Alzheimer's Disease Estimation | A Pattern Recognition based Approach using MRI and Genotype Data

Saima Farhan (u. a.)

ISBN 10: 3659772178 / ISBN 13: 9783659772177

Published by LAP LAMBERT Academic Publishing, 2015

About this Item

Alzheimer's Disease Estimation | A Pattern Recognition based Approach using MRI and Genotype Data | Saima Farhan (u. a.) | Taschenbuch | 164 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659772177 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 104227469

Bibliographic Details

• Title: Alzheimer's Disease Estimation | A Pattern Recognition based Approach using MRI and Genotype Data
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2015
• Language: English
• ISBN 10: 3659772178
• ISBN 13: 9783659772177
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 262 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Alzheimer’s disease, a progressive neurodegenerative disorder of the brain, is a major cause of death around the globe. Clinical symptoms of the disease appear at later stages, however, modern brain imaging techniques have enabled us to non-invasively visualize the internal structures of the brain and identify structural and functional changes well before time. Early detection of the disease is crucial for the patient, care givers and relatives to cope with the situation as well as for medical practitioners to discover new drugs. A novel image processing based approach is proposed for early identification of Alzheimer’s disease from MRI of the brain. Features used are different tissue densities and size of hippocampus. Seven classification models are used for identification of patients and controls. Results are obtained using image features, genetic features and combination of the both. Image features produced best classification of cases and controls. On the other hand, genetic data can be very useful in predicting the risk of disease. The proposed approach has achieved higher accuracy/specificity/sensitivity values even using smaller feature set.

About the Author

Saima Farhan PhD, is working as assistant professor in the Department of Computer Science, Lahore College for Women University, Lahore, Pakistan, since last 10 years. Her research interests include image processing, machine learning and medical image analysis. She has authored two books and many international conference and journal research papers.