Researchers at Arizona State University have developed a new, highly efficient framework for automated glomerular segmentation in 3D with the use of an MRI. The framework requires three phases for processing that are called Hessian based multi-Features Clustering (HmFC) and can accomplish the task of measuring glomerular morphology. In the first phase, a Hessian matrix is created for pre-segmentation purposes. In the second phase, features are extracted from the Hessian matrix and are enhanced with domain knowledge and geometric features. Finally, a Variational Bayesian Gaussian Mixture Model is used for the final segmentation. Accurate results are obtained without destroying the kidney. This innovation advances the physician’s ability to diagnose and treat renal diseases.
Potential Applications
- Early detection of renal diseases
- Medical research
- Industrial applications
Benefits and Advantages
- Early Diagnosis – Allows for early diagnosis of renal disease
- Non-Destructive – Glomerulus can be counted without damaging the kidney
- Technologically Compatible – Framework operates with images from existing MRI equipment
For more information about the inventor(s) and their research, please see
Dr. Teresa Wu's directory webpage