Ms. Shan Gao Profile

Shan Gao

Doctoral Candidate at Leiden Univ Medical Ctr

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Publications (1)

Proceedings Article | 20 March 2015 Paper

Evaluating intensity normalization for multispectral classification of carotid atherosclerotic plaque

Shan Gao, Ronald van’t Klooster, Diederik van Wijk, Aart Nederveen, Boudewijn Lelieveldt, Rob van der Geest

Proceedings Volume 9413, 94131T (2015) https://doi.org/10.1117/12.2080961

KEYWORDS: Tissues, Image segmentation, Magnetic resonance imaging, Image classification, Particle filters, Image analysis, Neodymium, In vivo imaging, Mahalanobis distance, Pattern recognition

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Intensity normalization is an important preprocessing step for automatic plaque analysis in MR images as most segmentation algorithms require the images to have a standardized intensity range. In this study, we derived several intensity normalization approaches with inspiration from expert manual analysis protocols, for classification of carotid vessel wall plaque from in vivo multispectral MRI. We investigated intensity normalization based on a circular region centered at lumen (nCircle); based on sternocleidomastoid muscle (nSCM); based on intensity scaling (nScaling); based on manually classified fibrous tissue (nManuFibrous) and based on automatic classified fibrous tissue (nAutoFibrous). The proposed normalization methods were evaluated using three metrics: (1) Dice similarity coefficient (DSC) between manual and automatic segmentation obtained by classifiers using different normalizations; (2) correlation between proposed normalizations and normalization used by expert; (3) Mahalanobis Distance between pairs of components. In the performed classification experiments, features of normalized image, smoothed, gradient magnitude and Laplacian images at multi-scales, distance to lumen, distance to outer wall, wall thickness were calculated for each vessel wall (VW) pixel. A supervised pattern recognition system, based on a linear discriminate classifier, was trained using the manual segmentation result to classify each VW pixel to be one of the four classes: fibrous tissue, lipid, calcification, and loose matrix according to the highest posterior probability. We evaluated our method on image data of 23 patients. Compared to the result of conventional square region based intensity normalizatio n, nScaling resulted in significant increase in DSC for lipid (p = 0.006) and nAutoFibrous resulted in significant increase in DSC for calcification (p = 0.004). In conclusion, it was demonstrated that the conventional region based normalization approach is not optimal and nAutoFibrous and nScaling are promising approaches deserving further studies.

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