Numerical estimation of the size of the kidney is useful in evaluating conditions of the kidney, especially, when
serial MR imaging is performed to evaluate the kidney function. This paper presents a new method for automatic
segmentation of the kidney in three-dimensional (3D) MR images, by extracting texture features and statistical matching
of geometrical shape of the kidney. A set of Wavelet-based support vector machines (W-SVMs) is trained on the MR
images. The W-SVMs capture texture priors of MRI for classification of the kidney and non-kidney tissues in different
zones around the kidney boundary. In the segmentation procedure, these W-SVMs are trained to tentatively label each
voxel around the kidney model as a kidney or non-kidney voxel by texture matching. A probability kidney model is
created using 10 segmented MRI data. The model is initially localized based on the intensity profiles in three directions.
The weight functions are defined for each labeled voxel for each Wavelet-based, intensity-based, and model-based label.
Consequently, each voxel has three labels and three weights for the Wavelet feature, intensity, and probability model.
Using a 3D edge detection method, the model is re-localized and the segmented kidney is modified based on a region
growing method in the model region. The probability model is re-localized based on the results and this loop continues
until the segmentation converges. Experimental results with mouse MRI data show the good performance of the
proposed method in segmenting the kidney in MR images.