A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis
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| Title | A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis |
| Authors | |
| Abstract | Medical imaging mainly manages and processes missing, ambiguous, complementary, redundantand distorted data and information has a strong structural character. This paper reports a new(semi)automated and supervised method for the segmentation of brain structures using a rule-basedfuzzy system and data fusion. In the field of biomedical image analysis fuzzy logic acts as aunified framework for representing and processing both numerical and symbolic information, aswell as structural information constituted mainly by spatial relationships. Applications developedare for the segmentation of brain structures in magnetic resonance (MR) and CT (computertomography) images, based both on atlas and real data. Promising results show the superiority ofthis knowledge-based approach over best traditional techniques in terms of segmentation errors.The quantitative assessment of this method is made by comparing manually and automatic segmentedbrain structures by using some indexes evaluating the accuracy of contour detection and spatiallocation. Though the proposed methodology has been implemented and successfully used formodel-driven in the domain of MR and CT imaging, this method is general and may be applied toany imagistic object that can be expressed by expert knowledge and morphological images. |
| Publisher | Agora University of Oradea |
| Date | 2013-04-11 |
| Source | International Journal of Computers Communications & Control Vol 8, No 2 (2013): INT J COMPUT COMMUN |
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