Medical Image Segmentation Algorithms using Deformable Models: A Review

Jayadevappa, D.; Kumar, S. Srinivas; Murty, D. S.

doi:10.4103/0256-4602.81244

Cited by 67 publications

(30 citation statements)

References 38 publications

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“…Therefore, similar to the region growing technique, model-based methods are also semi-automatic [4]. Both of these techniques are error sensitive due to the unsuitable and false description of initial plans, and wrong choice of the seed points.…”

Section: Model-based Methodsmentioning

confidence: 98%

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Image Segmentation Methods and Applications in MRI Brain Images

Yazdani

Yusof

Karimian

et al. 2015

IETE Technical Review

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Medical image segmentation plays an important role in medical-imaging applications and they provide a large amount of functional and anatomical information, which improve and facilitate diagnosis and disease therapy planning. However, the existence of image artifacts, such as intensity inhomogeneity, noise and partial volume in magnetic resonance images (MRIs), can adversely affect the quantitative image analysis. There are different segmentation methods in the literature, which segment brain MRI into white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). However, there is not a common algorithm that can be used for all types of images. We present a critical appraisal of the current status of techniques for MRI segmentation. In this paper, commonly used segmentation algorithms are reviewed and summarized with an emphasis on their characteristics, advantages and disadvantages of these techniques. These are categorized into five different groups based on their workflows and segmentation principles. Different solutions are also proposed to compensate the existing problems in each algorithm. This paper also addresses the issue of quantitative evaluation of segmentation results.

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Section: Model-based Methodsmentioning

confidence: 98%

“…In some medical studies, brain images are still being investigated and segmented by medical experts on a slice-by-slice basis, which is labour-intensive and timeconsuming task [4,5]. These algorithms, which require user interaction, suffer from inter-and intra-observer variability [6].…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Methods and Applications in MRI Brain Images

Yazdani

Yusof

Karimian

et al. 2015

IETE Technical Review

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“…It usually depends on the application and imaging modality. There is a vast amount of literature on designing such energies [7], [8], [31]- [33]. For the image energy, there are many strategies which can broadly be categorized in two families: 1) schemes, which use gradient information to detect contours (a.k.a.…”

Section: Active Contours With Prior Shapesmentioning

confidence: 99%

Efficient Shape Priors for Spline-Based Snakes

Delgado-Gonzalo

Schmitter

Uhlmann

et al. 2015

IEEE Trans. on Image Process.

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Abstract-Parametric active contours are an attractive approach for image segmentation, thanks to their computational efficiency. They are driven by application-dependent energies that reflect the prior knowledge on the object to be segmented. We propose an energy involving shape priors acting in a regularization-like manner. Thereby, the shape of the snake is orthogonally projected onto the space that spans the affine transformations of a given shape prior. The formulation of the curves is continuous, which provides computational benefits when compared with landmark-based (discrete) methods. We show that this approach improves the robustness and quality of splinebased segmentation algorithms, while its computational overhead is negligible. An interactive and ready-to-use implementation of the proposed algorithm is available and was successfully tested on real data in order to segment Drosophila flies and yeast cells in microscopic images.Index Terms-Active contours, model-based segmentation, parametric snake, deformable template, B-spline, shape space.

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“…Mabood et al [21] proposed an absolute median deviation based model for noisy images, which was accurate and efficient as compared to the local Chan–Vese (LCV) model [36]. More models can be found in [9,16,30,32] by defining energy functional with other information, such as texture features [22,46], prior shape information [3,23,40] and local patch [35]. With proper initialization, these models are able to successfully extract the desirable objects depicted on images, but typically need complicated estimation strategies [10,31].…”

Section: Introductionmentioning

confidence: 99%

An active contour model based on local fitted images for image segmentation

Wang

Chang

Wang

et al. 2017

Information Sciences

113

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Active contour models are popular and widely used for a variety of image segmentation applications with promising accuracy, but they may suffer from limited segmentation performances due to the presence of intensity inhomogeneity. To overcome this drawback, a novel region-based active contour model based on two different local fitted images is proposed by constructing a novel local hybrid image fitting energy, which is minimized in a variational level set framework to guide the evolving of contour curves toward the desired boundaries. The proposed model is evaluated and compared with several typical active contour models to segment synthetic and real images with different intensity characteristics. Experimental results demonstrate that the proposed model outperforms these models in terms of accuracy in image segmentation.

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Medical Image Segmentation Algorithms using Deformable Models: A Review

Cited by 67 publications

References 38 publications

Image Segmentation Methods and Applications in MRI Brain Images

Image Segmentation Methods and Applications in MRI Brain Images

Efficient Shape Priors for Spline-Based Snakes

An active contour model based on local fitted images for image segmentation

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