Synopsis
The main objective of this work is to propose efficient ROI based hybrid compression models that can efficiently extract the ROI by segmentation and compress medical images very effectively with good level of visual quality. The work proposes a multiple approach of extracting the ROI such as the sequence of morphological operations for MR Brain images, gradient based approaches and morphological operations for CT Abdomen images and ultra-contour models & structural edge detectors for CT Lung images.To improve the performance of the compression the Convolutional Neural Network (CNN) based segmentation method is used for ROI extraction of MR brain images and the extracted region is compressed with BPT (Binary Plane Technique) operated in both lossy and lossless for NROI and ROI. It is found that the DNN (Deep Neural Network) approach is attained better segmentation efficiency when compared with Satheesh’s approach in terms of accuracy, similarity index and correct detection ratio. The efficiency is improved by 6% than earlier methods.
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Effective Hybrid Compression Model for Medical Images
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LAP LAMBERT Academic Publishing Aug 2020, 2020
ISBN 10: 6202797622
ISBN 13: 9786202797627
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The main objective of this work is to propose efficient ROI based hybrid compression models that can efficiently extract the ROI by segmentation and compress medical images very effectively with good level of visual quality. The work proposes a multiple approach of extracting the ROI such as the sequence of morphological operations for MR Brain images, gradient based approaches and morphological operations for CT Abdomen images and ultra-contour models & structural edge detectors for CT Lung images.To improve the performance of the compression the Convolutional Neural Network (CNN) based segmentation method is used for ROI extraction of MR brain images and the extracted region is compressed with BPT (Binary Plane Technique) operated in both lossy and lossless for NROI and ROI. It is found that the DNN (Deep Neural Network) approach is attained better segmentation efficiency when compared with Satheesh's approach in terms of accuracy, similarity index and correct detection ratio. The efficiency is improved by 6% than earlier methods. 124 pp. Englisch. Seller Inventory # 9786202797627
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Effective Hybrid Compression Model for Medical Images
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202797622
ISBN 13: 9786202797627
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar B. P. SantoshB. P. Santosh Kumar, Assistant Professor, Department of ECE, YSR Engineering College of YVU, Proddatur, India. He received the B.Tech. degree from JNTU Hyderabad, India, the M.Tech. degree from Kerala University, T. Seller Inventory # 494133276
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Effective Hybrid Compression Model for Medical Images
Published by
LAP LAMBERT Academic Publishing Aug 2020, 2020
ISBN 10: 6202797622
ISBN 13: 9786202797627
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Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The main objective of this work is to propose efficient ROI based hybrid compression models that can efficiently extract the ROI by segmentation and compress medical images very effectively with good level of visual quality. The work proposes a multiple approach of extracting the ROI such as the sequence of morphological operations for MR Brain images, gradient based approaches and morphological operations for CT Abdomen images and ultra-contour models & structural edge detectors for CT Lung images.To improve the performance of the compression the Convolutional Neural Network (CNN) based segmentation method is used for ROI extraction of MR brain images and the extracted region is compressed with BPT (Binary Plane Technique) operated in both lossy and lossless for NROI and ROI. It is found that the DNN (Deep Neural Network) approach is attained better segmentation efficiency when compared with Satheesh's approach in terms of accuracy, similarity index and correct detection ratio. The efficiency is improved by 6% than earlier methods.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch. Seller Inventory # 9786202797627
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Effective Hybrid Compression Model for Medical Images
Published by
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202797622
ISBN 13: 9786202797627
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The main objective of this work is to propose efficient ROI based hybrid compression models that can efficiently extract the ROI by segmentation and compress medical images very effectively with good level of visual quality. The work proposes a multiple approach of extracting the ROI such as the sequence of morphological operations for MR Brain images, gradient based approaches and morphological operations for CT Abdomen images and ultra-contour models & structural edge detectors for CT Lung images.To improve the performance of the compression the Convolutional Neural Network (CNN) based segmentation method is used for ROI extraction of MR brain images and the extracted region is compressed with BPT (Binary Plane Technique) operated in both lossy and lossless for NROI and ROI. It is found that the DNN (Deep Neural Network) approach is attained better segmentation efficiency when compared with Satheesh's approach in terms of accuracy, similarity index and correct detection ratio. The efficiency is improved by 6% than earlier methods. Seller Inventory # 9786202797627
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Effective Hybrid Compression Model for Medical Images
Published by
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202797622
ISBN 13: 9786202797627
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Taschenbuch. Condition: Neu. Effective Hybrid Compression Model for Medical Images | B. P. Santosh Kumar | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202797627 | Verantwortliche Person für die EU: LAP Lambert Academic Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Seller Inventory # 119049461