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,redundant and distorted data and information has a strong structural character. Thispaper reports a new (semi)automated and supervised method for the segmentationof brain structures using a rule-based fuzzy system. In the field of biomedical imageanalysis fuzzy logic acts as a unified framework for representing and processing bothnumerical and symbolic information, as well as structural information constitutedmainly by spatial relationships. The developed application is for the segmentationof brain structures in CT (computer tomography) images. Promising results showthe superiority of this knowledge-based approach over best traditional techniques interms of segmentation errors. The quantitative assessment of this method is madeby comparing manually and automatic segmented brain structures by using someindexes evaluating the accuracy of contour detection and spatial location. Thoughthe proposed methodology has been implemented and successfully used for modeldrivenin medical imaging, it is general enough and may be applied to any imagisticobject that can be expressed by expert knowledge and morphological images. |
| Publisher | Agora University of Oradea |
| Date | 2013-02-18 |
| Source | International Journal of Computers Communications & Control Vol 8, No 2 (2013): INT J COMPUT COMMUN |
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