论文信息 - 3D segmentation and labeling using self-organizing Kohonen network for volumetric measurements on brain CT imaging to quantify TBI recovery

3D segmentation and labeling using self-organizing Kohonen network for volumetric measurements on brain CT imaging to quantify TBI recovery

In this paper, we present a new system to segment and label CT brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) patients that can accurately describe the morphological changes in the brain as recovery progresses. Segmentation is performed by assigning a feature pattern to each voxel, consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is input to Kohonen network for an unsupervised classification of the voxels into regions.

A. A. Farag | Mohamed Nooman Ahmed | A. Farag | M. N. Ahmed

[1] D. Kennedy,et al. Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging. , 1989, IEEE transactions on medical imaging.

[2] Lawrence O. Hall,et al. Knowledge-based classification and tissue labeling of MR images of human brain , 1993, IEEE Trans. Medical Imaging.

[3] S P Raya,et al. Low-level segmentation of 3-D magnetic resonance brain images-a rule-based system. , 1990, IEEE transactions on medical imaging.

[4] Silvana G. Dellepiane,et al. Model generation and model matching of real images by a fuzzy approach , 1992, Pattern Recognit..

[5] Max A. Viergever,et al. Scale and the differential structure of images , 1992, Image Vis. Comput..

[6] Hongyi Li,et al. Object recognition in brain CT-scans: knowledge-based fusion of data from multiple feature extractors , 1995, IEEE Trans. Medical Imaging.