A Combined Fuzzy C-Means and Level Set Method for Automatic DCE-MRI Kidney Segmentation Using Both Population-Based and Patient-Specific Shape Statistics

Abstract

Kidney segmentation from Dynamic Contrast Enhanced Magnetic Resonance Images (DCE-MRI) is a fundamental step for the early detection of transplanted kidney function. This paper presents an accurate and automatic DCE-MRI kidney segmentation method which combines fuzzy c-means (FCM) algorithm and geometric deformable model (level set) method. In order to precisely extract the kidney from its background, the evolution of the level set contour in the proposed method is controlled by the fuzzy memberships of the pixels and both population-based and patient-specific shape model. The FCM algorithm is used to initially divide the input image into kidney and background clusters. The obtained fuzzy clustering membership is used to define the initial contour of the level set method. For segmenting the kidney of a specific patient, a number of high contrast time-point images are segmented constraining the evolution of the level set contour by the population-based shape model constructed from different subjects. As more images are segmented, the patient-specific shape model is built from the obtained segmentation results and gradually used to guide the evolution of the level set contour. The performance of the proposed method is evaluated on 40 subjects. Experimental results demonstrate the efficiency, consistency, and accuracy of the proposed method especially for low contrast images.