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Symmetry Theory Based Classification Algorithm in Brain Computed Tomography Image Database

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In recent years, medical CT images have been extensively applied in clinical diagnosis. CT images can assist physicians to detect pathological changes with more accuracy. Some CT imaging shows that it is approximately symmetrical about the perpendicular bisector, for example brain CT imaging. Based on this medical knowledge guidance, a symmetry theory based classification algorithm in the CT image database is presented in this paper. First of all, weak symmetry and strong symmetry is defined to describe the symmetry from the different granularities. Then, the weak symmetry decision algorithm was given to finish the first-stage classification for medical image in the coarse granularity. Further, the strong symmetry decision algorithm based on the point symmetry is proposed, combining with the weak symmetry decision algorithm, to complete the second-stage classification of the abnormal images classified from the first-stage in finer granularity in order to the locate lesion area. Finally, the features extracted from the lesions are used for the third-stage classification to help the doctor's diagnosis. Experimental results show that symmetry theory based classification algorithm in the CT image database can increase the accuracy of the classification and reduce the time of the doctor's diagnosis.
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Document Type: Research Article

Publication date: February 1, 2016

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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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