Paper
20 March 2015 A novel Hessian based algorithm for rat kidney glomerulus detection in 3D MRI
Min Zhang, Teresa Wu, Kevin M. Bennett
Author Affiliations +
Abstract
The glomeruli of the kidney perform the key role of blood filtration and the number of glomeruli in a kidney is correlated with susceptibility to chronic kidney disease and chronic cardiovascular disease. This motivates the development of new technology using magnetic resonance imaging (MRI) to measure the number of glomeruli and nephrons in vivo. However, there is currently a lack of computationally efficient techniques to perform fast, reliable and accurate counts of glomeruli in MR images due to the issues inherent in MRI, such as acquisition noise, partial volume effects (the mixture of several tissue signals in a voxel) and bias field (spatial intensity inhomogeneity). Such challenges are particularly severe because the glomeruli are very small, (in our case, a MRI image is ~16 million voxels, each glomerulus is in the size of 8~20 voxels), and the number of glomeruli is very large. To address this, we have developed an efficient Hessian based Difference of Gaussians (HDoG) detector to identify the glomeruli on 3D rat MR images. The image is first smoothed via DoG followed by the Hessian process to pre-segment and delineate the boundary of the glomerulus candidates. This then provides a basis to extract regional features used in an unsupervised clustering algorithm, completing segmentation by removing the false identifications occurred in the pre-segmentation. The experimental results show that Hessian based DoG has the potential to automatically detect glomeruli,from MRI in 3D, enabling new measurements of renal microstructure and pathology in preclinical and clinical studies.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Min Zhang, Teresa Wu, and Kevin M. Bennett "A novel Hessian based algorithm for rat kidney glomerulus detection in 3D MRI", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94132N (20 March 2015); https://doi.org/10.1117/12.2081484
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Cited by 6 scholarly publications.
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KEYWORDS
Magnetic resonance imaging

Kidney

Sensors

3D image processing

Image processing

3D magnetic resonance imaging

Detection and tracking algorithms

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