Ординатура / Офтальмология / Английские материалы / Automated Image Detection of Retinal Pathology_Jelinek, Cree_2009

10.6Automated Segmentation and Analysis of Retinal Fundus Images

Algorithms have been described earlier that concentrate on measurement of vessel diameters from a sequence of vessel intensity profiles, but to facilitate topological analysis of the retinal vasculature, an automated approach to segmentation and analysis of the whole vascular network from retinal fundus images is desirable.

Most of the work on segmentation of retinal blood vessels can be categorized into three approaches; those based on line or edge detectors with boundary tracing [84; 85], those based on matched filters, either 1-D profile matching with vessel tracking and local thresholding [86–89] or 2-D matched filters [90–92], and those supervised methods that require manually labeled images for training [93; 94]. However, due to the large regional variations in intensity inherent in retinal images and the very low contrast between vessels and the background, particularly in red-free photographs, these techniques have several shortcomings. Techniques based on line or edge detectors lack robustness in defining blood vessels without fragmentation and techniques based on matched filters are difficult to adapt to the variations of widths and orientation of blood vessels. Furthermore, most of these segmentation methods have been developed to work either on red-free or fluorescein images, but not on both.

During the last few decades the computer vision and image processing communities have been studying and developing tools to describe image structures at different scales. Koenderink [95] advocated deriving a set of images at multiple scales by a process of diffusion, equivalent to convolution by a family of Gaussian kernels, and describing image properties in terms of differential geometric descriptors possessing invariance properties under certain transformations. Detection of tubelike structure features using multiscale analysis has been carried out by many researchers [96–101]. The main principle underlying all of these works was to develop a line-enhancement filter based on eigenvalue analysis of the Hessian matrix. Analysis of this kind has also been applied previously to optical images and particularly to retinal images [94; 102; 103], to extract the center lines and perform measurements of diameter using a medialness function.

We present an approach for segmenting blood vessels from fundus images, rather than enhancing them, based upon the principles of multiscale differential geometry in combination with gradient information and a region growing algorithm. This algorithm was originally presented at MICCAII’99 [104], where the segmentation method was tested on a small sample of images without validation. As far as we are aware, this was the first reported analysis of this kind applied to retinal images. An extension of this work [105] reports testing on two local databases and two public databases of complete manually labeled images [92; 94] that have also been used by other authors for the same validation purposes [89; 92; 94]. In addition, validation of diameters and branching angles measured from segmented vessels has also been presented [106]. This approach works equally well with both red-free fundus images

FIGURE 10.7

Multiscale convolution outputs for s = 0, 2, 8 and 14 pixels of a portion (720 580 pixels) of a negative of a red-free retinal image (2800 2400 pixels).

and fluorescein angiograms.

Under the multiscale framework, representation of information at different scales is achieved by convolving the original image I(x;y) with a Gaussian kernel G(x;y;s) of variance s2:

and s is a length scale factor. The effect of convolving an image with a Gaussian kernel is to suppress most of the structures in the image with a characteristic length less than s. Figure 10.7 shows different convolution outputs of a portion of a negative red-free retinal image, for s = 0, 2, 8, and 14 pixels, showing the progressive blurring of the image as the length scale factor increases.

The use of Gaussian kernels to generate multiscale information ensures that the image analysis is invariant with respect to translation, rotation, and size [95; 107]. To extract geometrical features from an image, a framework based on differentiation is used. Derivatives of an image can be numerically approximated by a linear convolution of the image with scale-normalized derivatives of the Gaussian kernel:

where n indicates the order of the derivative.

10.6.1Feature extraction

The first directional derivatives describe the variation of image intensity in the neighborhood of a point. Under a multiscale framework, the magnitude of the gradient

q

jÑIs (s)j = (dxIs)2 + (dyIs)2 (10.20)

Red-free vessel profile

FIGURE 10.8

(a)Intensity profile across a blood vessel from a negative red-free image and

(b)Eigenvalues, l+ (dash-dot line) and l (solid line). Ridges are regions where l+ 0 and l 0.

represents the slope of the image intensity for a particular value of the scale parameter s.

The second directional derivatives describe the variation in the gradient of intensity in the neighborhood of a point. Since vessels appear as ridge-like structures in the images, we look for pixels where the intensity image has a local maximum in the direction for which the gradient of the image undergoes the largest change (largest concavity) [108]. The second derivative information is derived from the Hessian of the intensity image I(x;y):

Since dxyIs = dyxIs, the Hessian matrix is symmetrical with real eigenvalues and orthogonal eigenvectors that are rotation invariant. The eigenvalues l+ and l , where we take l+ l , measure convexity and concavity in the corresponding eigendirections. Figure 10.8 shows the profile across a negative red-free vessel and the corresponding eigenvalues of the Hessian matrix, where l+ 0 and l 0 for pixels in the vessel. For a negative fluorescein vessel profile, where vessels are darker than the background, the eigenvalues are l 0 and l+ 0 for vessel pixels.

Labeling of each vascular tree involves three steps; thinning the segmented binary image to produce its skeleton, detecting branch and crossing points, and tracking the skeleton of the tree.

The skeleton of the vascular tree is first obtained from the segmented binary image by a thinning process in which pixels are eliminated from the boundaries towards the center without destroying connectivity in an 8-connected scheme. This yields an approximation to the medial axis of the tree. A pruning process is applied to eliminate short, false spurs, due to small undulations in the vessel boundary. Significant points in the skeleton must then be detected and classified as either terminal points, bifurcation points, or crossing points. In a first pass, skeleton pixels with only one neighbor in a 3 3 neighborhood are labeled as terminal points and pixels with three neighbors are labeled as candidate bifurcation points. Since vessel crossing points appear in the skeleton as two bifurcation points very close to each other, a second pass is made using a fixed size window centered on the candidate bifurcations, and the number of intersections of the skeleton with the window frame determines whether the point is a bifurcation or a crossing. Finally, each tree is tracked individually, commencing at the root. When the first bifurcation point is reached the chain of the current branch is ended and the coordinates of the starting points of the two daughters are found and saved. The process is iteratively repeated for every branch of the tree until a terminal point is reached and all daughters have been detected and numbered. The complete process has been reported by Martinez-Perez et al. [106].

Using the data generated in the labeling stage, the topological parameters described earlier can be derived. In addition, three types of geometrical features can also be measured automatically, these being bifurcation angles, together with lengths and areas of vessel segments. From the latter two items, vessel diameters can be estimated allowing nondimensional geometrical characteristics described earlier to be calculated, such as bifurcation optimality and length to diameter ratios.

10.7Clinical Findings from Retinal Vascular Topology

A recent investigation compared the topology and architecture of the retinal microvasculature in 20 normotensive subjects, 20 patients with uncomplicated essential hypertension (EHT), and 20 patients with malignant phase hypertension (MHT) [71]. Digitized red-free monochrome retinal photographs, reduced to 1400x1200 pixels, were analyzed to quantify geometrical and topological properties of arteriolar and venular trees.

Mean arteriolar LDR was significantly increased in essential hypertensives (p = 0:002) and malignant hypertensives (p < 0:001), as shown in Figure 10.10(a), consistent with other studies reported earlier. LDR in venules did not differ significantly between groups.

Both EHT and MHT were associated with significant changes in arteriolar topology. EHT and MHT were associated with reductions in NT ; this was most marked in

patients suggest that retinal vascular geometry may also yield clinically useful information here; by the time that conventional screening reveals diabetic retinopathy, the opportunity for effective preventative therapy may have passed. Analysis will be undertaken of retinal vascular geometry in a sub-group of patients participating in the ADVANCE study [110], a randomized trial of blood pressure lowering and glucose control in individuals with type 2 diabetes, and this is expected to add greater insight into the potential value of vascular geometry in the management of diabetes.

Alongside these clinical trials and observations, work is progressing on further development of algorithms to segment and measure the retinal vasculature, with the aim of minimizing the degree of user intervention required. Such developments offer the prospect of a highly automated screening tool, suitable for centralized analysis of retinal photographs captured locally using existing digital fundus cameras, for example during routine examinations by optometrists. Such an arrangement might offer a highly cost effective opportunity to screen large populations for risk factors in a range of systemic diseases.

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