Improving Anemic RBCs Images Recognition: Improving Anemic RBCs Recognition Using Spatial Spectral Statistical Features and Transformative Features With RBFNN - Softcover
Synopsis
In this research a new anemic red blood cells (RBCs) data set was collected and used to investigate the automatic recognition of the RBCs. It includes the required processes to extract Spatio- Statistical Spectral features of the RBCs and to utilize these features to recognize between normal RBCs and the other seven groups of abnormal RBCs from five most dominant types of anemic RBCs in Malaysia. Three sets of features were used and tested: geometrical,colo and textural. The suggested RBCs recognition scheme consists of four stages after data acquisition. The first one is to determine the background and target colors, isolating the cell target area from the surrounding and tracing the external and internal boundaries of central pallor area pixels of the cell cut-out. Second is to determine the adopted geometrical features by using the trace points, such as: Fourier descriptors, aspect ratio and moments, which have been used to describe the shapes of RBCs. Some textural features were also computed to evaluate the spatial color variation within the RBCs. PCA technique was applied in order to remove redundant features that affected the classification accuracy and training time.
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About the Author
Dr. Jameela Ali Alkrimi Lecturer in IT and communication at College of Dentistry, Babylon Univ., Babylon, Iraq. Doctorate in IT & Communication, UNITEN, 2015. M.Sc IT. Univ. of IT & Communications, 2006. Diploma in IT. Univ. of IT & Communications, 2000. B.Sc (Statistics Science) college of Administration & Economic, Baghdad Univ.,1987.
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Improving Anemic RBCs Images Recognition
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this research a new anemic red blood cells (RBCs) data set was collected and used to investigate the automatic recognition of the RBCs. It includes the required processes to extract Spatio- Statistical Spectral features of the RBCs and to utilize these features to recognize between normal RBCs and the other seven groups of abnormal RBCs from five most dominant types of anemic RBCs in Malaysia. Three sets of features were used and tested: geometrical,colo and textural. The suggested RBCs recognition scheme consists of four stages after data acquisition. The first one is to determine the background and target colors, isolating the cell target area from the surrounding and tracing the external and internal boundaries of central pallor area pixels of the cell cut-out. Second is to determine the adopted geometrical features by using the trace points, such as: Fourier descriptors, aspect ratio and moments, which have been used to describe the shapes of RBCs. Some textural features were also computed to evaluate the spatial color variation within the RBCs. PCA technique was applied in order to remove redundant features that affected the classification accuracy and training time. 268 pp. Englisch. Seller Inventory # 9783659974311
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Improving Anemic RBCs Images Recognition | Improving Anemic RBCs Recognition Using Spatial Spectral Statistical Features and Transformative Features With RBFNN
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Taschenbuch. Condition: Neu. Improving Anemic RBCs Images Recognition | Improving Anemic RBCs Recognition Using Spatial Spectral Statistical Features and Transformative Features With RBFNN | Jameela Ali Alkrimi (u. a.) | Taschenbuch | 268 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783659974311 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 109108062
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Improving Anemic RBCs Images Recognition
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this research a new anemic red blood cells (RBCs) data set was collected and used to investigate the automatic recognition of the RBCs. It includes the required processes to extract Spatio- Statistical Spectral features of the RBCs and to utilize these features to recognize between normal RBCs and the other seven groups of abnormal RBCs from five most dominant types of anemic RBCs in Malaysia. Three sets of features were used and tested: geometrical,colo and textural. The suggested RBCs recognition scheme consists of four stages after data acquisition. The first one is to determine the background and target colors, isolating the cell target area from the surrounding and tracing the external and internal boundaries of central pallor area pixels of the cell cut-out. Second is to determine the adopted geometrical features by using the trace points, such as: Fourier descriptors, aspect ratio and moments, which have been used to describe the shapes of RBCs. Some textural features were also computed to evaluate the spatial color variation within the RBCs. PCA technique was applied in order to remove redundant features that affected the classification accuracy and training time.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 268 pp. Englisch. Seller Inventory # 9783659974311
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Improving Anemic RBCs Images Recognition : Improving Anemic RBCs Recognition Using Spatial Spectral Statistical Features and Transformative Features With RBFNN
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this research a new anemic red blood cells (RBCs) data set was collected and used to investigate the automatic recognition of the RBCs. It includes the required processes to extract Spatio- Statistical Spectral features of the RBCs and to utilize these features to recognize between normal RBCs and the other seven groups of abnormal RBCs from five most dominant types of anemic RBCs in Malaysia. Three sets of features were used and tested: geometrical,colo and textural. The suggested RBCs recognition scheme consists of four stages after data acquisition. The first one is to determine the background and target colors, isolating the cell target area from the surrounding and tracing the external and internal boundaries of central pallor area pixels of the cell cut-out. Second is to determine the adopted geometrical features by using the trace points, such as: Fourier descriptors, aspect ratio and moments, which have been used to describe the shapes of RBCs. Some textural features were also computed to evaluate the spatial color variation within the RBCs. PCA technique was applied in order to remove redundant features that affected the classification accuracy and training time. Seller Inventory # 9783659974311
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Improving Anemic RBCs Images Recognition: Improving Anemic RBCs Recognition Using Spatial Spectral Statistical Features and Transformative Features With RBFNN
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