Multi-scale Line Detection Research Articles

Robust retinal blood vessel segmentation using line detectors with multiple masks

Several diseases can be detected and diagnosed at a very early stage by observing changes in retinal blood vessel features. In order to detect these changes, the major step required is blood vessel segmentation. In this study, an effective method for the segmentation of blood vessels on coloured digital retinal images has been proposed. The proposed method uses linear combination of line detectors at varying scales along with multiple windows of different sizes. By implementing this technique, the drawbacks encountered in multi-scale line detection such as noise and false vessel detection around the optic disk are eliminated. The performance of the proposed method is evaluated on three publicly available datasets DRIVE, STARE and CHASE by considering sensitivity, specificity, accuracy, precision, false discovery rate, F1 score, Matthews correlation coefficient and G-mean. The results were analysed by comparing with most of the other earlier existing methods and have proven to achieve higher accuracy. It has also confirmed its effectiveness and robustness with high-resolution retinal images together with better simplicity and faster implementation for reliable blood vessel segmentation.

A Robust Actin Filaments Image Analysis Framework.

The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a ‘cartoon’ part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the ‘cartoon’ image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts grown in two different conditions: static (control) and fluid shear stress. The proposed methodology exhibited higher sensitivity values and similar accuracy compared to state-of-the-art methods.

Random Forests for Dura Mater Microvasculature Segmentation Using Epifluorescence Images.

Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for segmenting thin vessel structures using multiscale features based on Hessian, oriented second derivatives, Laplacian of Gaussian and line features. The latter multiscale line detector feature helps in detecting and connecting faint vessel structures that would otherwise be missed. Experimental results on epifluorescence imagery show that the RF approach produces foreground vessel regions that are almost 20 and 25 percent better than Niblack and Otsu threshold-based segmentations respectively.

A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images

Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. Line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting.The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p<0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p<0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p<0.05).

Automatic Segmentation of Retinal Blood Vessels Based on Improved Multiscale Line Detection

The appearance of retinal blood vessels is an important diagnostic indicator of serious disease, such as hypertension, diabetes, cardiovascular disease, and stroke. Automatic segmentation of the retinal vasculature is a primary step towards automatic assessment of the retinal blood vessel features. This paper presents an automated method for the enhancement and segmentation of blood vessels in fundus images. To decrease the influence of the optic disk, and emphasize the vessels for each retinal image, a multidirectional morphological top-hat transform with rotating structuring elements is first applied to the background homogenized retinal image. Then, an improved multiscale line detector is presented to produce a vessel response image, and yield the retinal blood vessel tree for each retinal image. Since different line detectors at varying scales have different line responses in the multiscale detector, the line detectors with longer length produce more vessel responses than the ones with shorter length; the improved multiscale detector combines all the responses at different scales by setting different weights for each scale. The methodology is evaluated on two publicly available databases, DRIVE and STARE. Experimental results demonstrate an excellent performance that approximates the average accuracy of a human observer. Moreover, the method is simple, fast, and robust to noise, so it is suitable for being integrated into a computer-assisted diagnostic system for ophthalmic disorders.

A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection

A retinal vessel tracking method based on Bayesian theory and multi-scale line detection is proposed in this paper. The optic disk is located by a PCA method and the initial points of tracking are identified. In each step, candidate points for vessel edges are selected on a semi-ellipse. Three types of vessel structure are considered in the tracking: normal vessel, branching, and crossing. To determine the new pair of edge points, the characteristics of the vessel intensity profiles along both the cross section and the longitudinal direction are considered in the tracking. A Gaussian model is assumed in the cross section and multi-scale line detection is employed in the longitudinal direction. The advantage of the proposed method is that two dimensional vessel information is employed, which makes it work better than methods using one dimensional information only. Our method is tested on the REVIEW database and a comparison study is performed. Experimental results show that the proposed method is precise and robust in tracking vessel edges.

An Automated Method for Retinal Arteriovenous Nicking Quantification From Color Fundus Images

Retinal arteriovenous (AV) nicking is one of the prominent and significant microvascular abnormalities. It is characterized by the decrease in the venular caliber at both sides of an artery-vein crossing. Recent research suggests that retinal AV nicking is a strong predictor of eye diseases such as branch retinal vein occlusion and cardiovascular diseases such as stroke. In this study, we present a novel method for objective and quantitative AV nicking assessment. From the input retinal image, the vascular network is first extracted using the multiscale line detection method. The crossover point detection method is then performed to localize all AV crossing locations. At each detected crossover point, the four vessel segments, two associated with the artery and two associated with the vein, are identified and two venular segments are then recognized through the artery-vein classification method. The vessel widths along the two venular segments are measured and analyzed to compute the AV nicking severity of that crossover. The proposed method was validated on 47 high-resolution retinal images obtained from two population-based studies. The experimental results indicate a strong correlation between the computed AV nicking values and the expert grading with a Spearman correlation coefficient of 0.70. Sensitivity was 77% and specificity was 92% (Kappa κ = 0.70) when comparing AV nicking detected using the proposed method to that detected using a manual grading method, performed by trained photographic graders.

Retinal Blood Vessel Segmentation with Optic Disc Pixels Exclusion

The morphological changes of retinal blood vessels are important indicators used to diagnose and monitor the progression of various diseases. A number of retinal blood vessel segmentation methods have been introduced, including the line operator based methods, which have shown satisfactory results. However, the basic line operator method cannot discriminate the pixels around the retinal optic disc, resulting in false classification of those pixels. In this paper, we integrate the detection of pixels around the retinal optic disc to the line operator method so that those pixels can be excluded from the vessel pixels. The method is evaluated on the widely used retinal dataset, the DRIVE dataset. The results demonstrate that the proposed method has made improvements over the basic and the multi-scale line detector with accuracy and area under curve of 0.942 and 0.9521, respectively.

An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency

We present a line matching algorithm which utilizes both the local appearance of lines and their geometric attributes. To overcome the problem of segment fragmentation and geometric variation, we extract lines in the scale space. To depict the local appearance of lines, we design a novel line descriptor called Line Band Descriptor (LBD). To evaluate the pairwise geometric consistency, we define the pairwise geometric attributes between line pairs. Then we built a relational graph for candidate line matches and employ a spectral technique to solve this matching problem efficiently. The advantages of the proposed algorithm are as follows: (1) it is robust to image transformations because of the multi-scale line detection strategy; (2) it is efficient because the designed LBD descriptor is fast to compute and the appearance similarities reduce the dimension of the graph matching problem; (3) it is accurate even for low-texture images because of the pairwise geometric consistency evaluation.

Retinal Vessel Segmentation Using Multi-Scale Line Detection

Inspection of the retinal vasculature may reveal precursors of serious diseases such as hypertension, diabetes, cardiovascular disease and stroke. In this paper an effective method for automatically extracting the vascular network in retinal images is presented. The proposed method is based on a multi-scale line detection, which is the line responses at varying scales. Linearly combining these line responses produces the final segmentation for each retinal image. The multi-scale line detection is applied on a vessel enhanced image whose noise and optic disc is removed and the contrast of blood vessels (including thin vessels) is enhanced by top-hat transformation and line detector filter. The preprocessing with retinal image results in the response of thin vessels with multi-scale detection is more sensitive and free from the influence of the optic disc, so the proposed method can get the very detail vascular tree in segmentation results. The performance of the proposed method was evaluated on two publicly available DRIVE, STARE databases. Experimental results have also shown the proposed method achieves high local accuracy (a measure to assess the accuracy at regions around the vessels) and approximates the average accuracy of a human observer. Moreover, the method is simple, fast, and robust to noise, so suitable for being integrated into a computer-assisted diagnostic system for ophthalmic disorders.

Retinal vessel segmentation using an improved multi-scale line detection

This paper proposes an effective retinal vessel segmentation method based on a combination of line detectors at different scales. To decrease the influence of optic disc and enhance retinal vessel (especially small vessel) response for each retinal image, the proposed method first applies a top-hat transform and a line filter to the background homogenised retinal image. Then an improved multi-scale detection is used to produce a vascular response image for each retinal image. Since the contribution of different lengths of line detectors to vessel response is not equal, the improved multi-scale detection combines all line responses at varying scales by their contribution to vessel response. The performance of the proposed method is evaluated on two publicly available DRIVE, STARE databases. Experimental results demonstrate that our method achieves high accuracy and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity.

An effective retinal blood vessel segmentation method using multi-scale line detection

Changes in retinal blood vessel features are precursors of serious diseases such as cardiovascular disease and stroke. Therefore, analysis of retinal vascular features can assist in detecting these changes and allow the patient to take action while the disease is still in its early stages. Automation of this process would help to reduce the cost associated with trained graders and remove the issue of inconsistency introduced by manual grading. Among different retinal analysis tasks, retinal blood vessel extraction plays an extremely important role as it is the first essential step before any measurement can be made. In this paper, we present an effective method for automatically extracting blood vessels from colour retinal images. The proposed method is based on the fact that by changing the length of a basic line detector, line detectors at varying scales are achieved. To maintain the strength and eliminate the drawbacks of each individual line detector, the line responses at varying scales are linearly combined to produce the final segmentation for each retinal image. The performance of the proposed method was evaluated both quantitatively and qualitatively on three publicly available DRIVE, STARE, and REVIEW datasets. On DRIVE and STARE datasets, the proposed method achieves high local accuracy (a measure to assess the accuracy at regions around the vessels) while retaining comparable accuracy compared to other existing methods. Visual inspection on the segmentation results shows that the proposed method produces accurate segmentation on central reflex vessels while keeping close vessels well separated. On REVIEW dataset, the vessel width measurements obtained using the segmentations produced by the proposed method are highly accurate and close to the measurements provided by the experts. This has demonstrated the high segmentation accuracy of the proposed method and its applicability for automatic vascular calibre measurement. Other advantages of the proposed method include its efficiency with fast segmentation time, its simplicity and scalability to deal with high resolution retinal images.