Frangi Filter Research Articles

Performance analysis of biometric recognition system based on palmprint

Nowadays biometric authentication is one of the most preferred choice and in that also palmprint is becoming the most widely accepted technique because it can be captured easily and the algorithms can be implemented with ease. Detection of coarse lines in palm images is easy to the point that these lines can be revealed even by using a low determination camera. The integration of palmprint recognition with some other biometric recognition system does not require any special capture devices, so it can be done easily. Hence, this method suites best for individual verification. This research work is related to palmprint recognition in which the main aim is related to software based performance analysis of classifiers. The entire work is divided into three major phases namely palmprint pre-processing, feature extraction and accuracy estimation. Different functions and operators related to binarization, morphology and other operations are used for pre-processing; similarly feature extraction is carried out by using Frangi filter, Freak descriptor and FAST algorithm. After the final feature extraction procedure, classifiers are used to predict the accuracy in identifying right and wrong features for a given subject. This research work uses two classifiers namely discriminant analysis and K-nearest neighbor algorithm (KNN) where discriminant analysis shows 77% accuracy and KNN gives a better accuracy of 93%.

Quantitative Comparison of Retinal Vascular Features in Optical Coherence Tomography Angiography Images From Three Different Devices

To compare optical coherence tomography angiography (OCTA) images from three different devices. This was a prospective, observational, cross-sectional study. All eyes (n = 24) were imaged thrice each time with swept-source OCT (DRI OCT Triton Plus; Topcon, Tokyo, Japan), spectral-domain OCTA (AngioVue; Optovue, Fremont, CA), and SD-OCT Angioplex (Cirrus HD-OCT 5000; Carl Zeiss Meditec, Jena, Germany). Outcome measures were foveal avascular zone (FAZ) area, vessel density, spacing between large vessels (SLV), and spacing between small vessels (SSV). They were calculated using local fractal analyses and Frangi filter. The vascular parameters showed good to excellent repeatability (intraclass coefficient > 0.8) in the three devices. The FAZ area (P = .3) and vessel density of the superficial layer (P = .8) were similar between the devices. In the deep layer, vessel density was significantly higher (P = .02) with Angio-Vue images compared to those acquired using the other devices. However, SLV was significantly higher (P < .05) and SSV was significantly lower (P < .05) on Angioplex scans compared to other devices. Vessel parameters were significantly altered in Frangi-filtered images as compared to nonfiltered images. Vessel density showed good repeatability among the three devices, although there were differences in vessel parameters between the devices. Also, vessel parameters changed significantly after Frangi filtering. [Ophthalmic Surg Lasers Imaging Retina. 2018;49:488-496.].

Retracted: Robust Retinal Vessel Segmentation using Vessel's Location Map and Frangi Enhancement Filter

The analysis of retinal vascular is quite important because many diseases including stroke, diabetic retinopathy (DR) and coronary heart diseases can damage retinal vessel structure. In this research, a technique has been proposed using a combination of pre-processing steps, vessel enhancement techniques, segmentation and post-processing. The pre-processing section comprises of adaptive histogram equalisation for dissimilarity enhancement between vessels and background, a morphological top hat filter for macula and optic disc removal and high boost filtering, edges enhancement. Frangi filter is applied at multi-scale for enhancement of different vessel widths. Segmentation has been performed using global Otsu thresholding with some offset applied on difference image and enhanced image separately. A vessel location map (VLM) has been extracted using the post-processing steps of raster to vector transformed area and eccentricity-based threshold to eliminate the exudate/unwanted region from binarised image. Post-processing has been used in a new way to reject misclassified vessel pixels. The final segmented image has been obtained by using pixel-by-pixel AND operation between VLM and Frangi binarised image. The method has been rigorously analysed using STARE and DRIVE datasets.

A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising.

The exploration of retinal vessel structure is colossally important on account of numerous diseases including stroke, Diabetic Retinopathy (DR) and coronary heart diseases, which can damage the retinal vessel structure. The retinal vascular network is very hard to be extracted due to its spreading and diminishing geometry and contrast variation in an image. The proposed technique consists of unique parallel processes for denoising and extraction of blood vessels in retinal images. In the preprocessing section, an adaptive histogram equalization enhances dissimilarity between the vessels and the background and morphological top-hat filters are employed to eliminate macula and optic disc, etc. To remove local noise, the difference of images is computed from the top-hat filtered image and the high-boost filtered image. Frangi filter is applied at multi scale for the enhancement of vessels possessing diverse widths. Segmentation is performed by using improved Otsu thresholding on the high-boost filtered image and Frangi’s enhanced image, separately. In the postprocessing steps, a Vessel Location Map (VLM) is extracted by using raster to vector transformation. Postprocessing steps are employed in a novel way to reject misclassified vessel pixels. The final segmented image is obtained by using pixel-by-pixel AND operation between VLM and Frangi output image. The method has been rigorously analyzed on the STARE, DRIVE and HRF datasets.

Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering

Perivascular Spaces (PVS) are a feature of Small Vessel Disease (SVD), and are an important part of the brain’s circulation and glymphatic drainage system. Quantitative analysis of PVS on Magnetic Resonance Images (MRI) is important for understanding their relationship with neurological diseases. In this work, we propose a segmentation technique based on the 3D Frangi filtering for extraction of PVS from MRI. We used ordered logit models and visual rating scales as alternative ground truth for Frangi filter parameter optimization and evaluation. We optimized and validated our proposed models on two independent cohorts, a dementia sample (N = 20) and patients who previously had mild to moderate stroke (N = 48). Results demonstrate the robustness and generalisability of our segmentation method. Segmentation-based PVS burden estimates correlated well with neuroradiological assessments (Spearman’s ρ = 0.74, p < 0.001), supporting the potential of our proposed method.

Improving performance of coronary artery segmentation using calculated vessel location from the angiogram

Vessel segmentation in X-Ray Coronary Angiography (XCA) images, which is used for the diagnosis and treatment of anomalies in coronary vessels, has been an intriguing topic in recent years. Due to dynamic background and ambiguous brightness distribution, success rate in segmentation is low and realization has been a challenging process. This paper presents a new approach to increase the success rate of segmentation in XCA images. For this purpose, the vessel structure position is first determined with foreground detection algorithm in the XCA image sequences. Next, state of the art methods of vessel identification and image thresholding used for vessel structure segmentation are being experimented in full and foreground images. The contribution of the suggested method to the success of the segmentation is revealed with the experiments carried out. The scales of sensitivity, specificity and accuracy are given in the paper to evaluate the success of the segmentation. The results were compared with the angiography images segmented manually by a specialist physician. Without proposed detection of candidate vessel location, highest accuracy results 92.4 is achieved with Multiscale Gabor filter and PTile thresholding. Frangi Filter and Otsu thresholding algorithm reached the highest success rate of 93.81 by using the proposed method. All the experiments have shown that accuracy of vessel segmentation is increase by the proposed approach.

Automatic Vessel Extraction Using Particle Swarming Optimization for 3D Medical Images

Background: Blood vessel segmentation plays an important role in medical image analysis. Modern blood vessel segmentation algorithms attempts to attempts to increase patient safety by providing better diagnosis and support to more accurate medical decisions. Methods: In 3D image processing techniques leads to an emerging area, and voxel classification. Most of the voxel classification algorithms are a manual classification. This work introduces a novel fully automatic blood vessel segmentation algorithm from 3D images using Hessian-based multi-scale filters (Frangi's filter) and Chan-Vese model with level-set framework. Parameters of Frangi's filter are adjusted by means of an evolutionary computation method, particle swarm optimization (PSO). 3D synthetic and real CTA clinical image database is used to test the proposed algorithm and show a correct voxel classification. Conclusion: The proposed algorithm shows results that are more accurate. Keywords: Particle swarm optimization, Chan-Vese model, level-set, Hessian-based multi-scale filter, 3D medical images, blood vessel segmentation.

ROI DETECTION AND VESSEL SEGMENTATION IN RETINAL IMAGE

Abstract. Diabetes disrupts work by affecting the structure of the eye and afterwards leads to loss of vision. Depending on the stage of disease that called diabetic retinopathy, there are sudden loss of vision and blurred vision problems. Automated detection of vessels in retinal images is a useful study to diagnose eye diseases, disease classification and other clinical trials. The shape and structure of the vessels give information about the severity of the disease and the stage of the disease. Automatic and fast detection of vessels allows for a quick diagnosis of the disease and the treatment process to start shortly. ROI detection and vessel extraction methods for retinal image are mentioned in this study. It is shown that the Frangi filter used in image processing can be successfully used in detection and extraction of vessels.

An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network

High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer-aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography image transformation, the lung nodule segmentation, and the feature extraction, to construct a whole CADe system. It is difficult for these schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multigroup patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80.06% with 4.7 false positives per scan and the sensitivity of 94% with 15.1 false positives per scan. The results demonstrate that the multigroup patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.

Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification.

Retinal blood vessels have a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. For this reason, retinal vasculature extraction is important in order to help specialists for the diagnosis and treatment of systematic diseases. In this paper, a novel approach is developed to extract retinal blood vessel network. Our method comprises four stages: (1) preprocessing stage in order to prepare dataset for segmentation; (2) an enhancement procedure including Gabor, Frangi, and Gauss filters obtained separately before a top-hat transform; (3) a hard and soft clustering stage which includes K-means and Fuzzy C-means (FCM) in order to get binary vessel map; and (4) a postprocessing step which removes falsely segmented isolated regions. The method is tested on color retinal images obtained from STARE and DRIVE databases which are available online. As a result, Gabor filter followed by K-means clustering method achieves 95.94% and 95.71% of accuracy for STARE and DRIVE databases, respectively, which are acceptable for diagnosis systems.

Marker-less tracking of brain surface deformations by non-rigid registration integrating surface and vessel/sulci features

To compensate for brain shift in image-guided neurosurgery, we propose a new non-rigid registration method that integrates surface and vessel/sulci feature to noninvasively track the brain surface. Textured brain surfaces were acquired using phase-shift three-dimensional (3D) shape measurement, which offers 2D image pixels and their corresponding 3D points directly. Measured brain surfaces were noninvasively tracked using the proposed method by minimizing a new energy function, which is a weighted combination of 3D point corresponding estimation and surface deformation constraints. Initially, the measured surfaces were divided into featured and non-featured parts using a Frangi filter. The corresponding feature/non-feature points between intraoperative brain surfaces were estimated using the closest point algorithm. Subsequently, smoothness and rigidity constraints were introduced in the energy function for a smooth surface deformation and local surface detail conservation, respectively. Our 3D shape measurement accuracy was evaluated using 20 spheres for bias and precision errors. In addition, the proposed method was evaluated based on root mean square error (RMSE) and target registration error (TRE) with five porcine brains for which deformations were produced by gravity and pushing with different displacements in both the vertical and horizontal directions. The minimum and maximum bias errors were 0.32 and 0.61mm, respectively. The minimum and maximum precision errors were 0.025 and 0.30mm, respectively. Quantitative validation with porcine brains showed that the average RMSE and TRE were 0.1 and 0.9mm, respectively. The proposed method appeared to be advantageous in integrating vessels/sulci feature, robust to changes in deformation magnitude and integrated feature numbers, and feasible in compensating for brain shift deformation in surgeries.

Unsupervised Retinal Vessel Segmentation Using Combined Filters.

Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels’ appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi’s filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.

Application of the Ordered Logit Model to Optimising Frangi Filter Parameters for Segmentation of Perivascular Spaces

Segmentation of perivascular spaces (PVS) from brain magnetic resonance images (MRI) is important for understanding the brain's lymphatic system and its relationship with neurological diseases. The Frangi filter might be a valuable tool for this purpose. However, its parameters need to be adjusted in response to the variability in the scanner's parameters and study protocols. Knowing the neuroradiological ratings of the PVS, we used the ordered logit model to optimise Frangi filter parameters. The PVS volume obtained significantly and strongly correlated with neuroradiological assessments (Spearman's ρ=0.75, p < 0.001), suggesting that the ordered logit model could be a good alternative to conventional optimisation frameworks for segmenting PVS on MRI.

Rock Fracture Centerline Extraction based on Hessian Matrix and Steger Algorithm

The rock fracture detection by image analysis is significant for fracture measurement and assessment engineering. The paper proposes a novel image segmentation algorithm for the centerline tracing of a rock fracture based on Hessian Matrix at Multi-scales and Steger algorithm. A traditional fracture detection method, which does edge detection first, then makes image binarization, and finally performs noise removal and fracture gap linking, is difficult for images of rough rock surfaces. To overcome the problem, the new algorithm extracts the centerlines directly from a gray level image. It includes three steps: (1) Hessian Matrix and Frangi filter are adopted to enhance the curvilinear structures, then after image binarization, the spurious-fractures and noise are removed by synthesizing the area, circularity and rectangularity; (2) On the binary image, Steger algorithm is used to detect fracture centerline points, then the centerline points or segments are linked according to the gap distance and the angle differences; and (3) Based on the above centerline detection roughly, the centerline points are searched in the original image in a local window along the direction perpendicular to the normal of the centerline, then these points are linked. A number of rock fracture images have been tested, and the testing results show that compared to other traditional algorithms, the proposed algorithm can extract rock fracture centerlines accurately.

A coronary artery segmentation method based on multiscale analysis and region growing

Accurate coronary artery segmentation is a fundamental step in various medical imaging applications such as stenosis detection, 3D reconstruction and cardiac dynamics assessing. In this paper, a multiscale region growing (MSRG) method for coronary artery segmentation in 2D X-ray angiograms is proposed. First, a region growing rule incorporating both vesselness and direction information in a unique way is introduced. Then an iterative multiscale search based on this criterion is performed. Selected points in each step are considered as seeds for the following step. By combining vesselness and direction information in the growing rule, this method is able to avoid blockage caused by low vesselness values in vascular regions, which in turn, yields continuous vessel tree. Performing the process in a multiscale fashion helps to extract thin and peripheral vessels often missed by other segmentation methods. Quantitative evaluation performed on real angiography images shows that the proposed segmentation method identifies about 80% of the total coronary artery tree in relatively easy images and 70% in challenging cases with a mean precision of 82% and outperforms others segmentation methods in terms of sensitivity. The MSRG segmentation method was also implemented with different enhancement filters and it has been shown that the Frangi filter gives better results. The proposed segmentation method has proven to be tailored for coronary artery segmentation. It keeps an acceptable performance when dealing with challenging situations such as noise, stenosis and poor contrast.

Automatic detection of selective arterial devices for advanced visualization during abdominal aortic aneurysm endovascular repair

Here we address the automatic segmentation of endovascular devices used in the endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) that deform vascular tissues. Using this approach, the vascular structure is automatically reshaped solving the issue of misregistration observed on 2D/3D image fusion for EVAR guidance. The endovascular devices we considered are the graduated pigtail catheter (PC) used for contrast injection and the stent-graft delivery device (DD). The segmentation of the DD was enhanced using an asymmetric Frangi filter. The segmented geometries were then analysed using their specific features to remove artefacts. The radiopaque markers of the PC were enhanced using a fusion of Hessian and newly introduced gradient norm shift filters. Extensive experiments were performed using a database of images taken during 28 AAA-EVAR interventions. This dataset was divided into two parts: the first half was used to optimize parameters and the second to compile performances using optimal values obtained. The radiopaque markers of the PC were detected with a sensitivity of 88.3% and a positive predictive value (PPV) of 96%. The PC can therefore be positioned with a majority of its markers localized while the artefacts were all located inside the vessel lumen. The major parts of the DD, the dilatator tip and the pusher surfaces, were detected accurately with a sensitivity of 85.9% and a PPV of 88.7%. The less visible part of the DD, the stent enclosed within the sheath, was segmented with a sensitivity of 63.4% because the radiopacity of this region is low and uneven. The centreline of the DD in this stent region was alternatively traced within a 0.74 mm mean error. The automatic segmentation of endovascular devices during EVAR is feasible and accurate; it could be useful to perform elastic registration of the vascular lumen during endovascular repair.

Red lesion detection in retinal fundus images using Frangi-based filters.

This paper presents a method to detect red lesions related to Diabetic Retinopathy (DR), namely Microaneurysms and Hemorrhages from retinal fundus images with robustness to the presence of blood vessels. Filters based on Frangi filters are used for the first time for this task. Green channel of the input image was decomposed into smaller sub images and proposed filters were applied to each sub image after initial preprocessing. Features were extracted from the filter response and used to train a Support Vector Machine classifier to predict whether a test image had lesions or not. Experiments were performed on a dataset of 143 retinal fundus and the proposed method achieved areas under the ROC curve equal to 0.97 and 0.87 for Microaneurysms and Hemorrhages respectively. Results show the effectiveness of the proposed method for detecting red lesions. This method can help significantly in automated detection of DR with fewer false positives.

Automated segmentation of the lamina cribrosa using Frangi's filter: a novel approach for rapid identification of tissue volume fraction and beam orientation in a trabeculated structure in the eye.

The lamina cribrosa (LC) is a tissue in the posterior eye with a complex trabecular microstructure. This tissue is of great research interest, as it is likely the initial site of retinal ganglion cell axonal damage in glaucoma. Unfortunately, the LC is difficult to access experimentally, and thus imaging techniques in tandem with image processing have emerged as powerful tools to study the microstructure and biomechanics of this tissue. Here, we present a staining approach to enhance the contrast of the microstructure in micro-computed tomography (micro-CT) imaging as well as a comparison between tissues imaged with micro-CT and second harmonic generation (SHG) microscopy. We then apply a modified version of Frangi's vesselness filter to automatically segment the connective tissue beams of the LC and determine the orientation of each beam. This approach successfully segmented the beams of a porcine optic nerve head from micro-CT in three dimensions and SHG microscopy in two dimensions. As an application of this filter, we present finite-element modelling of the posterior eye that suggests that connective tissue volume fraction is the major driving factor of LC biomechanics. We conclude that segmentation with Frangi's filter is a powerful tool for future image-driven studies of LC biomechanics.

Automated Assessment of Hemodynamics in the Conjunctival Microvasculature Network.

The conjunctival microcirculation is accessible for direct visualization and quantitative assessment of microvascular hemodynamic properties. Currently available methods to assess hemodynamics in the conjunctival microvasculature use manual or semi-automated algorithms, which can be inefficient for application to a large number of microvessels within the microvascular network. We present an automated image analysis method for measurements of diameter and blood velocity in microvessels. The method was applied to conjunctival microcirculation images acquired in 15 healthy human subjects. Frangi filtering, thresholding, and morphological closing were applied to automatically segment microvessels, while variance filtering was used to detect blood flow. Diameter and blood velocity were measured in arterioles and venules within the conjunctival microvascular network, and blood flow and wall shear rate were calculated. Repeatability and validity of hemodynamic measurements were established. The automated image analysis method allows reliable, rapid and quantitative assessment of hemodynamics in the conjunctival microvascular network and can be potentially applied to microcirculation images of other tissues.

Multi-scale tubular structure detection in ultrasound imaging.

We propose a novel, physics-based method for detecting multi-scale tubular features in ultrasound images. The detector is based on a Hessian-matrix eigenvalue method, but unlike previous work, our detector is guided by an optimal model of vessel-like structures with respect to the ultrasound-image formation process. Our method provides a voxel-wise probability map, along with estimates of the radii and orientations of the detected tubes. These results can then be used for further processing, including segmentation and enhanced volume visualization. Most Hessian-based algorithms, including the well-known Frangi filter, were developed for CTA or MRA; they implicitly assume symmetry about the vessel centerline. This is not consistent with ultrasound data. We overcome this limitation by introducing a novel filter that allows multi-scale estimation both with respect to the vessel's centerline and with respect to the vessel's border. We use manually-segmented ultrasound imagery from 35 patients to show that our method is superior to standard Hessian-based methods. We evaluate the performance of the proposed methods based on the sensitivity and specificity like measures, and finally demonstrate further applicability of our method to vascular ultrasound images of the carotid artery, as well as ultrasound data for abdominal aortic aneurysms.