The generation of anatomical models is one the most important concern to biomedical researchers as well as to medical doctors, due to needed to understand the human tissues. Is know that the soft tissues like heart, b...
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The generation of anatomical models is one the most important concern to biomedical researchers as well as to medical doctors, due to needed to understand the human tissues. Is know that the soft tissues like heart, brain, prostate and hard tissues like jaw, bones, skull, etc are structures of complex morphologies, so, the anatomical models generation is not an easy and trivial task. Currently, this task has benefited of advances of imaging diagnostic, which permit obtain cross and longitudinal sections of human body. In this research, we describe a method to obtain 3D discrete models of human body given by a dataset of medical images. Five main modules were implemented in prototype software: (1) Reading and 3D reconstruction of Computerized Axial Tomography and Magnetic Resonance Images. (2) Preprocessing techniques for improve the low medical images quality by using enhancement algorithms to reduce image noise and to increase structures contrast. (3) Combined segmentation techniques for tissue identification, which were applied through a multi-stage approach. (4) Post processing techniques to improve segmented volumes and (5) Exportation task of volumes to readable formats by Computer Aided Design (CAD) tools to be later analyzed by numerical methods. The performance of our method is shown on several medical examples and the techniques were validated using statistical descriptors to compare our models with models from free databases. Results showed that the implemented techniques generate precise and useful models for numerical analysis and medical survey, planning and surgery in a short processing time. (C) 2010 CIMNE (UPC). Published by Elsevier Espana, S.L. All rights reserved.
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