Abstract
Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the system.
Highlights
Medical image segmentation is nowadays at the core of medical image analysis and supports e.g. computer-aided diagnosis, surgical planning, intra-operative guidance or postoperative assessment
Motivated by practical problems of image segmentation in the medical field, we present in this paper a Graphics Processing Units (GPU) framework based on explicit discrete deformable models, implemented over the NVidia Compute Unified Device Architecture (CUDA) architecture, aimed for the segmentation of volumetric images
Our GPU segmentation framework was evaluated under real conditions for the segmentation of the hip joint bones, i.e. femur and hip bone, from Magnetic Resonance Imaging (MRI) (Figs. 7(a)–7(d))
Summary
Medical image segmentation is nowadays at the core of medical image analysis and supports e.g. computer-aided diagnosis, surgical planning, intra-operative guidance or postoperative assessment. Segmentation attracts the interest of the Computer Graphics community, by supporting e.g. visualization of medical data sets. The large variety of image modalities with associated artifacts, the variability of the structures to segment and the strong demanded requirements (e.g., high accuracy, automation) seriously hinder the design of efficient segmentation methods. In this context, the use of interactive and fast segmentation approaches can expedite tedious parameter tuning and reduce the limitations of segmentation methods since interactive control is available [22]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
