zeki mohammed esraa (15 risultati)

Taschenbuch. Condizione: Neu. Benign/Malignant Tissues Classification | Methods,Concepts and System for Tissue Recognition | Esraa Zeki Mohammed (u. a.) | Taschenbuch | 112 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659328152 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Content Based Image Retrieval | Concepts and Methods for Efficient Retrieval for Medical Images | Esraa Zeki Mohammed (u. a.) | Taschenbuch | 132 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659452871 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Breast cancer has become a major health problem over the last 50 years; it is a leading cause of cancer-related death in women population today. However, if breast abnormalities are detected and diagnoses are made at early stages, studies show that the chances of survival can be greatly improved. In this work, multi-scale fractal dimension is used to derive a set of textural features in order to perform texture analysis on breast tissues samples. The box counting method was used to estimate the multi fractal dimensions. The feed forward neural network is used to classify different types of breast tissues according to the extracted fractal dimension vectors. 112 pp. Englisch.

Condizione: New. Print on Demand pp. 112 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condizione: New. PRINT ON DEMAND pp. 112.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The field of content-based image retrieval (CBIR) focuses on the analysis of image content and the development of tools to represent the visual content in a way that can be efficiently searched and compared. The main aim of this work is to improve the retrieval performance of medical images retrieval methodology that is based on various types of visual features (such as color, texture and shape). The improvement is based on a new multi-level retrieval scheme that deals with different types of medical databases. Within the two phases of the system (i.e., enrollment and retrieval) some new features are introduced. Also, to reach high precision and recall levels, various methods are combined; such as the similarity measures based on Euclidian distance and the artificial neural network trained using back propagation algorithm. 132 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Zeki Mohammed EsraaB.Sc. in Computer Science, University of Mosul, Iraq, M.Sc. and Ph.D. in Computer Science, University of Sulaimani, Iraq. Research interests:Artificial Neural networks, CBIR, Database, Fractals, FPGA, Image Process.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Zeki Mohammed EsraaPh.D. in Computer Science, University of Sulaimani, Iraq, 2013. M.Sc. in Computer Science, University of Sulaimani, Iraq, 2009.B.Sc. in Computer Science, University of Mosul, Iraq, 2000. Research Interest: Artifici.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Breast cancer has become a major health problem over the last 50 years; it is a leading cause of cancer-related death in women population today. However, if breast abnormalities are detected and diagnoses are made at early stages, studies show that the chances of survival can be greatly improved. In this work, multi-scale fractal dimension is used to derive a set of textural features in order to perform texture analysis on breast tissues samples. The box counting method was used to estimate the multi fractal dimensions. The feed forward neural network is used to classify different types of breast tissues according to the extracted fractal dimension vectors.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 112 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Breast cancer has become a major health problem over the last 50 years; it is a leading cause of cancer-related death in women population today. However, if breast abnormalities are detected and diagnoses are made at early stages, studies show that the chances of survival can be greatly improved. In this work, multi-scale fractal dimension is used to derive a set of textural features in order to perform texture analysis on breast tissues samples. The box counting method was used to estimate the multi fractal dimensions. The feed forward neural network is used to classify different types of breast tissues according to the extracted fractal dimension vectors.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The field of content-based image retrieval (CBIR) focuses on the analysis of image content and the development of tools to represent the visual content in a way that can be efficiently searched and compared. The main aim of this work is to improve the retrieval performance of medical images retrieval methodology that is based on various types of visual features (such as color, texture and shape). The improvement is based on a new multi-level retrieval scheme that deals with different types of medical databases. Within the two phases of the system (i.e., enrollment and retrieval) some new features are introduced. Also, to reach high precision and recall levels, various methods are combined; such as the similarity measures based on Euclidian distance and the artificial neural network trained using back propagation algorithm.Books on Demand GmbH, Überseering 33, 22297 Hamburg 132 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The field of content-based image retrieval (CBIR) focuses on the analysis of image content and the development of tools to represent the visual content in a way that can be efficiently searched and compared. The main aim of this work is to improve the retrieval performance of medical images retrieval methodology that is based on various types of visual features (such as color, texture and shape). The improvement is based on a new multi-level retrieval scheme that deals with different types of medical databases. Within the two phases of the system (i.e., enrollment and retrieval) some new features are introduced. Also, to reach high precision and recall levels, various methods are combined; such as the similarity measures based on Euclidian distance and the artificial neural network trained using back propagation algorithm.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.