University of Twente Student Theses


Texture analysis for quantifying and visualising loss of corticomedullary differentiation

Bruijn, I.L. de and Klepper, M. and Veltmaat, I.D. (2021) Texture analysis for quantifying and visualising loss of corticomedullary differentiation.

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Abstract:Background: Currently, no method is available for non invasive, early diagnosis of chronic kidney disease. Corticomedullary differentiation calculated on native T1 maps is a promising parameter. Purpose: This research aims to find an image-analysis technique that can quantify the loss of corticomedullary differentiation on native T1 maps. Method: Texture analysis, using a grey level co-occurrence matrix, was performed on segmented images from T1 maps of kidneys of 14 healthy volunteers and 15 patients with mild diabetic nephropathy. The first-order features tested include minimum, maximum, mean, median, range, standard deviation, skewness, kurtosis and entropy. The texture features tested include contrast, correlation, homogeneity, and energy. Results: First-order statistics indicate significant differences between groups in standard deviation (p = 0.000), kurtosis (p = 0.000), skewness (p = 0.005) and entropy (p = 0.025). Moreover, a significant difference between groups was found for texture features contrast (p = 0.005) and correlation (p = 0.012). Conclusion: Texture analysis is a promising method to quantify slight changes in corticomedullary differentiation. If explained and substantiated clearly to a nephrologist, the value of contrast and correlation could be used in a clinical setting. Future research should focus on visualizing texture features in feature maps to improve its clinical application and apply texture features to a larger population.
Item Type:Essay (Bachelor)
Faculty:TNW: Science and Technology
Subject:44 medicine
Programme:Technical Medicine BSc (50033)
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