ISBN: 9783659689765 - intelligent breast cancer identification system (4 resultados)

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book aims to develop an intelligent breast cancer identification (ICBIS) system based on image processing techniques and neural network classifier. Recently, many researchers have developed image recognition systems for classifying breast cancer tumors using different image processing and classification techniques. The challenge is the extraction of the real features that distinguish the benign and malignant tumor. The classifications of breast cancer images have been performed using the shape and texture characteristics of the images. The asymmetry, roundness, intensity levels and more are the exact shape and texture features that distinguish the two types of breast tumors. Image processing techniques are used in order to detect tumor and extract the region of interest from the mammogram. The following data processing operations have been done for detection of images: thresholding, filtering and adjustments, canny edge detection, and some morphological operations. Shape and texture features are then extracted using GLCM (Gray-Level Co-Occurrence Matrix) algorithm in order to accurately classify the mammograms into normal, benign, and malignant tumors. 96 pp. Englisch.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book aims to develop an intelligent breast cancer identification (ICBIS) system based on image processing techniques and neural network classifier. Recently, many researchers have developed image recognition systems for classifying breast cancer tumors using different image processing and classification techniques. The challenge is the extraction of the real features that distinguish the benign and malignant tumor. The classifications of breast cancer images have been performed using the shape and texture characteristics of the images. The asymmetry, roundness, intensity levels and more are the exact shape and texture features that distinguish the two types of breast tumors. Image processing techniques are used in order to detect tumor and extract the region of interest from the mammogram. The following data processing operations have been done for detection of images: thresholding, filtering and adjustments, canny edge detection, and some morphological operations. Shape and texture features are then extracted using GLCM (Gray-Level Co-Occurrence Matrix) algorithm in order to accurately classify the mammograms into normal, benign, and malignant tumors.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Helwan AbdulkaderMr. Abdulkader Helwan was born in Tripoli/Lebanon. He studied in Lebanon at Al Manar University of Tripoli where he successfully completed his degree:a B.E. in Biomedical engineering. As a scholar he continued for an.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book aims to develop an intelligent breast cancer identification (ICBIS) system based on image processing techniques and neural network classifier. Recently, many researchers have developed image recognition systems for classifying breast cancer tumors using different image processing and classification techniques. The challenge is the extraction of the real features that distinguish the benign and malignant tumor. The classifications of breast cancer images have been performed using the shape and texture characteristics of the images. The asymmetry, roundness, intensity levels and more are the exact shape and texture features that distinguish the two types of breast tumors. Image processing techniques are used in order to detect tumor and extract the region of interest from the mammogram. The following data processing operations have been done for detection of images: thresholding, filtering and adjustments, canny edge detection, and some morphological operations. Shape and texture features are then extracted using GLCM (Gray-Level Co-Occurrence Matrix) algorithm in order to accurately classify the mammograms into normal, benign, and malignant tumors.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.…