Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images

Alharbi, Shuaa S.; Sazak, Çiğdem; Nelson, Carl J.; Obara, Bogusław

doi:10.1109/bibm.2018.8621329

Cited by 8 publications

(6 citation statements)

References 34 publications

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“…A non-linear filtering method based on mathematical morphology [32] that combines two basic morphological operations (erosion and dilation) can be employed to address the drawback of Hessian-based Frangi filter. One of the morphological techniques is known as top-hat transformation and it can deal with a non-uniform background illumination [33].…”

Section: Vessel Enhancementmentioning

confidence: 99%

“…It may lead to poor enhancement, or it may suppress the finer and lower intensity vessels. Phase Congruency Tensor-based approaches on the other hand are image contrast-independent [32].…”

Section: Vessel Enhancementmentioning

confidence: 99%

“…Local tensor can detect structures in any direction. By taking advantage of both top-hat transformation and local tensor methods, Alharbi et al [32] combine these two methods and propose a technique called Multiscale Top-Hat Tensor (MTHT). MTHT shows better enhancement performance compared to the performance of Hessian-based and top-hat transformation alone.…”

Section: Vessel Enhancementmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Collaterals from Cone-Beam CT Images in Stroke

Aziz

Izhar

Asirvadam

et al. 2021

Sensors

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Collateral vessels play an important role in the restoration of blood flow to the ischemic tissues of stroke patients, and the quality of collateral flow has major impact on reducing treatment delay and increasing the success rate of reperfusion. Due to high spatial resolution and rapid scan time, advance imaging using the cone-beam computed tomography (CBCT) is gaining more attention over the conventional angiography in acute stroke diagnosis. Detecting collateral vessels from CBCT images is a challenging task due to the presence of noises and artifacts, small-size and non-uniform structure of vessels. This paper presents a technique to objectively identify collateral vessels from non-collateral vessels. In our technique, several filters are used on the CBCT images of stroke patients to remove noises and artifacts, then multiscale top-hat transformation method is implemented on the pre-processed images to further enhance the vessels. Next, we applied three types of feature extraction methods which are gray level co-occurrence matrix (GLCM), moment invariant, and shape to explore which feature is best to classify the collateral vessels. These features are then used by the support vector machine (SVM), random forest, decision tree, and K-nearest neighbors (KNN) classifiers to classify vessels. Finally, the performance of these classifiers is evaluated in terms of accuracy, sensitivity, precision, recall, F-Measure, and area under the receiver operating characteristics curve. Our results show that all classifiers achieve promising classification accuracy above 90% and able to detect the collateral and non-collateral vessels from images.

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Section: Vessel Enhancementmentioning

confidence: 99%

“…It may lead to poor enhancement, or it may suppress the finer and lower intensity vessels. Phase Congruency Tensor-based approaches on the other hand are image contrast-independent [32].…”

Section: Vessel Enhancementmentioning

confidence: 99%

Section: Vessel Enhancementmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Collaterals from Cone-Beam CT Images in Stroke

Aziz

Izhar

Asirvadam

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The fractional anisotropic tensor (Alhasson et al 2018) explores the feasibility of detecting vascular structures based on regularized Hessian eigenvalues and junction reconstruction in multiscale strategy to construct a novel enhancement function. The multiscale top-hat tensor (Alharbi et al 2018) combines the multiscale morphological filtering with a local tensor representation of vessel-like structures in images to enhance the vascular structure. Compared with Hessian-based methods, tensor-based ones are insensitive to intensity and noise variations in images, so they are suitable for vessel enhancement in medical images with low intensity and noise.…”

Section: Introductionmentioning

confidence: 99%

Local-global active contour model based on tensor-based representation for 3D ultrasound vessel segmentation

Dong¹,

Ai²,

Fan³

et al. 2021

Phys. Med. Biol.

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Three-dimensional (3D) vessel segmentation can provide full spatial information about an anatomic structure to help physicians gain increased understanding of vascular structures, which plays an utmost role in many medical image-processing and analysis applications. The purpose of this paper aims to develop a 3D vessel-segmentation method that can improve segmentation accuracy in 3D ultrasound (US) images. We propose a 3D tensor-based active contour model method for accurate 3D vessel segmentation. With our method, the contrast-independent multiscale bottom-hat tensor representation and local-global information are captured. This strategy ensures the effective extraction of the boundaries of vessels from inhomogeneous and homogeneous regions without being affected by the noise and low-contrast of the 3D US images. Experimental results in clinical 3D US and public 3D Multiphoton Microscopy datasets are used for quantitative and qualitative comparison with several state-of-the-art vessel segmentation methods. Clinical experiments demonstrate that our method can achieve a smoother and more accurate boundary of the vessel object than competing methods. The mean SE, SP and ACC of the proposed method are: 0.7768 ± 0.0597, 0.9978 ± 0.0013 and 0.9971 ± 0.0015 respectively. Experiments on the public dataset show that our method can segment complex vessels in different medical images with noise and low- contrast.

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“…He et al [18] developed a brand-new design of structure-maintaining kernels for supervised tensor learning. If the order of tensors is specified, tensor representation can be achieved by some very simple and convenient kernel methods, namely, the matrix kernel function of second-order tensor [19][20][21][22] and the K3rd kernel function for third-order tensor [23,24]. This paper probes into the nonlinear classification problem with tensor representation, and designs a tensor-based nonlinear classification algorithm, namely, the kernel-based STM (KSTM).…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Tensor-Based Machine Learning Algorithm for Image Classification

Wang¹,

Chen²

2019

RIA

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In recent year, the tensor theory has been frequently incorporated to machine learning, because of the various advantages of tensor-based machine learning over vector-based machining learning: the ability to preserve the spatiotemporal information, allowing full utilization of the data, and the suitability for solving high-dimensional problems with a small sample size. Considering the suitability of tensor algorithm for classical high-dimensional, small-sample problems, this paper probes into the nonlinear classification problem with tensor representation, and designs a tensor-based nonlinear classification algorithm, namely, the kernel-based STM (KSTM). The maximum margin principle was adopted for the classification by the KSTM: the two types of samples are separated by the decision hyperplane as far as possible in the tensor space. Through numerical experiments, it is proved that the KSTM achieved better classification accuracy than the linear method, especially for the highdimensional problem with a small sample size.

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Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images

Cited by 8 publications

References 34 publications

Detection of Collaterals from Cone-Beam CT Images in Stroke

Detection of Collaterals from Cone-Beam CT Images in Stroke

Local-global active contour model based on tensor-based representation for 3D ultrasound vessel segmentation

A Nonlinear Tensor-Based Machine Learning Algorithm for Image Classification

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