Article |
| id | doaj-art-bbb158ecceea4a27a26acd768cb38147 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-bbb158ecceea4a27a26acd768cb381472025-02-03T05:53:36ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2012-01-01201210.1155/2012/327861327861Artificial Neural Network-Statistical Approach for PET Volume Analysis and ClassificationMhd Saeed Sharif0Maysam Abbod1Abbes Amira2Habib Zaidi3Department of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, Uxbridge UB83PH, UKDepartment of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, Uxbridge UB83PH, UKNanotechnology and Integrated BioEngineering Centre, University of Ulster, Newtownabbey BT37OQB, UKDivision of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, SwitzerlandThe increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.http://dx.doi.org/10.1155/2012/327861 |
| spellingShingle | Mhd Saeed Sharif Maysam Abbod Abbes Amira Habib Zaidi Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification Advances in Fuzzy Systems |
| title | Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification |
| title_full | Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification |
| title_fullStr | Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification |
| title_full_unstemmed | Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification |
| title_short | Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification |
| title_sort | artificial neural network statistical approach for pet volume analysis and classification |
| url | http://dx.doi.org/10.1155/2012/327861 |
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