Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images

Kim, Do Yeon; Park, Jong Won

doi:10.1016/j.imavis.2005.09.005

Cited by 37 publications

(14 citation statements)

References 42 publications

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“…In conclusion, automated hepatic vessel segmentation scheme is recommended for liver surgical planning such as tumor resection and transplantation. In addition, these vessel extraction method combined with liver region segmentation technique could be applicable to extract tree-like organ structures such as carotid [6,20], renal, coronary artery, and airway path from various medical imaging modalities.…”

Section: Discussionmentioning

confidence: 99%

“…While one class includes pixels with intensity values that are below or equal to a certain threshold value, the remaining class includes those pixels with intensity values above the threshold. Correct threshold value selection is crucial for successful segmentation; this selection can be determined interactively or it can be the result of automatic threshold detection method [6]. In this section, we describe the method that automatically determines the threshold value, which is based on the distribution ratio of intensity value within the rectilinear structured grid, to segment the liver regions on the contrast enhanced abdominal CT image sequence.…”

Section: Liver Segmentationmentioning

confidence: 99%

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Hepatic vessel segmentation on contrast enhanced CT image sequence for liver transplantation planning

Kim¹

2013

JBiSE

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The structure and morphology of the hepatic vessels and their relationship between tumors and liver segments are major interests to surgeons for liver surgical planning. In case of living donor liver transplantation (LDLT), the most important step in determining donor suitability is an accurate assessment of the liver volume available for transplantation. In addition, the mutual principles of the procedures include dissection in the appropriate anatomic plane without portal occlusion, minimization of blood loss, and avoidance of injury to the remaining liver. It is essential first step to identify and evaluate the major hepatic vascular structure for liver surgical planning. In this paper, the threshold was determined to segment the liver region automatically based on the distribution ratio of intensity value; and the hepatic vessels were extracted with mathematical morphology transformation, which called hit operation, that is slightly modified version of hit-and-miss operation on contrast enhanced CT image sequence. We identified the vein using the preserved voxel connectivity between two consecutive transverse image sequences, followed by resection into right lobe including right hepatic vein, middle hepatic vein branches andleft lobe including left hepatic vein. An automated hepatic vessel segmentation scheme is recommended for liver surgical planning such as tumor resection and transplantation. These vessel extraction method combined with liver region segmentation technique could be applicable to extract tree-like organ structures such as carotid, renal, coronary artery, and airway path from various medical imaging modalities.

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Section: Discussionmentioning

confidence: 99%

Section: Liver Segmentationmentioning

confidence: 99%

Hepatic vessel segmentation on contrast enhanced CT image sequence for liver transplantation planning

Kim¹

2013

JBiSE

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“…Another application would be to guide surgery and other medical procedures. An example is given in [14], where segmenting a carotid artery is a useful step in medical imaging. Efficiency requirements can also be found in several other areas.…”

Section: Achieving Efficiency and Robustnessmentioning

confidence: 99%

Fast two-step segmentation of natural color scenes using hierarchical region-growing and a color-gradient network

Wangenheim¹,

Bertoldi²,

Abdala³

et al. 2008

J. Braz. Comp. Soc.

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We present evaluation results with focus on combined image and efficiency performance of the Gradient Network Method to segment color images, especially images showing outdoor scenes. A brief review of the techniques, Gradient Network Method and Color Structure Code, is also presented. Different region-growing segmentation results are compared against ground truth images using segmentation evaluation indices Rand and Bipartite Graph Matching. These results are also confronted with other well established segmentation methods (EDISON and JSEG). Our preliminary results show reasonable performance in comparison to several state-of-art segmentation techniques, while also showing very promising results comparatively in the terms of efficiency, indicating the applicability of our solution to real time problems.

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“…A simple yet often effective means for obtaining segmentation of images in which different structures have contrasting intensities is thresholding. Examples of connectivity-based thresholding, which finds a boundary between two regions using the path connection algorithm and changing the threshold adaptively, can be found in 4, 5 . A major limitation of thresholding is that it does not take into account the spatial characteristics of an image and thus is sensitive to the noise, artifacts, and intensity inhomogeneities that can occur in magnetic resonance (MR) images.…”

Section: Introductionmentioning

confidence: 99%

3D Segmentation and Quantification of a Masticatory Muscle from MR Data Using Patient-Specific Models and Matching Distributions

Ong

Liu

et al. 2008

J Digit Imaging

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A method is proposed for 3D segmentation and quantification of the masseter muscle from magnetic resonance (MR) images, which is often performed in pre-surgical planning and diagnosis. Because of a lack of suitable automatic techniques, a common practice is for clinicians to manually trace out all relevant regions from the image slices which is extremely time-consuming. The proposed method allows significant time savings. In the proposed method, a patient-specific masseter model is built from a test dataset after determining the dominant slices that represent the salient features of the 3D muscle shape from training datasets. Segmentation is carried out only on these slices in the test dataset, with shape-based interpolation then applied to build the patient-specific model, which serves as a coarse segmentation of the masseter. This is first refined by matching the intensity distribution within the masseter volume against the distribution estimated from the segmentations in the dominant slices, and further refined through boundary analysis where the homogeneity of the intensities of the boundary pixels is analyzed and outliers removed. It was observed that the left and right masseter muscles' volumes in young adults (28.54 and 27.72 cm(3)) are higher than those of older (ethnic group removed) adults (23.16 and 22.13 cm(3)). Evaluation indicates good agreement between the segmentations and manual tracings, with average overlap indexes for the left and right masseters at 86.6% and 87.5% respectively.

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Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images

Cited by 37 publications

References 42 publications

Hepatic vessel segmentation on contrast enhanced CT image sequence for liver transplantation planning

Hepatic vessel segmentation on contrast enhanced CT image sequence for liver transplantation planning

Fast two-step segmentation of natural color scenes using hierarchical region-growing and a color-gradient network

3D Segmentation and Quantification of a Masticatory Muscle from MR Data Using Patient-Specific Models and Matching Distributions

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