Author:
Yang Fan,Qin Wenjian,Xie Yaoqin,Wen Tiexiang,Gu Jia
Abstract
Abstract
Background
Computer-assisted surgical navigation aims to provide surgeons with anatomical target localization and critical structure observation, where medical image processing methods such as segmentation, registration and visualization play a critical role. Percutaneous renal intervention plays an important role in several minimally-invasive surgeries of kidney, such as Percutaneous Nephrolithotomy (PCNL) and Radio-Frequency Ablation (RFA) of kidney tumors, which refers to a surgical procedure where access to a target inside the kidney by a needle puncture of the skin. Thus, kidney segmentation is a key step in developing any ultrasound-based computer-aided diagnosis systems for percutaneous renal intervention.
Methods
In this paper, we proposed a novel framework for kidney segmentation of ultrasound (US) images combined with nonlocal total variation (NLTV) image denoising, distance regularized level set evolution (DRLSE) and shape prior. Firstly, a denoised US image was obtained by NLTV image denoising. Secondly, DRLSE was applied in the kidney segmentation to get binary image. In this case, black and white region represented the kidney and the background respectively. The last stage is that the shape prior was applied to get a shape with the smooth boundary from the kidney shape space, which was used to optimize the segmentation result of the second step. The alignment model was used occasionally to enlarge the shape space in order to increase segmentation accuracy. Experimental results on both synthetic images and US data are given to demonstrate the effectiveness and accuracy of the proposed algorithm.
Results
We applied our segmentation framework on synthetic and real US images to demonstrate the better segmentation results of our method. From the qualitative results, the experiment results show that the segmentation results are much closer to the manual segmentations. The sensitivity (SN), specificity (SP) and positive predictive value (PPV) of our segmentation result can reach 95%, 96% and 91% respectively; As well as we compared our results with the edge-based level set and level set with shape prior method by means of the same quantitative index, such as SN, SP, PPV, which have corresponding values of 97%, 88%, 78% and 81%, 91%, 80% respectively.
Conclusions
We have found NLTV denosing method is a good initial process for the ultrasound segmentation. This initial process can make us use simple segmentation method to get satisfied results. Furthermore, we can get the final segmentation results with smooth boundary by using the shape prior after the segmentation process. Every step enjoy simple energy model and every step in this framework is needed to keep a good robust and convergence property.
Publisher
Springer Science and Business Media LLC
Subject
Radiology Nuclear Medicine and imaging,Biomedical Engineering,General Medicine,Biomaterials,Radiological and Ultrasound Technology
Reference22 articles.
1. Xie J, Jiang Y, Tsui HT: Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Transaction on Medical Imaging 2005, 24: 1.
2. Noble JA, Boukerroui D: Ultrasound image segmentation: a survey. IEEE Transaction on Medical Imaging 2006, 25: 8.
3. Yezzi A Jr, Tsai A, Willsky A: A statistical approach to snakes for bimodal and trimodal imagery. Kerkyra: Proceedings of IEEE International Conference on Computer Vision; 1999.
4. Leventon M, Grimson E, Faugeras O: Statistical Shape Influence in Geodesic Active Contours. Hilton Head: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; 2000.
5. Medical Image Computing and Computer-Assisted Intervention - MICCAI: 13th International Conference, Beijing, China, September 20–24, 2010, Proceedings, Part III. Lecture Notes in Computer Science 6363. Beijing: Springer; 2010.
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