Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures

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.

Retinal Artery-Vein Classification via Topology Estimation

We propose a novel, graph-theoretic framework for distinguishing arteries from veins in a fundus image. We make use of the underlying vessel topology to better classify small and midsized vessels. We extend our previously proposed tree topology estimation framework by incorporating expert, domain-specific features to construct a simple, yet powerful global likelihood model. We efficiently maximize this model by iteratively exploring the space of possible solutions consistent with the projected vessels. We tested our method on four retinal datasets and achieved classification accuracies of 91.0%, 93.5%, 91.7%, and 90.9%, outperforming existing methods. Our results show the effectiveness of our approach, which is capable of analyzing the entire vasculature, including peripheral vessels, in wide field-of-view fundus photographs. This topology-based method is a potentially important tool for diagnosing diseases with retinal vascular manifestation.

Segmentation of retinal vessels by means of directional response vector similarity and region growing

This paper presents a novel retinal vessel segmentation method. Opposed to the general approach in similar directional methods, where only the maximal or summed responses of a pixel are used, here, the directional responses of a pixel are considered as a vector. The segmentation method is a unique region growing procedure which combines a hysteresis thresholding scheme with the response vector similarity of adjacent pixels. A vessel score map is constructed as the combination of the statistical measures of the response vectors and its local maxima to provide the seeds for the region growing procedure. A nearest neighbor classifier based on a rotation invariant response vector similarity measure is used to filter the seed points. Many techniques in the literature that capture the Gaussian-like cross-section of vessels suffer from the drawback of giving false high responses to the steep intensity transitions at the boundary of the optic disc and bright lesions. To overcome this issue, we also propose a symmetry constrained multiscale matched filtering technique. The proposed vessel segmentation method has been tested on three publicly available image sets, where its performance proved to be competitive with the state-of-the-art and comparable to the accuracy of a human observer, as well.

Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images

Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large-scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational imaging framework using deep and ensemble learning based hybrid architecture for reliable detection of blood vessels in fundus color images. A deep neural network (DNN) is used for unsupervised learning of vesselness dictionaries using sparse trained denoising auto-encoders (DAE), followed by supervised learning of the DNN response using a random forest for detecting vessels in color fundus images. In experimental evaluation with the DRIVE database, we achieve the objective of vessel detection with max. avg. accuracy of 0.9327 and area under ROC curve of 0.9195.

Automated identification of retinal vessels using a multiscale directional contrast quantification (MDCQ) strategy

PURPOSE

A novel algorithm is presented to automatically identify the retinal vessels depicted in color fundus photographs.

METHODS

The proposed algorithm quantifies the contrast of each pixel in retinal images at multiple scales and fuses the resulting consequent contrast images in a progressive manner by leveraging their spatial difference and continuity. The multiscale strategy is to deal with the variety of retinal vessels in width, intensity, resolution, and orientation; and the progressive fusion is to combine consequent images and meanwhile avoid a sudden fusion of image noise and/or artifacts in space. To quantitatively assess the performance of the algorithm, we tested it on three publicly available databases, namely, DRIVE, STARE, and HRF. The agreement between the computer results and the manual delineation in these databases were quantified by computing their overlapping in both area and length (centerline). The measures include sensitivity, specificity, and accuracy.

RESULTS

For the DRIVE database, the sensitivities in identifying vessels in area and length were around 90% and 70%, respectively, the accuracy in pixel classification was around 99%, and the precisions in terms of both area and length were around 94%. For the STARE database, the sensitivities in identifying vessels were around 90% in area and 70% in length, and the accuracy in pixel classification was around 97%. For the HRF database, the sensitivities in identifying vessels were around 92% in area and 83% in length for the healthy subgroup, around 92% in area and 75% in length for the glaucomatous subgroup, around 91% in area and 73% in length for the diabetic retinopathy subgroup. For all three subgroups, the accuracy was around 98%.

CONCLUSIONS

The experimental results demonstrate that the developed algorithm is capable of identifying retinal vessels depicted in color fundus photographs in a relatively reliable manner.

Adaptive Ridge Point Refinement for Seeds Detection in X-Ray Coronary Angiogram

Seed point is prerequired condition for tracking based method for extracting centerline or vascular structures from the angiogram. In this paper, a novel seed point detection method for coronary artery segmentation is proposed. Vessels on the image are first enhanced according to the distribution of Hessian eigenvalue in multiscale space; consequently, centerlines of tubular vessels are also enhanced. Ridge point is extracted as candidate seed point, which is then refined according to its mathematical definition. The theoretical feasibility of this method is also proven. Finally, all the detected ridge points are checked using a self-adaptive threshold to improve the robustness of results. Clinical angiograms are used to evaluate the performance of the proposed algorithm, and the results show that the proposed algorithm can detect a large set of true seed points located on most branches of vessels. Compared with traditional seed point detection algorithms, the proposed method can detect a larger number of seed points with higher precision. Considering that the proposed method can achieve accurate seed detection without any human interaction, it can be utilized for several clinical applications, such as vessel segmentation, centerline extraction, and topological identification.

Automatic extraction of blood vessels in the retinal vascular tree using multiscale medialness

We propose an algorithm for vessel extraction in retinal images. The first step consists of applying anisotropic diffusion filtering in the initial vessel network in order to restore disconnected vessel lines and eliminate noisy lines. In the second step, a multiscale line-tracking procedure allows detecting all vessels having similar dimensions at a chosen scale. Computing the individual image maps requires different steps. First, a number of points are preselected using the eigenvalues of the Hessian matrix. These points are expected to be near to a vessel axis. Then, for each preselected point, the response map is computed from gradient information of the image at the current scale. Finally, the multiscale image map is derived after combining the individual image maps at different scales (sizes). Two publicly available datasets have been used to test the performance of the suggested method. The main dataset is the STARE project's dataset and the second one is the DRIVE dataset. The experimental results, applied on the STARE dataset, show a maximum accuracy average of around 94.02%. Also, when performed on the DRIVE database, the maximum accuracy average reaches 91.55%.

Automatic left and right lung separation using free-formed surface fitting on volumetric CT

This study presents a completely automated method for separating the left and right lungs using free-formed surface fitting on volumetric computed tomography (CT). The left and right lungs are roughly divided using iterative 3-dimensional morphological operator and a Hessian matrix analysis. A point set traversing between the initial left and right lungs is then detected with a Euclidean distance transform to determine the optimal separating surface, which is then modeled from the point set using a free-formed surface-fitting algorithm. Subsequently, the left and right lung volumes are smoothly and directly separated using the separating surface. The performance of the proposed method was estimated by comparison with that of a human expert on 44 CT examinations. For all data sets, averages of the root mean square surface distance, maximum surface distance, and volumetric overlap error between the results of the automatic and the manual methods were 0.032 mm, 2.418 mm, and 0.017 %, respectively. Our study showed the feasibility of automatically separating the left and right lungs by identifying the 3D continuous separating surface on volumetric chest CT images.

Model-guided extraction of coronary vessel structures in 2D X-ray angiograms

Analysis of vessel structures in 2D X-ray angiograms is important for pre-operative evaluation and image-guided intervention. However, automated vessel segmentation in angiograms, especially extraction of the topology such as bifurcations and vessel crossings, remains challenging mainly due to the projective nature of angiography and background clutter. In this paper, a novel framework for model-guided coronary vessel extraction in 2D angiograms is presented. In this framework, a graph is constructed using a sparse set of pixels in the angiogram. With a single user-supplied click as the starting point, the vessel tree structure in the angiogram is automatically extracted from the graph. Ambiguities in this tree structure caused by 3D-to-2D projection are then resolved using topological information from the 3D vessel model of the same patient. By incorporating this prior shape information, the proposed method is effective in extraction of vessel topology, and is robust to background clutter and uneven illumination. Through quantitative evaluation on 20 angiograms, it is shown that this model-guided approach significantly improves detection of vessel structures and bifurcations.

Retinal vessel detection and measurement for computer-aided medical diagnosis

Since blood vessel detection and characteristic measurement for ocular retinal images is a fundamental problem in computer-aided medical diagnosis, automated algorithms/systems for vessel detection and measurement are always demanded. To support computer-aided diagnosis, an integrated approach/solution for vessel detection and diameter measurement is presented and validated. In the proposed approach, a Dempster-Shafer (D-S)-based edge detector is developed to obtain initial vessel edge information and an accurate vascular map for a retinal image. Then, the appropriate path and the centerline of a vessel of interest are identified automatically through graph search. Once the vessel path has been identified, the diameter of the vessel will be measured accordingly by the algorithm in real time. To achieve more accurate edge detection and diameter measurement, mixed Gaussian-matched filters are designed to refine the initial detection and measures. Other important medical indices of retinal vessels can also be calculated accordingly based on detection and measurement results. The efficiency of the proposed algorithm was validated by the retinal images obtained from different public databases. Experimental results show that the vessel detection rate of the algorithm is 100 % for large vessels and 89.9 % for small vessels, and the error rate on vessel diameter measurement is less than 5 %, which are all well within the acceptable range of deviation among the human graders.

A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA

We propose a fast seed detection for automatic tracking of coronary arteries in coronary computed tomographic angiography (CCTA). To detect vessel regions, Hessian-based filtering is combined with a new local geometric feature that is based on the similarity of the consecutive cross-sections perpendicular to the vessel direction. It is in turn founded on the prior knowledge that a vessel segment is shaped like a cylinder in axial slices. To improve computational efficiency, an axial slice, which contains part of three main coronary arteries, is selected and regions of interest (ROIs) are extracted in the slice. Only for the voxels belonging to the ROIs, the proposed geometric feature is calculated. With the seed points, which are the centroids of the detected vessel regions, and their vessel directions, vessel tracking method can be used for artery extraction. Here a particle filtering-based tracking algorithm is tested. Using 19 clinical CCTA datasets, it is demonstrated that the proposed method detects seed points and can be used for full automatic coronary artery extraction. ROC (receiver operating characteristic) curve analysis shows the advantages of the proposed method.

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

The 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.

Automatic anatomical labeling of the complete cerebral vasculature in mouse models

Study of cerebral vascular structure broadens our understanding of underlying variations, such as pathologies that can lead to cerebrovascular disorders. The development of high resolution 3D imaging modalities has provided us with the raw material to study the blood vessels in small animals such as mice. However, the high complexity and 3D nature of the cerebral vasculature make comparison and analysis of the vessels difficult, time-consuming and laborious. Here we present a framework for automated segmentation and recognition of the cerebral vessels in high resolution 3D images that addresses this need. The vasculature is segmented by following vessel center lines starting from automatically generated seeds and the vascular structure is represented as a graph. Each vessel segment is represented as an edge in the graph and has local features such as length, diameter, and direction, and relational features representing the connectivity of the vessel segments. Using these features, each edge in the graph is automatically labeled with its anatomical name using a stochastic relaxation algorithm. We have validated our method on micro-CT images of C57Bl/6J mice. A leave-one-out test performed on the labeled data set demonstrated the recognition rate for all vessels including major named vessels and their minor branches to be >75%. This automatic segmentation and recognition methods facilitate the comparison of blood vessels in large populations of subjects and allow us to study cerebrovascular variations.

Statistical pattern analysis of blood vessel features on retina images and its application to blood vessel mapping algorithms

Computer based modeling and analysis of blood vessel (BV) networks is essential for automated detection and tracking of anomalies and structural changes in retina images. Among many published techniques for automated BV mapping, optimal selection of thresholds to delineate BV pixels from their background pixels remains an open problem. In this paper we propose a novel representation of a BV pixel feature, daisy graph, using rotational contrast transform (RCT), and two feature descriptors energy E(p) and symmetry difference S(p) of the daisy graph. Non-BV pixels are separated from BV and boundary pixels based on E(p). Fitness of the lognormal distribution to S(p) of BV pixels with negative E(p) has been tested extensively for images in the STARE and DRIVE databases. Based on statistical pattern analysis in the feature space, we propose a fast self-calibrated BV mapping algorithm which achieve comparable and statistically sound performance as contemporary solutions.

Robust vessel segmentation in fundus images

One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.

Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation

The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710.

Automatic vasculature identification in coronary angiograms by adaptive geometrical tracking

As the uneven distribution of contrast agents and the perspective projection principle of X-ray, the vasculatures in angiographic image are with low contrast and are generally superposed with other organic tissues; therefore, it is very difficult to identify the vasculature and quantitatively estimate the blood flow directly from angiographic images. In this paper, we propose a fully automatic algorithm named adaptive geometrical vessel tracking (AGVT) for coronary artery identification in X-ray angiograms. Initially, the ridge enhancement (RE) image is obtained utilizing multiscale Hessian information. Then, automatic initialization procedures including seed points detection, and initial directions determination are performed on the RE image. The extracted ridge points can be adjusted to the geometrical centerline points adaptively through diameter estimation. Bifurcations are identified by discriminating connecting relationship of the tracked ridge points. Finally, all the tracked centerlines are merged and smoothed by classifying the connecting components on the vascular structures. Synthetic angiographic images and clinical angiograms are used to evaluate the performance of the proposed algorithm. The proposed algorithm is compared with other two vascular tracking techniques in terms of the efficiency and accuracy, which demonstrate successful applications of the proposed segmentation and extraction scheme in vasculature identification.

Application of morphological bit planes in retinal blood vessel extraction

The appearance of the retinal blood vessels is an important diagnostic indicator of various clinical disorders of the eye and the body. Retinal blood vessels have been shown to provide evidence in terms of change in diameter, branching angles, or tortuosity, as a result of ophthalmic disease. This paper reports the development for an automated method for segmentation of blood vessels in retinal images. A unique combination of methods for retinal blood vessel skeleton detection and multidirectional morphological bit plane slicing is presented to extract the blood vessels from the color retinal images. The skeleton of main vessels is extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. A multidirectional top-hat operator with rotating structuring elements is used to emphasize the vessels in a particular direction, and information is extracted using bit plane slicing. An iterative region growing method is applied to integrate the main skeleton and the images resulting from bit plane slicing of vessel direction-dependent morphological filters. The approach is tested on two publicly available databases DRIVE and STARE. Average accuracy achieved by the proposed method is 0.9423 for both the databases with significant values of sensitivity and specificity also; the algorithm outperforms the second human observer in terms of precision of segmented vessel tree.

Retina verification system based on biometric graph matching

This paper presents an automatic retina verification framework based on the biometric graph matching (BGM) algorithm. The retinal vasculature is extracted using a family of matched filters in the frequency domain and morphological operators. Then, retinal templates are defined as formal spatial graphs derived from the retinal vasculature. The BGM algorithm, a noisy graph matching algorithm, robust to translation, non-linear distortion, and small rotations, is used to compare retinal templates. The BGM algorithm uses graph topology to define three distance measures between a pair of graphs, two of which are new. A support vector machine (SVM) classifier is used to distinguish between genuine and imposter comparisons. Using single as well as multiple graph measures, the classifier achieves complete separation on a training set of images from the VARIA database (60% of the data), equaling the state-of-the-art for retina verification. Because the available data set is small, kernel density estimation (KDE) of the genuine and imposter score distributions of the training set are used to measure performance of the BGM algorithm. In the one dimensional case, the KDE model is validated with the testing set. A 0 EER on testing shows that the KDE model is a good fit for the empirical distribution. For the multiple graph measures, a novel combination of the SVM boundary and the KDE model is used to obtain a fair comparison with the KDE model for the single measure. A clear benefit in using multiple graph measures over a single measure to distinguish genuine and imposter comparisons is demonstrated by a drop in theoretical error of between 60% and more than two orders of magnitude.

Retinal artery and venular caliber grading: a semi-automated evaluation tool

Retinal imaging can facilitate the measurement and quantification of subtle variations and abnormalities in retinal vasculature. Retinal vascular imaging may thus offer potential as a noninvasive research tool to probe the role and pathophysiology of the microvasculature, and as a cardiovascular risk prediction tool. In order to perform this, an accurate method must be provided that is statistically sound and repeatable. This paper presents the methodology of such a system that assists physicians in measuring vessel caliber (i.e., diameters or width) from digitized fundus photographs. The system involves texture and edge information to measure and quantify vessel caliber. The graphical user interfaces are developed to allow retinal image graders to select individual vessel area that automatically returns the vessel calibers for noisy images. The accuracy of the method is validated using the measured caliber from graders and an existing method. The system provides very high accuracy vessel caliber measurement which is also reproducible with high consistency.

Segmentation of Coronary CT Angiography Images Based on Deformable Model with New Edge Measures

To deal with locally narrow, low-contrast and spatially varying intensity in segmentation of the computed tomography angiographic (CTA) data, a deformable model with newly proposed edge measures was presented for segmenting coronary arteries. The edge measures were derived from the refined vesselness measures of brightness and multi-scale filtering responses, i.e., vesselness and scale. The initial vessel region and boundary region was derived from the multi-scale filtering responses, from which the statistical information of vessel appearance was attained to yield brightness measure. As compared with the multi-scale filtering responses, the refined vesselness measures could effectively suppress non-vascular background while preserving vessel-like structure. Finally, the new edge measures were embedded into deformable model, resulting in better artery segmentation.

Retinal artery-vein caliber grading using color fundus imaging

Recent research suggests that retinal vessel caliber (or cross-sectional width) measured from retinal photographs is an important feature for predicting cardiovascular diseases (CVDs). One of the most utilized measures is to quantify retinal arteriolar and venular caliber as the Central Retinal Artery Equivalent (CRAE) and Central Retinal Vein Equivalent (CRVE). However, current computer tools utilize manual or semi-automatic grading methods to estimate CRAE and CRVE. These methods involve a significant amount of grader's time and can add a significant level of inaccuracy due to repetitive nature of grading and intragrader distances. An automatic and time efficient grading of the vessel caliber with highly repeatable measurement is essential, but is technically challenging due to a substantial variation of the retinal blood vessels' properties. In this paper, we propose a new technique to measure the retinal vessel caliber, which is an "edge-based" vessel tracking method. We measured CRAE and CRVE from each of the vessel types. We achieve very high accuracy (average 96.23%) for each of the cross-sectional width measurement compared to manually graded width. For overall vessel caliber measurement accuracy of CRAE and CRVE, we compared the results with an existing semi-automatic method which showed high correlation of 0.85 and 0.92, respectively. The intra-grader reproducibility of our method was high, with the correlation coefficient of 0.881 for CRAE and 0.875 for CRVE.

Retinal image graph-cut segmentation algorithm using multiscale Hessian-enhancement-based nonlocal mean filter

We propose a new method to enhance and extract the retinal vessels. First, we employ a multiscale Hessian-based filter to compute the maximum response of vessel likeness function for each pixel. By this step, blood vessels of different widths are significantly enhanced. Then, we adopt a nonlocal mean filter to suppress the noise of enhanced image and maintain the vessel information at the same time. After that, a radial gradient symmetry transformation is adopted to suppress the nonvessel structures. Finally, an accurate graph-cut segmentation step is performed using the result of previous symmetry transformation as an initial. We test the proposed approach on the publicly available databases: DRIVE. The experimental results show that our method is quite effective.

Real-Time Retinal Vessel Mapping and Localization for Intraocular Surgery

Computer-aided intraocular surgery requires precise, real-time knowledge of the vasculature during retinal procedures such as laser photocoagulation or vessel cannulation. Because vitreoretinal surgeons manipulate retinal structures on the back of the eye through ports in the sclera, voluntary and involuntary tool motion rotates the eye in the socket and causes movement to the microscope view of the retina. The dynamic nature of the surgical workspace during intraocular surgery makes mapping, tracking, and localizing vasculature in real time a challenge. We present an approach that both maps and localizes retinal vessels by temporally fusing and registering individual-frame vessel detections. On video of porcine and human retina, we demonstrate real-time performance, rapid convergence, and robustness to variable illumination and tool occlusion.

Targeting the phosphoinositide 3-kinase p110-α isoform impairs cell proliferation, survival, and tumor growth in small cell lung cancer

PURPOSE

The phosphoinositide 3-kinase (PI3K) pathway is fundamental for cell proliferation and survival and is frequently altered and activated in neoplasia, including carcinomas of the lung. In this study, we investigated the potential of targeting the catalytic class I(A) PI3K isoforms in small cell lung cancer (SCLC), which is the most aggressive of all lung cancer types.

EXPERIMENTAL DESIGN

The expression of PI3K isoforms in patient specimens was analyzed. The effects on SCLC cell survival and downstream signaling were determined following PI3K isoform inhibition by selective inhibitors or downregulation by siRNA.

RESULTS

Overexpression of the PI3K isoforms p110-α and p110-β and the antiapoptotic protein Bcl-2 was shown by immunohistochemistry in primary SCLC tissue samples. Targeting the PI3K p110-α with RNA interference or selective pharmacologic inhibitors resulted in strongly affected cell proliferation of SCLC cells in vitro and in vivo, whereas targeting p110-β was less effective. Inhibition of p110-α also resulted in increased apoptosis and autophagy, which was accompanied by decreased phosphorylation of Akt and components of the mTOR pathway, such as the ribosomal S6 protein, and the eukaryotic translation initiation factor 4E-binding protein 1. A DNA microarray analysis revealed that p110-α inhibition profoundly affected the balance of pro- and antiapoptotic Bcl-2 family proteins. Finally, p110-α inhibition led to impaired SCLC tumor formation and vascularization in vivo.

CONCLUSION

Together our data show the key involvement of the PI3K isoform p110-α in the regulation of multiple tumor-promoting processes in SCLC.

An approach to localize the retinal blood vessels using bit planes and centerline detection

The change in morphology, diameter, branching pattern or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports an automated method for segmentation of blood vessels in retinal images. A unique combination of techniques for vessel centerlines detection and morphological bit plane slicing is presented to extract the blood vessel tree from the retinal images. The centerlines are extracted by using the first order derivative of a Gaussian filter in four orientations and then evaluation of derivative signs and average derivative values is performed. Mathematical morphology has emerged as a proficient technique for quantifying the blood vessels in the retina. The shape and orientation map of blood vessels is obtained by applying a multidirectional morphological top-hat operator with a linear structuring element followed by bit plane slicing of the vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The methodology is tested on three publicly available databases DRIVE, STARE and MESSIDOR. The results demonstrate that the performance of the proposed algorithm is comparable with state of the art techniques in terms of accuracy, sensitivity and specificity.

Blood vessel segmentation methodologies in retinal images--a survey

Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. This work examines the blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera and a survey of techniques is presented. The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures. We intend to give the reader a framework for the existing research; to introduce the range of retinal vessel segmentation algorithms; to discuss the current trends and future directions and summarize the open problems. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve.

Supervised retinal biometrics in different lighting conditions

Retinal image has been considered for number of health and biometrics applications. However, the reliability of these has not been investigated thoroughly. The variation observed in retina scans taken at different times is attributable to differences in illumination and positioning of the camera. It causes some missing bifurcations and crossovers from the retinal vessels. Exhaustive selection of optimal parameters is needed to construct the best similarity metrics equation to overcome the incomplete landmarks. In this paper, we extracted multiple features from the retina scans and employs supervised classification to overcome the shortcomings of the current techniques. The experimental results of 60 retina scans with different lightning conditions demonstrate the efficacy of this technique. The results were compared with the existing methods.

Cerebral artery-vein separation using 0.1-Hz oscillation in dual-wavelength optical imaging

We present a novel artery-vein separation method using 0.1-Hz oscillation at two wavelengths with optical imaging of intrinsic signals (OIS). The 0.1-Hz oscillation at a green light wavelength of 546 nm exhibits greater amplitude in arteries than in veins and is primarily caused by vasomotion, whereas the 0.1-Hz oscillation at a red light wavelength of 630 nm exhibits greater amplitude in veins than in arteries and is primarily caused by changes of deoxyhemoglobin concentration. This spectral feature enables cortical arteries and veins to be segmented independently. The arteries can be segmented on the 0.1-Hz amplitude image at 546 nm using matched filters of a modified dual Gaussian model combining with a single Gaussian model. The veins are a combination of vessels segmented on both amplitude images at the two wavelengths using multiscale matched filters of single Gaussian model. Our method can separate most of the thin arteries and veins from each other, especially the thin arteries with low contrast in raw gray images. In vivo OIS experiments demonstrate the separation ability of the 0.1-Hz based segmentation method in cerebral cortex of eight rats. Two validation studies were undertaken to evaluate the performance of the method by quantifying the arterial and venous length based on a reference standard. The results indicate that our 0.1-Hz method is very effective in separating both large and thin arteries and veins regardless of vessel crossover or overlapping to great extent in comparison with previous methods.

Automated clarity assessment of retinal images using regionally based structural and statistical measures

An automated image analysis system for application in mass medical screening must assess the clarity of the images before analysing their content. This is the case in grading for diabetic retinopathy screening where the failure to assess clarity could result in retinal images of people with retinopathy being erroneously classed as normal. This paper compares methods of clarity assessment based on the degradation of visible structures and based on the deviation of image properties outside expected norms caused by clarity loss. Vessel visibility measures and statistical measures were determined at locations in the image which have high saliency and these were used to obtain an image clarity assessment using supervised classification. The usefulness of the measures as indicators of image clarity was assessed. Tests were performed on 987 disc-centred and macula-centred retinal photographs (347 with inadequate clarity) obtained from the English National Screening Programme. Images with inadequate clarity were detected with 92.6% sensitivity at 90% specificity. In a set of 2000 macula-centred images (200 with inadequate clarity) from the Scottish Screening Programme, inadequate clarity was detected with 96.7% sensitivity at 90% specificity. This study has shown that structural and statistical measures are equally useful for retinal image clarity assessment.

A new phenotypic manifestation of familial adenomatous polyposis

Familial adenomatous polyposis (FAP) is an autosomal dominant disease with hundreds of colorectal adenomas in teenagers and progression to colorectal cancer if colectomy is not performed. We investigated the association of two phenotypic manifestations-oral mucosal vascular density (OMVD) and oral mucosal reflectance (OMR)--with FAP and patients with multiple colorectal adenomas. Thirty-three patients with FAP from 29 unrelated pedigrees with APC gene mutation, 5 with multiple adenomas and no known gene mutations, and 50 population controls were evaluated for the two different manifestations utilizing a photographic/spectrophotometric system capturing images and reflectance at various wavelengths. Statistical analysis was performed with student t test and test performance characteristics were calculated. There were no significant differences in demographic variables between the FAP and control group. A significant difference in OMVD between FAP patients and controls was noted, P < 0.001. The sensitivity and specificity of oral mucosal vascular density for FAP was 91 and 90%, respectively. No association between this phenotypic manifestation and age or gender was found. All 5 patient with multiple polyps were positive for OMVD and the value was significantly higher than controls, P = 0.002. No significant difference was noted in OMR between the two patient groups and controls. OMVD is a new phenotypic manifestation in patients with FAP and also may identify those with multiple adenomas without known gene mutation.

Detection of familial adenomatous polyposis with orthogonal polarized spectroscopy of the oral mucosa vasculature

Familial Adenomatous Polyposis (FAP) is an autosomal dominant disease characterized by the development of multiple colonic polyps at younger age with a near 100% lifetime risk of colorectal cancer. The determination of FAP is made after extensive clinical evaluation and genetic testing of at risk individuals. We investigated a novel spectro-polarimetric imaging system capable of capturing high-resolution images of the oral mucosa at different wavelengths in an attempt to distinguish patients with FAP from controls. Results of a clinical trial show that the system is capable of separating FAP positive individuals from controls by measuring the individuals' oral vascular density and complexity.

A prokaryotic S1P lyase degrades extracellular S1P in vitro and in vivo: implication for treating hyperproliferative disorders

Sphingosine-1-phosphate (S1P) regulates a broad spectrum of fundamental cellular processes like proliferation, death, migration and cytokine production. Therefore, elevated levels of S1P may be causal to various pathologic conditions including cancer, fibrosis, inflammation, autoimmune diseases and aberrant angiogenesis. Here we report that S1P lyase from the prokaryote Symbiobacterium thermophilum (StSPL) degrades extracellular S1P in vitro and in blood. Moreover, we investigated its effect on cellular responses typical of fibrosis, cancer and aberrant angiogenesis using renal mesangial cells, endothelial cells, breast (MCF-7) and colon (HCT 116) carcinoma cells as disease models. In all cell types, wild-type StSPL, but not an inactive mutant, disrupted MAPK phosphorylation stimulated by exogenous S1P. Functionally, disruption of S1P receptor signaling by S1P depletion inhibited proliferation and expression of connective tissue growth factor in mesangial cells, proliferation, migration and VEGF expression in carcinoma cells, and proliferation and migration of endothelial cells. Upon intravenous injection of StSPL in mice, plasma S1P levels rapidly declined by 70% within 1 h and then recovered to normal 6 h after injection. Using the chicken chorioallantoic membrane model we further demonstrate that also under in vivo conditions StSPL, but not the inactive mutant, inhibited tumor cell-induced angiogenesis as an S1P-dependent process. Our data demonstrate that recombinant StSPL is active under extracellular conditions and holds promise as a new enzyme therapeutic for diseases associated with increased levels of S1P and S1P receptor signaling.

Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography

Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality for the visualization of the heart and coronary arteries. To fully exploit the potential of the CCTA datasets and apply it in clinical practice, an automated coronary artery extraction approach is needed. The purpose of this paper is to present and validate a fully automatic centerline extraction algorithm for coronary arteries in CCTA images. The algorithm is based on an improved version of Frangi's vesselness filter which removes unwanted step-edge responses at the boundaries of the cardiac chambers. Building upon this new vesselness filter, the coronary artery extraction pipeline extracts the centerlines of main branches as well as side-branches automatically. This algorithm was first evaluated with a standardized evaluation framework named Rotterdam Coronary Artery Algorithm Evaluation Framework used in the MICCAI Coronary Artery Tracking challenge 2008 (CAT08). It includes 128 reference centerlines which were manually delineated. The average overlap and accuracy measures of our method were 93.7% and 0.30 mm, respectively, which ranked at the 1st and 3rd place compared to five other automatic methods presented in the CAT08. Secondly, in 50 clinical datasets, a total of 100 reference centerlines were generated from lumen contours in the transversal planes which were manually corrected by an expert from the cardiology department. In this evaluation, the average overlap and accuracy were 96.1% and 0.33 mm, respectively. The entire processing time for one dataset is less than 2 min on a standard desktop computer. In conclusion, our newly developed automatic approach can extract coronary arteries in CCTA images with excellent performances in extraction ability and accuracy.

An integrated software solution for multi-modal mapping of morphological and functional ocular data

Various morphological and functional techniques for retina examination have been established in the recent years. Although many examination results are spatially resolved and can be mapped onto data originating from other modalities, usually only data from one modality is analyzed by a clinician at a time. This is mainly because there is no software available to the public that enables the registration of structure and function in the clinical setting. Therefore we developed an integrated mapping application that allows the registration and analysis of morphological data (fundus photography, optical coherence tomography, scanning laser ophthalmoscopy images, and GDx thickness profiles) and functional data (multifocal electroretinography, multifocal pattern electroretinography, perimetry, and microperimetry). To obtain quantitative data that can be used for clinical trials and statistical analyses, extraction routines for morphological parameters - such as retinal layer thicknesses and measures of the vascular network - have been integrated. Global, regional and point-specific data from registered modalities can be extracted and exported for statistical analyses. In this article we present the implementation and examples of use of the developed software.

Retinal vessel extraction by combining radial symmetry transform and iterated graph cuts

In this paper, we propose a new method for the extraction of blood vessels in retinal images. This approach starts with a Hessian-based multiscale filtering method to enhance blood vessels in gray retinal images. Subsequently, a new radial symmetry transformation, which is based on line kernels, is proposed to improve the detection of vessel structures and restrain the response of nonvessel structures. Finally, an iterated segmentation algorithm is used to extract retinal vessels. The proposed approach has been tested on the two publicly available databases, DRIVE and STARE. The experimental results show the feasibility of the proposed method.

Scale-invariant vesselness filtering for simultaneous extraction of optimal medial and boundary paths in retinal images

In this paper we present a new multiscale vesselness filtering technique to simultaneously compute optimal medial axes and boundaries on fundus images of the retina. The scale-invariance of the vessel cross-sectional profile in the frequency domain is examined, and phase congruency implementing the scale-invariance is utilised for multiscale vesselness filtering. This allows the vessel ridge and boundary evidence to be preserved under single filtering settings. We have performed width measurement experiments on a dataset of fundus images, using an optimal medial axis skeletonisation scheme as a post-processing step, to compare the proposed technique with a generalised Gaussian profile modelling approach.

Automated kymograph analysis for profiling axonal transport of secretory granules

This paper describes an automated method to profile the velocity patterns of small organelles (BDNF granules) being transported along a selected section of axon of a cultured neuron imaged by time-lapse fluorescence microscopy. Instead of directly detecting the granules as in conventional tracking, the proposed method starts by generating a two-dimensional spatio-temporal map (kymograph) of the granule traffic along an axon segment. Temporal sharpening during the kymograph creation helps to highlight granule movements while suppressing clutter due to stationary granules. A voting algorithm defined over orientation distribution functions is used to refine the locations and velocities of the granules. The refined kymograph is analyzed using an algorithm inspired from the minimum set cover framework to generate multiple motion trajectories of granule transport paths. The proposed method is computationally efficient, robust to significant levels of noise and clutter, and can be used to capture and quantify trends in transport patterns quickly and accurately. When evaluated on a collection of image sequences, the proposed method was found to detect granule movement events with 94% recall rate and 82% precision compared to a time-consuming manual analysis. Further, we present a study to evaluate the efficacy of velocity profiling by analyzing the impact of oxidative stress on granule transport in which the fully automated analysis correctly reproduced the biological conclusion generated by manual analysis.

Salient feature region: a new method for retinal image registration

Retinal image registration is crucial for the diagnoses and treatments of various eye diseases. A great number of methods have been developed to solve this problem; however, fast and accurate registration of low-quality retinal images is still a challenging problem since the low content contrast, large intensity variance as well as deterioration of unhealthy retina caused by various pathologies. This paper provides a new retinal image registration method based on salient feature region (SFR). We first propose a well-defined region saliency measure that consists of both local adaptive variance and gradient field entropy to extract the SFRs in each image. Next, an innovative local feature descriptor that combines gradient field distribution with corresponding geometric information is then computed to match the SFRs accurately. After that, normalized cross-correlation-based local rigid registration is performed on those matched SFRs to refine the accuracy of local alignment. Finally, the two images are registered by adopting high-order global transformation model with locally well-aligned region centers as control points. Experimental results show that our method is quite effective for retinal image registration.

Automatic blood vessel localization in small field of view eye images

Localizing blood vessels in eye images is a crucial step in the automated and objective diagnosis of eye diseases. Most previous research has focused on extracting the centerlines of vessels in large field of view images. However, for diagnosing diseases of the optic disk region, like glaucoma, small field of view images have to be analyzed. One needs to identify not only the centerlines, but also vessel widths, which vary widely in these images. We present an automatic technique for localizing vessels in small field of view images using multi-scale matched filters. We also estimate local vessel properties - width and orientation - along the length of each vessel. Furthermore, we explicitly account for highlights on thick vessels - central reflexes - which are ignored in many previous works. Qualitative and quantitative results demonstrate the efficacy of our method - e.g. vessel centers are localized with RMS and median errors of 2.11 and 1 pixels, respectively in 700×700 images.