A hybrid active contour model based on global and local information for medical image segmentation

Fang, Lingling; Qiu, Tianshuang; Zhao, Hongyang; Lv, Fang

doi:10.1007/s11045-018-0578-0

Cited by 24 publications

(16 citation statements)

References 21 publications

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“…It works by, given an initial boundary estimate, iteratively expanding or contracting the estimate to minimize an energy function. Several active contour models have been used to segment and delineate boundaries in different types of medical images [ 18 – 20 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis

et al. 2020

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Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient’s lumbar spine. Our goal is to develop a novel strategy for assessing the extent of Lumbar Spinal Stenosis by automatically calculating these distances from the patient’s lumbar spine MRI. Our method starts with a semantic segmentation of T1- and T2-weighted composite axial MRI images using SegNet that partitions the image into six regions of interest. They consist of three main regions-of-interest, namely the Intervertebral Disc, Posterior Element, and Thecal Sac, and three auxiliary regions-of-interest that includes the Area between Anterior and Posterior elements. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. Nine anatomical landmarks on the image are located by delineating the region boundaries found in the segmented image before the anteroposterior diameter and foraminal widths can be measured. The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine MRI dataset containing MRI studies of 515 patients. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. We found that our method works best when our contour evolution algorithm is applied to improve the accuracy of both the label images used to train the SegNet model and the automatically segmented image. The average error of the calculated right and left foraminal distances relative to their expert-measured distances are 0.28 mm ( p = 0.92) and 0.29 mm ( p = 0.97), respectively. The average error of the calculated anteroposterior diameter relative to their expert-measured diameter is 0.90 mm ( p = 0.92). The method also achieves 96.7% agreement with an expert opinion on determining the severity of the Intervertebral Disc herniations.

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Section: Background and Related Workmentioning

confidence: 99%

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis

et al. 2020

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“…Generally speaking, according to image information used as the formulation of the energy function, the existing ACMs are classified into three categories: edge-based ACMs [3], [5], region-based ACMs [6]- [22], and the mixed ACMs [23]- [32]. The edge-based ACMs use the image gradient to construct an edge stopping function (ESP), which makes the evolution curve to move toward the object boundaries.…”

Section: Introductionmentioning

confidence: 99%

A Novel Active Contour Model Guided by Global and Local Signed Energy-Based Pressure Force

et al. 2020

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Active contour models (ACMs) have been widely applied in the field of image segmentation. However, it is still very challenging to construct an efficient ACM to segment images with intensity inhomogeneity. In this paper, a novel ACM guided by global and local signed energy-based pressure force (GLSEPF) is proposed. First, by computing the energy difference between the inner and outer energies of the evolution curve, a global signed energy-based pressure force (GSEPF) is designed, which can improve the robustness to initial curves. Second, a local signed energy-based pressure force (LSEPF) is introduced by computing the pixel-by-pixel energy difference within local neighborhood region, which can handle images with intensity inhomogeneity and noise. Finally, the global image information and the local energy information are used for the global and local force propagation functions, respectively. The global and local variances are used to automatically balance the weights of the GSEPF and the LSEPF, which can solve the problem of setting parameters. Meanwhile, a regularization term and a penalty term are applied to avoid the re-initialization process during iterations and smooth the level set function. Experimental results on different types of images demonstrate that the proposed model is more robust than the popular region-based and mixed ACMs for segmenting images with intensity inhomogeneity and noise.

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“…Liu et al 23 proposed an edge-based level set method based on multilocal statistical information to handle of the problem of noise too. Fang et al 24 proposed a hybrid ACM based on global and local information, which can segment medical images with noise and had a better performance than some other methods. Aiming at the robustness of flexible initialization and computational efficiency of model, in this article, we proposed an improved RSF model which a global expansion term is added to the RSF model.…”

Section: Introductionmentioning

confidence: 99%

A medical image segmentation method based on hybrid active contour model with global and local features

Li¹,

Wang²

2020

Concurrency and Computation

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Summary In this article, we proposed an improved region‐based active contour model based on curve evolution theory and variational level set method. Our method can be used to segment image with intensity inhomogeneity, such as medical computed tomography images. Our model contains a local intensity fitting term that makes the evolution curve stop at boundaries of the object and a global expansion term that makes the evolution curve have the chance to get to every location in the image. Therefore, our model has a good performance to solve the problem of flexible initialization, which exposed in region‐scalable fitting energy model. For the curvature term that occurs during the calculation, we calculated it with a more efficiency and accuracy method. Compared with other models, our model shows good segmentation result and less computation expense. Finally, we will present some experimental results, especially the result of contrast experiment.

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A hybrid active contour model based on global and local information for medical image segmentation

Cited by 24 publications

References 21 publications

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis

A Novel Active Contour Model Guided by Global and Local Signed Energy-Based Pressure Force

A medical image segmentation method based on hybrid active contour model with global and local features

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