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Detection of Brain Tumor from MR Images Based on Co-occurrence Matrix: Collabration Work between Ministry Of Education, Baghdad University and Al-Nahrain University. Baghdad Iraq. 2019 - Couverture souple
Synopsis
The Automatic analysis of Medical images using computer analysis diagnosis is one of the most interesting field in biomedical image processing. The proposed system gives techniques related to MRI analysis. A statistical structure analysis based on tumor segmentation scheme is presented, which focuses on the structural analysis in both normal and abnormal tissues,will help doctors to avoid the human error in manual interpretation of medical content. In this study, an enhanced thresholding algorithm is applied to extract the abnormal part from the 2D MRI. Samples of different ages and cases are taken from the AL-Imammain Al-Kadhimain Medical city and the Radiology Institute.Calculating the area of the abnormal tissue (tumor), the Wavelet transformation is then applied which is a signal estimation technique that exploits the capabilities to denoising the signal. A statistical feature has been obtained; then a hybrid method is applied in which k-mean clustering is a method of cluster analysis which aims to partitioned the images into clusters. Finally,an algorithm has been created to colored images depending on the boundary. This helps to separate the abnormal part into k clusters.
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Detection of Brain Tumor from MR Images Based on Co-occurrence Matrix
Edité par
LAP LAMBERT Academic Publishing Aug 2019, 2019
ISBN 10 : 6200287848
ISBN 13 : 9786200287847
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The Automatic analysis of Medical images using computer analysis diagnosis is one of the most interesting field in biomedical image processing. The proposed system gives techniques related to MRI analysis. A statistical structure analysis based on tumor segmentation scheme is presented, which focuses on the structural analysis in both normal and abnormal tissues,will help doctors to avoid the human error in manual interpretation of medical content. In this study, an enhanced thresholding algorithm is applied to extract the abnormal part from the 2D MRI. Samples of different ages and cases are taken from the AL-Imammain Al-Kadhimain Medical city and the Radiology Institute.Calculating the area of the abnormal tissue (tumor), the Wavelet transformation is then applied which is a signal estimation technique that exploits the capabilities to denoising the signal. A statistical feature has been obtained; then a hybrid method is applied in which k-mean clustering is a method of cluster analysis which aims to partitioned the images into clusters. Finally,an algorithm has been created to colored images depending on the boundary. This helps to separate the abnormal part into k clusters. 108 pp. Englisch. N° de réf. du vendeur 9786200287847
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Detection of Brain Tumor from MR Images Based on Co-occurrence Matrix
Edité par
LAP LAMBERT Academic Publishing, 2019
ISBN 10 : 6200287848
ISBN 13 : 9786200287847
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Khalaph Kawther AliKawther Ali Khalaph: Assistant Lecturer, Msc. Physics, Ministry of Education. Alyaa Hussein Ali:Assistant Professor, College Of Science For Women, Baghdad University. Ihssan S. Nema: Professor and Consultant Neuros. N° de réf. du vendeur 385887356
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Detection of Brain Tumor from MR Images Based on Co-occurrence Matrix: Collabration Work between Ministry Of Education, Baghdad University and Al-Nahrain University. Baghdad Iraq. 2019
Edité par
LAP LAMBERT Academic Publishing, 2019
ISBN 10 : 6200287848
ISBN 13 : 9786200287847
Neuf
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Paperback. Etat : Brand New. 108 pages. 8.66x5.91x0.25 inches. In Stock. N° de réf. du vendeur zk6200287848
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Detection of Brain Tumor from MR Images Based on Co-occurrence Matrix
Edité par
LAP LAMBERT Academic Publishing Aug 2019, 2019
ISBN 10 : 6200287848
ISBN 13 : 9786200287847
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The Automatic analysis of Medical images using computer analysis diagnosis is one of the most interesting field in biomedical image processing. The proposed system gives techniques related to MRI analysis. A statistical structure analysis based on tumor segmentation scheme is presented, which focuses on the structural analysis in both normal and abnormal tissues,will help doctors to avoid the human error in manual interpretation of medical content. In this study, an enhanced thresholding algorithm is applied to extract the abnormal part from the 2D MRI. Samples of different ages and cases are taken from the AL-Imammain Al-Kadhimain Medical city and the Radiology Institute.Calculating the area of the abnormal tissue (tumor), the Wavelet transformation is then applied which is a signal estimation technique that exploits the capabilities to denoising the signal. A statistical feature has been obtained; then a hybrid method is applied in which k-mean clustering is a method of cluster analysis which aims to partitioned the images into clusters. Finally,an algorithm has been created to colored images depending on the boundary. This helps to separate the abnormal part into k clusters.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 108 pp. Englisch. N° de réf. du vendeur 9786200287847
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Detection of Brain Tumor from MR Images Based on Co-occurrence Matrix : Collabration Work between Ministry Of Education, Baghdad University and Al-Nahrain University. Baghdad Iraq. 2019
Edité par
LAP LAMBERT Academic Publishing, 2019
ISBN 10 : 6200287848
ISBN 13 : 9786200287847
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The Automatic analysis of Medical images using computer analysis diagnosis is one of the most interesting field in biomedical image processing. The proposed system gives techniques related to MRI analysis. A statistical structure analysis based on tumor segmentation scheme is presented, which focuses on the structural analysis in both normal and abnormal tissues,will help doctors to avoid the human error in manual interpretation of medical content. In this study, an enhanced thresholding algorithm is applied to extract the abnormal part from the 2D MRI. Samples of different ages and cases are taken from the AL-Imammain Al-Kadhimain Medical city and the Radiology Institute.Calculating the area of the abnormal tissue (tumor), the Wavelet transformation is then applied which is a signal estimation technique that exploits the capabilities to denoising the signal. A statistical feature has been obtained; then a hybrid method is applied in which k-mean clustering is a method of cluster analysis which aims to partitioned the images into clusters. Finally,an algorithm has been created to colored images depending on the boundary. This helps to separate the abnormal part into k clusters. N° de réf. du vendeur 9786200287847
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Detection of Brain Tumor from MR Images Based on Co-occurrence Matrix | Collabration Work between Ministry Of Education, Baghdad University and Al-Nahrain University. Baghdad Iraq. 2019
Edité par
LAP LAMBERT Academic Publishing, 2019
ISBN 10 : 6200287848
ISBN 13 : 9786200287847
Neuf
Taschenbuch
Vendeur : preigu, Osnabrück, Allemagne
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Taschenbuch. Etat : Neu. Detection of Brain Tumor from MR Images Based on Co-occurrence Matrix | Collabration Work between Ministry Of Education, Baghdad University and Al-Nahrain University. Baghdad Iraq. 2019 | Kawther Ali Khalaph (u. a.) | Taschenbuch | 108 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200287847 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 117479188
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