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A shape-optimized framework for kidney segmentation in ultrasound images using NLTV denoising and DRLSE.

Yang F, Qin W, Xie Y, Wen T, Gu J - Biomed Eng Online (2012)

Bottom Line: Experimental results on both synthetic images and US data are given to demonstrate the effectiveness and accuracy of the proposed algorithm.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.Furthermore, we can get the final segmentation results with smooth boundary by using the shape prior after the segmentation process.

View Article: PubMed Central - HTML - PubMed

Affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

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.

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Related in: MedlinePlus

Synthetic image and initial shape model. (a) ultrasound noise image; (b) a shape come from the kidney shape space; (c) the mixture image of the noise image and shape image; (d) initial shape model.
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Figure 1: Synthetic image and initial shape model. (a) ultrasound noise image; (b) a shape come from the kidney shape space; (c) the mixture image of the noise image and shape image; (d) initial shape model.

Mentions: For our experiments, several evaluations were performed on synthetic and real US images. The specific synthetic and real US images have 512×512 pixels. The real US images of left kidney were acquired by DC-7 ultrasound machine from Mindray with covex array transducers. So we need do extracted ROI of kidney before using our framework. The synthetic image (c) in Figure1 is mixture of a shape image (b) in Figure1, from our shape space and the noise image (a) in Figure1. The real US images is shown in row (a) of Figure2 and (a) of Figure3. The training set of the shape space was also used in[1]. Our initial shape model was also used in[1], which is shown in image (d) in Figure1. We used 11 eigenvectors to cover the 98.8% variation of the shape space.


A shape-optimized framework for kidney segmentation in ultrasound images using NLTV denoising and DRLSE.

Yang F, Qin W, Xie Y, Wen T, Gu J - Biomed Eng Online (2012)

Synthetic image and initial shape model. (a) ultrasound noise image; (b) a shape come from the kidney shape space; (c) the mixture image of the noise image and shape image; (d) initial shape model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585889&req=5

Figure 1: Synthetic image and initial shape model. (a) ultrasound noise image; (b) a shape come from the kidney shape space; (c) the mixture image of the noise image and shape image; (d) initial shape model.
Mentions: For our experiments, several evaluations were performed on synthetic and real US images. The specific synthetic and real US images have 512×512 pixels. The real US images of left kidney were acquired by DC-7 ultrasound machine from Mindray with covex array transducers. So we need do extracted ROI of kidney before using our framework. The synthetic image (c) in Figure1 is mixture of a shape image (b) in Figure1, from our shape space and the noise image (a) in Figure1. The real US images is shown in row (a) of Figure2 and (a) of Figure3. The training set of the shape space was also used in[1]. Our initial shape model was also used in[1], which is shown in image (d) in Figure1. We used 11 eigenvectors to cover the 98.8% variation of the shape space.

Bottom Line: Experimental results on both synthetic images and US data are given to demonstrate the effectiveness and accuracy of the proposed algorithm.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.Furthermore, we can get the final segmentation results with smooth boundary by using the shape prior after the segmentation process.

View Article: PubMed Central - HTML - PubMed

Affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

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.

Show MeSH
Related in: MedlinePlus