Fast and Robust Clinical Triple-Region Image Segmentation Using One Level Set Function

Li, Shuo; Fevens, Thomas; Krzyżak, Adam; Jin, Chao; Song, Li

doi:10.1007/11866763_94

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Several studies have shown that the variational, active contour/level set formalism can lead to effective algorithms to solve many vision problems such as tracking, 16 motion estimation, 17 and 3D interpretation, 18 as well as many medical image analysis problems. [1][2][3] This formalism is well suited to medical image segmentation because it can balance the contribution of image data and prior knowledge in a principled, flexible and transparent way. Level set methods are commonly based on the optimization of an objective functional which contains two types of terms: data terms, which measure the fidelity of segmentation to image intensities, and prior terms, which traduce prior knowledge learned from a set of relevant images and segmentation examples.…”

Section: Introductionmentioning

confidence: 99%

Area prior constrained level set evolution for medical image segmentation

Ayed¹,

Li²,

Islam

et al. 2008

SPIE Proceedings

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The level set framework has proven well suited to medical image segmentation 1-6 thanks to its ability of balancing the contribution of image data and prior knowledge in a principled, flexible and transparent way. It consists of evolving a curve toward the target object boundaries. The curve evolution equation is sought following the optimization of a cost functional containing two types of terms: data terms, which measure the fidelity of segmentation to image intensities, and prior terms, which traduce learned prior knowledge. Without priors many algorithms are likely to fail due to high noise, low contrast and data incompleteness. Different priors have been investigated such as shape 1 and appearance priors. 7 In this study, we propose a simple type of priors: the area prior. This prior embeds knowledge of an approximate object area and has two positive effects. First, It speeds up significantly the evolution when the curve is far from the target object boundaries. Second, it slows down the evolution when the curve is close to the target. Consequently, it reinforces curve stability at the desired boundaries when dealing with low contrast intensity edges. The algorithm is validated with several experiments using Magnetic Resonance (MR) images and Computed Tomography (CT) images. A comparison with another level set method illustrates the positive effects of the area prior.

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Section: Introductionmentioning

confidence: 99%

Area prior constrained level set evolution for medical image segmentation

Ayed¹,

Li²,

Islam

et al. 2008

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…These approaches are computationally intensive and therefore time consuming (Jeleń, Krzyżak and Fevens, 2006). On the other hand, level set methods, which, also proved to be a powerful tool for medical image segmentation (Li, Xu, Gui and Fox, 2005;Droske, Meyer, Rumpf and K., 2001;Deng and Tsui, 2002;Tsai, Yezzi, Wells, Tempany, Tucker, Fan, Grimson and Willsky, 2003;Li, Fevens, Krzyżak, Jin and Li, 2006), involve fewer computations than Hough transform approaches and therefore achieve faster computational times. Level sets were first described by Osher and Sethian (1988) as a method for capturing moving fronts.…”

Section: Preprocessingmentioning

confidence: 99%

Classification of Breast Cancer Malignancy Using Cytological Images of Fine Needle Aspiration Biopsies

Jeleń¹,

Fevens²,

Krzyżak³

2008

International Journal of Applied Mathematics and Computer Science

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According to the World Health Organization (WHO), breast cancer (BC) is one of the most deadly cancers diagnosed among middle-aged women. Precise diagnosis and prognosis are crucial to reduce the high death rate. In this paper we present a framework for automatic malignancy grading of fine needle aspiration biopsy tissue. The malignancy grade is one of the most important factors taken into consideration during the prediction of cancer behavior after the treatment. Our framework is based on a classification using Support Vector Machines (SVM). The SVMs presented here are able to assign a malignancy grade based on preextracted features with the accuracy up to 94.24%. We also show that SVMs performed best out of four tested classifiers.

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“…We have chosen these two constraints due to their descriptive power in segmenting compound objects. The level set-based methods have been widely used in computer vision over the years and proven to be useful for medical image segmentation [5], [29], [41], [49] due to their several advantages such as 1) parametrization independence, 2) the ease of implementation, 3) their ability to deal with topological changes, 4) the ease of extendibility from a curve in 2D to higher dimensions (e.g. surfaces and hyper-surfaces) and 5) their ability to impose different image data and prior knowledge terms and control their contributions in segmentation tasks.…”

Section: Introductionmentioning

confidence: 99%

Local Optimization Based Segmentation of Spatially-Recurring, Multi-Region Objects With Part Configuration Constraints

Nosrati

Hamarneh

2014

IEEE Trans. Med. Imaging

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The level set framework has been a popular medical image segmentation technique for many years due to its several advantages, such as parametrization independence, ease of implementation , extendibility from a curve in 2D to higher dimensions , and automatic handling of topological changes. However, existence of noise, low contrast and objects complexity in medical images cause many segmentation algorithms (including level set-based methods) to fail. Incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate and plausible results. Two important constraints, containment and exclusion of regions, have gained attention in recent years mainly due to their descriptive power and intuitive definitions. In this paper, we augment the level set framework with the ability to handle these two intuitive geometric relationships, containment and exclusion, along with a distance constraint between boundaries of multi-region objects. Level set's important property of automatically handling topological changes of evolving contours/surfaces enables us to segment spatially-recurring objects (e.g. multiple instances of multi-region cells in a large microscopy image) while satisfying the two aforementioned constraints. In addition, the level set approach gives us a very simple and natural way to compute the distance between contours/surfaces and impose constraints on it. The downside, however, is a local optimization framework in which the final segmentation solution depends on the initialization. In fact, here, we sacrifice the optimizibility (local instead of global solution) in exchange for lower space complexity (less memory usage) and faster runtime (especially for large microscopic images) as well as no grid artifacts. Nevertheless, the result from validating our method on synthetic and several biomedical applications, mainly on multi-region cell segmentation in microscopy images and cardiac segmentation in MR images, showed the utility and advantages of this augmented level set framework (even with fully automatic or rough initialization that is distant from the desired boundaries). We also compared our framework with its counterpart methods in the discrete domain and reported the pros and cons of each of these methods in terms of metrication error and efficiency in memory usage and runtime.

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Fast and Robust Clinical Triple-Region Image Segmentation Using One Level Set Function

Cited by 6 publications

References 13 publications

Area prior constrained level set evolution for medical image segmentation

Area prior constrained level set evolution for medical image segmentation

Classification of Breast Cancer Malignancy Using Cytological Images of Fine Needle Aspiration Biopsies

Local Optimization Based Segmentation of Spatially-Recurring, Multi-Region Objects With Part Configuration Constraints

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