image processing computer aided (11 risultati)

Hardcover. Condizione: Very Good. 1st Edition. 442 pages, a generally very good hardback, many text-figures, ex-library [0123333652].

Condizione: New. In.

Hardcover. 441 S. Good condition. The reading pages are clean and unmarked. Slight signs of storage and use. Retired library copy with corresponding labelling. Without dust jacket. Otherwise a good copy. Sprache: Englisch Gewicht in Gramm: 1500.

Taschenbuch. Condizione: Neu. KIDNEY CANCER DETECTION USING IMAGE PROCESSING TECHNIQUES | IMPLEMENTATION OF COMPUTER AIDED DIAGNOSIS FOR KIDNEY CANCER DETECTION | Subraja R. (u. a.) | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207841776 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Breast Cancer Detection Using Image Processing and Electromagnetics | A New Computer Aided Detection Technique with the Aid of Electromagnetic Waves | Dina A. Ragab | Taschenbuch | 140 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659597220 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Computational Intelligence in Computer Aided Medical Diagnosis | Cognitive clinical decision-making with Neuro-Fuzzy-Genetic systems, Weight initialization and Advanced image processing techniques | Latha Parthiban | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783843371445 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Rijksuniversiteit Utrecht, 1991. 492 pp., 355 ills. in b/w, small 4to, orig. wrappers.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic procedures for early detection and diagnosis are crucial. Some difficulties with manual segmentation have necessitated the use of deep learning models to assist clinicians in effectively recognizing and segmenting cancer. Probabilistic Convolutional Neural Network (PCNN) particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images. In this project, image filtering on MRI kidney images is carried out using Bilateral Anisotropic Diffusion Filter algorithm. This proposed preprocessing technique provides high Peak Signal to Noise Ratio) PSNR and low Mean Square Error (MSE). Image enhancement on MRI kidney images is carried out using Edge Preservation-Contrast Limited Adaptive Histogram Equalization (EP-CLAHE) algorithm. The EP-CLAHE is used to improve contrast and brightness. MRI kidney image segmentation is carried out using Improved Fast Fuzzy C Means Clustering (IFFCMC) algorithm. IFFCMC is used to segment on the kidney cancer pixels and suppress other pixels on MRI kidney image.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Breast cancer is one of the most common kinds of cancer, as well as the leading cause of decease among women. Early detection and diagnosis of breast cancer increases the chances for successful treatment and complete recovery for the patient. Mammography is currently the most sensitive method to detect early breast cancer; however, the magnetic resonance imaging is the most attractive alternative to mammogram. Manual readings of mammograms may result in misdiagnosis due to human errors caused by visual fatigue. Computer aided detection systems serve as a second opinion for radiologists. The system classifies normal and abnormal tissues in addition to benign and malignant tumors. A further investigation was implemented using electromagnetic waves instead of the classical MRI approach. A breast model was generated and near field data of electromagnetic waves were extracted to detect the abnormalities in the breast.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a comprehensive treatment of methodologies underlying fuzzy logic, genetic algorithm, artificial neural networks and neuro-fuzzy hybrid for solving medical diagnostic problems like cancer, skin disease etc. Expert systems have been built to perform clinical decision-making functions, but the knowledge rules extracted from human experts generally have uncertain and ambiguous characteristics. To handle uncertainties in symptoms, descriptions and data, fuzzy logic is used with neural networks. Artificial neural networks provide aspects such as learning, adaptation and generalization that aid fuzzy logic inference under cognitive uncertainty. The neuro-fuzzy inference engine uses a weighed average of the premise and consequent parameters with the fuzzy rule serving as node and fuzzy sets representing the weight of the nodes. The image from which feature is to be extracted must be preprocessed so that significant features are not disturbed and then neuro-fuzzy-genetic hybrid can be used for making decisions. This monograph will appeal to students, researchers and R&D professionals who need the state-of-art introduction into this challenging and exciting young field.