Vessel Segmentation Using a Shape Driven Flow

Nain, Delphine; Yezzi, Anthony; Turk, Greg

doi:10.1007/978-3-540-30135-6_7

Cited by 114 publications

(82 citation statements)

References 17 publications

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“…61 Retinal vessel detection Many different methods for automatic detection of the retinal vascular structure in fundus camera images have been reported. 28,69 This includes vessel tracking where vessel centre lines are followed guided by local information; 70 matched filters to highlight blood vessels; 71 deformable models; 72 Hessian measures are used to steer the application of matched filters and confidence measures; 73 and gradient vector fields. 74 Classifying image pixels as either vessel or non-vessel sees another application for supervised classification techniques.…”

Section: Retinal Lesion Detectionmentioning

confidence: 99%

Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions

MacGillivray¹,

Trucco²,

Cameron³

et al. 2014

BJR

119

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imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions. Br J Radiol 2014;87:20130832. REVIEW ARTICLERetinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions 1,2 T J MACGILLIVRAY, MSc ABSTRACTThe black void behind the pupil was optically impenetrable before the invention of the ophthalmoscope by von Helmholtz over 150 years ago. Advances in retinal imaging and image processing, especially over the past decade, have opened a route to another unexplored landscape, the retinal neurovascular architecture and the retinal ganglion pathways linking to the central nervous system beyond. Exploiting these research opportunities requires multidisciplinary teams to explore the interface sitting at the border between ophthalmology, neurology and computing science. It is from the detail and depth of retinal phenotyping that novel metrics and candidate biomarkers are likely to emerge. Confirmation that in vivo retinal neurovascular measures are predictive of microvascular change in the brain and other organs is likely to be a major area of research activity over the next decade. Unlocking this hidden potential within the retina requires integration of structural and functional data sets, that is, multimodal mapping and longitudinal studies spanning the natural history of the disease process. And with further advances in imaging, it is likely that this area of retinal research will remain active and clinically relevant for many years to come. Accordingly, this review looks at state-of-the-art retinal imaging and its application to diagnosis, characterization and prognosis of chronic illness or long-term conditions.Currently about 20 million people in the UK suffer from at least 1 long-term condition (a health problem that cannot be cured but can be controlled by medication or other therapies), and this is set to increase 3-fold over the next decade placing an enormous economic and humanresource strain on the National Health Service.1-3 The retina is unique in the body in allowing easy observation of blood vessels with simple, non-invasive instruments. It is also the only accessible site for studying the central nervous system (CNS) in vivo.4 This makes it a ripe environment for the study of systemic and neurological disease, in a patient-acceptable way. Changes to the retina can occur in some chronic diseases years before other signs become apparent. [5][6][7][8][9] Thus, studying the retina may provide an additional means for clinicians to stratify risk and help identify people who would benefit from early lifestyle changes and preventative therapies, as well as assessing the efficacy of new treatments. In addition, there is some early evidence that certain retinal measures may correlate with disease characterization and also prognosis, which not only provides clinicians with a useful assessment tool but also offers research scientists new avenues to explore in the understanding of the p...

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Section: Retinal Lesion Detectionmentioning

confidence: 99%

Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions

MacGillivray¹,

Trucco²,

Cameron³

et al. 2014

BJR

119

View full text Add to dashboard Cite

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“…Methods belonging to this category often follow the same framework where each image having an initial boundary evolves to minimize a surface energy depending on the image intensity values and the local smoothness properties of the desired boundary. The level set technique [5,6] provides as a flexible tool in handling morphological variations. Approaches have been proposed to improve the performance of the original active contour models.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Vascular Extraction Using MRA Statistics and Gradient Information

Zhao

Tian

Wang

et al. 2018

Mathematical Problems in Engineering

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Brain vessel segmentation is a fundamental component of cerebral disease screening systems. However, detecting vessels is still a challenging task owing to their complex appearance and thinning geometry as well as the contrast decrease from the root of the vessel to its thin branches. We present a method for segmentation of the vasculature in Magnetic Resonance Angiography (MRA) images. First, we apply volume projection, 2D segmentation, and back-projection procedures for first stage of background subtraction and vessel reservation. Those labeled as background or vessel voxels are excluded from consideration in later computation. Second, stochastic expectation maximization algorithm (SEM) is used to estimate the probability density function (PDF) of the remaining voxels, which are assumed to be mixture of one Rayleigh and two Gaussian distributions. These voxels can then be classified into background, middle region, or vascular structure. Third, we adapt the -means method which is based on the gradient of remaining voxels to effectively detect true positives around boundaries of vessels. Experimental results on clinical cerebral data demonstrate that using gradient information as a further step improves the mixture model based segmentation of cerebral vasculature, in particular segmentation of the low contrast vasculature.

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“…The scales are sampled logarithmically as discussed by Sato et al in [10]. Suppose the Hessian matrix obtained at the scale σ is H σ and the associated Hessian matrix along the surface is M σ , the surface deformation equation becomes, (6) where the terms arg max σ |Tr(M σ )| and arg max σ |n T H σ n| select the scales that exhibit the largest second order intensity changes along the surface tangential plane and along the surface normal, among a set of pre-defined scales.…”

Section: Vessel Specific Image Features and Multiscale Detectionmentioning

confidence: 99%

“…The capillary force aims at pulling the evolving surface into thin and low contrast vessels. Nain et al devised the shape driven flow [6] to reduce the chance of false positive detection when segmenting vessels.…”

Section: Introductionmentioning

confidence: 99%

A Deformable Surface Model for Vascular Segmentation

Law

Chung

2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. Inspired by the motion of a solid surface under liquid pressure, this paper proposes a novel deformable surface model to segment blood vessels in medical images. In the proposed model, the segmented region and the background region are respectively considered as liquid and an elastic solid. The surface of the elastic solid experiences various forces derived from the second order intensity statistics and the surface geometry. These forces cause the solid surface to deform in order to segment vascular structures in an image. The proposed model has been studied in the experiments on synthetic data and clinical data acquired by different imaging modalities. It is experimentally shown that the new model is robust to intensity contrast changes inside blood vessels and thus very suitable to perform vascular segmentation.

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Vessel Segmentation Using a Shape Driven Flow

Cited by 114 publications

References 17 publications

Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions

Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions

Vascular Extraction Using MRA Statistics and Gradient Information

A Deformable Surface Model for Vascular Segmentation

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