Abdominal CT images are commonly used for the diagnosis of kidney diseases. With the advances of CT technology,
processing of CT images has become a challenging task mainly because of the large number of CT images being studied.
This paper presents a texture-learning based system for the three-dimensional (3D) segmentation of renal parenchyma in
abdominal CT images. The system is designed to automatically delineate renal parenchyma and is based on the texturelearning
and the region-homogeneity-based approaches. The first approach is achieved with the texture analysis using the
gray-level co-occurrence matrix (GLCM) features and an artificial neural network (ANN) to determine if a pixel in the
CT image is likely to fall within the renal parenchyma. The second approach incorporates a two-dimensional (2D) region
growing to segment renal parenchyma in single CT image slice and a 3D region growing to propagate the segmentation
results to neighboring CT image slices. The criterion for the region growing is a test of region-homogeneity which is
defined by examining the ANN outputs. In system evaluation, 10 abdominal CT image sets were used. Automatic
segmentation results were compared with manually segmentation results using the Dice similarity coefficient. Among
the 10 CT image sets, our system has achieved an average Dice similarity coefficient of 0.87 that clearly shows a high
correlation between the two segmentation results. Ultimately, our system could be incorporated in applications for the
delineation of renal parenchyma or as a preprocessing in a CAD system of kidney diseases.