Interactive 3D GPU-Based Breast Mass Lesion Segmentation Method Based on Level Sets for Dce-MRI Images

A new method for the segmentation of 3D breast lesions in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images, using parallel programming with general purpose computing on graphics processing units (GPGPUs), is proposed. The method has two main parts: a pre-processing step and a segmentation algorithm. In the pre-processing step, DCE-MRI images are registered using an intensity-based rigid transformation algorithm based on gradient descent. After the registration, voxels that correspond to breast lesions are enhanced using the Naïve Bayes machine learning classifier. This classifier is trained to identify four different classes inside breast images: lesion, normal tissue, chest and background. Training is performed by manually selecting 150 voxels for each of the four classes from images in which breast lesions have been confirmed by an expert in the field. Thirteen attributes obtained from the kinetic curves of the selected voxels are later used to train the classifier. Finally, the classifier is used to increase the intensity values of voxels labeled as lesions and to decrease the intensities of all other voxels. The post-processed images are used for volume segmentation of the breast lesions using a level set method based on the active contours without edges (ACWE) algorithm. The segmentation algorithm is implemented in OpenCL for the GPGPUs to accelerate the original model by parallelizing two main steps of the segmentation process: the computation of the signed distance function (SDF) and the evolution of the segmented curve. The proposed framework uses OpenGL to display the segmented volume in real time, allowing the physician to obtain immediate feedback on the current segmentation progress. The proposed implementation of the SDF is compared with an optimal implementation developed in Matlab and achieves speedups of 25 and 12 for 2D and 3D images, respectively. Moreover, the OpenCL implementation of the segmentation algorithm is compared with an optimal implementation of the narrow-band ACWE algorithm. Peak speedups of 55 and 40 are obtained for 2D and 3D images, respectively. The segmentation algorithm has been developed as open source software, with different versions for 2D and 3D images, and can be used in different areas of medical imaging as well as in areas within computer vision, such like tracking, robotics and navigation.