Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of MR images using neural nets
Image and Vision Computing - Special issue: BMVC 1991
Overview and fundamentals of medical image segmentation
Handbook of medical imaging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interpretation of MR images using self-organizing maps and knowledge-based expert systems
Digital Signal Processing
Review of brain MRI image segmentation methods
Artificial Intelligence Review
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Self organization of a massive document collection
IEEE Transactions on Neural Networks
A modified fuzzy C-means algorithm for MR brain image segmentation
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Engineering Applications of Artificial Intelligence
A novel fuzzy Dempster-Shafer inference system for brain MRI segmentation
Information Sciences: an International Journal
Hi-index | 0.00 |
This study presents an image segmentation system that automatically segments and labels T1-weighted brain magnetic resonance (MR) images. The method is based on a combination of unsupervised learning algorithm of the self-organizing maps (SOM) and supervised learning vector quantization (LVQ) methods. Stationary wavelet transform (SWT) is applied to the images to obtain multiresolution information for distinguishing different tissues. Statistical information of the different tissues is extracted by applying spatial filtering to the coefficients of SWT. A multidimensional feature vector is formed by combining SWT coefficients and their statistical features. This feature vector is used as input to the SOM. SOM is used to segment images in a competitive unsupervised approach and an LVQ system is used for fine-tuning. Results are evaluated using Tanimoto similarity index and are compared with manually segmented images. Quantitative comparisons of our system with the other methods on real brain MR images using Tanimoto similarity index demonstrate that our system shows better segmentation performance for the gray matter while it gives average results for white matter.