9786200433787 - Segmentation and Classification Algorithms for Brain Tumor Detection: A Novel Approach de Kumar P.S., Shijin (6 resultados)

Taschenbuch. Condición: Neu. Segmentation and Classification Algorithms for Brain Tumor Detection | A Novel Approach | Shijin Kumar P. S. | Taschenbuch | 176 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200433787 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Brain Tumor is a complex disease that occurs due to the abnormal growth of brain cells. For efficient treatment planning, earlier detection of tumor is necessary. Magnetic Resonance Imaging (MRI) is now recognized as an important tool for the detection of Brain tumor. Computer Aided Diagnosis (CAD) could be almost as effective as double reading by providing a second opinion to the radiologist and help in increasing the sensitivity and accuracy of detection. A novel algorithm for brain MRI segmentation using K-Means Clustering and Texture Pattern Matrix is proposed in this work. K-Means clustering with Texture Pattern Matrix (TPM) based segmentation process is implemented to detect Brain Tumor. In this book, Region growing, Watershed and Active Contour Model (ACM) are implemented to authenticate the performance of the proposed method. Fuzzy C-means (FCM) algorithm is also implemented and it is combined with TPM to evaluate the performance of the segmentation algorithm. The parameters used to evaluate the performance of segmentation are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Accuracy, Correlation, Dice Coefficient, and Jaccard Index. 176 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar P.S. ShijinDr. Shijin Kumar P.S, obtained his PhD degree from Noorul Islam University and Master of Engineering in Communication Systems from Anna University, TamilNadu. Currently, he is working as Associate Professor in the de.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Brain Tumor is a complex disease that occurs due to the abnormal growth of brain cells. For efficient treatment planning, earlier detection of tumor is necessary. Magnetic Resonance Imaging (MRI) is now recognized as an important tool for the detection of Brain tumor. Computer Aided Diagnosis (CAD) could be almost as effective as double reading by providing a second opinion to the radiologist and help in increasing the sensitivity and accuracy of detection. A novel algorithm for brain MRI segmentation using K-Means Clustering and Texture Pattern Matrix is proposed in this work. K-Means clustering with Texture Pattern Matrix (TPM) based segmentation process is implemented to detect Brain Tumor. In this book, Region growing, Watershed and Active Contour Model (ACM) are implemented to authenticate the performance of the proposed method. Fuzzy C-means (FCM) algorithm is also implemented and it is combined with TPM to evaluate the performance of the segmentation algorithm. The parameters used to evaluate the performance of segmentation are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Accuracy, Correlation, Dice Coefficient, and Jaccard Index.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 176 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Brain Tumor is a complex disease that occurs due to the abnormal growth of brain cells. For efficient treatment planning, earlier detection of tumor is necessary. Magnetic Resonance Imaging (MRI) is now recognized as an important tool for the detection of Brain tumor. Computer Aided Diagnosis (CAD) could be almost as effective as double reading by providing a second opinion to the radiologist and help in increasing the sensitivity and accuracy of detection. A novel algorithm for brain MRI segmentation using K-Means Clustering and Texture Pattern Matrix is proposed in this work. K-Means clustering with Texture Pattern Matrix (TPM) based segmentation process is implemented to detect Brain Tumor. In this book, Region growing, Watershed and Active Contour Model (ACM) are implemented to authenticate the performance of the proposed method. Fuzzy C-means (FCM) algorithm is also implemented and it is combined with TPM to evaluate the performance of the segmentation algorithm. The parameters used to evaluate the performance of segmentation are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Accuracy, Correlation, Dice Coefficient, and Jaccard Index.