Cantitate/Preț
Produs
Total produse
Vezi coșul
Medical Image Understanding Technology: Artificial Intelligence and Soft-Computing for Image Understanding de Ryszard Tadeusiewicz

Medical Image Understanding Technology: Artificial Intelligence and Soft-Computing for Image Understanding: Studies in Fuzziness and Soft Computing, cartea 156

Autor Ryszard Tadeusiewicz
en Limba Engleză Hardback – 14 mai 2004
A detailed description of a new approach to perceptual analysis and processing of medical images is given. Instead of traditional pattern recognition a new method of image analysis is presented, based on a syntactic description of the shapes selected on the image and graph-grammar parsing algorithms. This method of "Image Understanding" can be found as a model of mans' cognitive image understanding processes. The usefulness for the automatic understanding of the merit of medical images is demonstrated as well as the ability for giving useful diagnostic descriptions of the illnesses. As an application, the production of a content-based, automatically generated index for arranging and for searching medical images in multimedia medical databases is presented.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 70878 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 11 aug 2012 70878 lei  6-8 săpt.
Hardback (1) 71446 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 14 mai 2004 71446 lei  6-8 săpt.

Din seria Studies in Fuzziness and Soft Computing

Preț: 71446 lei

Preț vechi: 75206 lei
-5% Nou

Puncte Express: 1072
Preț estimativ în valută:
13672€ • 14188$ • 11428£

Carte tipărită la comandă

Livrare economică 15-29 martie

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783540219859
ISBN-10: 3540219854
Pagini: 168
Ilustrații: VII, 156 p. 133 illus.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.39 kg
Ediția:2004
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Fuzziness and Soft Computing

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

1. What is Image Understanding Technology and why do we need it?.- 1.1 Methods of Medical Image Acquisition.- 1.2. Analysis and interpretation of medical images.- 1.3. What new values can add to this scheme ‘automatic understanding’ ?.- 1.4. Areas of applications for the automatic understanding of images.- 2. A General Description of the Fundamental Ideas Behind Automatic Image Understanding.- 2.1. Fundamental assumptions.- 2.2. What does image understanding mean?.- 2.3. Linguistic description of images.- 2.4. The use of graph grammar to cognitive resonance.- 3. Formal Bases for the Semantic Approach to Medical Image Processing Leading to Image Understanding Technology.- 3.1 Fundamentals of syntactic pattern recognition methods.- 3.2 Characteristic features and advantages of structural approaches to medical image semantic analysis.- 4. Examples of Structural Pattern Analysis and Medical Image Understanding Application to Medical Diagnosis.- 4.1. Introduction.- 4.2. Pre-processing Methods Designed to Process Selected Medical Images.- 4.3. Making Lexical Elements for the Syntactic Descriptions of Examined structures.- 4.4. Structural Analysis of Coronary Vessels.- 4.4.1 Syntactic Analysis and Diagnosing Coronary Artery Stenoses.- 4.5. Structural Analysis and Understanding of Lesions in Urinary Tract.- 4.6. Syntactic Methods Supporting the Diagnosis of Pancreatitis and Pancreas Neoplasm.- 4.9. Conclusions.- 5. The application of the Image Understanding Technology to Semantic Organisation and Content-Based Searching in Multimedia Medical Data Bases.- 6. Strengths and Weaknesses of the Image Understanding Technology Compared to Previously Known Approaches.- References.

Textul de pe ultima copertă

A detailed description of a new approach to perceptual analysis and processing of medical images is given. Instead of traditional pattern recognition a new method of image analysis is presented, based on a syntactic description of the shapes selected on the image and graph-grammar parsing algorithms. This method of "Image Understanding" can be found as a model of mans’ cognitive image understanding processes. The usefulness for the automatic understanding of the merit of medical images is demonstrated as well as the ability for giving useful diagnostic descriptions of the illnesses. As an application, the production of a content-based, automatically generated index for arranging and for searching medical images in multimedia medical databases is presented.

Caracteristici

New approach for "image understanding" instead of "image recognition" for medical images