Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Medical Imaging 2001: Image Processing
Medical Imaging 2001: Image Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Fuzzy relations applied to minimize over segmentation in watershed algorithms
Pattern Recognition Letters
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Computer Methods and Programs in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
Automatic closed edge detection using level lines selection
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Watershed segmentation of intervertebral disk and spinal canal from MRI images
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Heterogeneous computing for vertebra detection and segmentation in x-ray images
Journal of Biomedical Imaging - Special issue on Parallel Computation in Medical Imaging Applications
Personalized identification of abdominal wall hernia meshes on computed tomography
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
This paper presents a unified framework for automatic segmentation of intervertebral disks of scoliotic spines from different types of magnetic resonance (MR) image sequences. The method exploits a combination of statistical and spectral texture features to discriminate closed regions representing intervertebral disks from background in MR images of the spine. Specific texture features are evaluated for three types of MR sequences acquired in the sagittal plane: 2-D spin echo, 3-D multiecho data image combination, and 3-D fast imaging with steady state precession. A total of 22 texture features (18 statistical and 4 spectral) are extracted from every closed region obtained from an automatic segmentation procedure based on the watershed approach. The feature selection step based on principal component analysis and clustering process permit to decide among all the extracted features which ones resulted in the highest rate of good classification. The proposed method is validated using a supervised k-nearest-neighbor classifier on 505 MR images coming from three different scoliotic patients and three differentMRacquisition protocols. Results suggest that the selected texture features and classification can contribute to solve the problem of oversegmentation inherent to existing automatic segmentation methods by successfully discriminating intervertebral disks from the background on MRI of scoliotic spines.