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

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each, a method for finding common vessels in the image and performing change detection is presented.

9.1Identifying Blood Vessels

The processes of separating the portions of a retinal image that are vessels from the rest of the image are known as vessel extraction (or tracing) and vessel segmentation. These processes are similar in goal but typically are distinct in process. Each have complications due to such factors as poor contrast between vessels and background; presence of noise; varying levels of illumination and contrast across the image; physical inconsistencies of vessels such as central reflex [1–5]; and the presence of pathologies. All these factors yield different results, both in terms of how the results are modeled and the actual regions identified. Different methods yield distinctly different results and even the same method will yield different results for images taken from the same patient in a single session. These differences become significant for images taken at different points in time as they could be mistaken as change. Methods used to determine the vessel boundaries should strive to lessen the frequency and severity of these inconsistencies.

9.2Vessel Models

Many models are used to describe and identify vessels in images. Most are based on detectable image features such as edges, cross-sectional profiles, or regions of uniform intensity. Edge models use algorithms that work by identifying vessel boundaries typically by applying an edge detection operator such as differential or gradient operators [6–8]; Sobel operators [9; 10]; Kirsch operators [11]; or first order Gaussian filters [12]. Cross-sectional models employ algorithms that attempt to find regions of the image that closely approximate a predetermined shape such as a half ellipse [13]; Gaussian [1; 14–19]; or 6th degree polynomial [20]. Algorithms that use local areas of uniform intensity generally employ thresholding [21; 22], relaxation [23], morphological operators [16; 24–26], or affine convex sets to identify ridges and valleys [27].

Blood vessel models can more generally be characterized as those that are boundary detection (or edge detection) based or those that are cross section or matchedfilter based. Depending on the intended application for the vessels detected, different aspects of the model may be more important. For vessel change detection, we decided that the most important aspect of the vessel model is the accurate and precise location of vessel boundaries.

9.3Vessel Extraction Methods

Algorithms for identifying blood vessels in a retina image generally fall into two classes — those that segment vessel pixels and those that extract vessel information. Generally, segmentation is referred to as a process in which, for all pixels, certain characteristics of each pixel and its neighbors are examined. Then, based on some criteria, each pixel is determined to belong to one of several groups or categories. In retinal image segmentation, the simplest set of categories is binary — vessel or nonvessel (background). This is what we refer to as “vessel segmentation.” Techniques for segmentation include thresholding [21], morphological operations [16; 24–26; 28], matched filters [9; 15–17; 22; 29; 30], neural nets [31–33], FFTs [34], and edge detectors [6; 9; 11; 35; 36]. Segmentation algorithms generally produce binary segmentations, are computationally expensive and typically require further analysis to calculate morphometric information such as vessel width.

Extraction algorithms [1; 8; 12; 14; 18; 36–39] on the other hand, generally use exploratory techniques. They are faster computationally and usually determine useful morphometric information as part of the discovery process. They work by starting on known vessel points and “tracing” or “tracking” the vasculature structure in the image based on probing for certain vessel characteristics such as the vessel boundaries. Since the vessel boundaries are part of the discovery process, these algorithms generally contain information such as vessel widths, center points, and local orientation that can be used later in the change detection process. Many of these methods employ edge-centric models and are well suited for vessel change detection. For these reasons, we selected the exploratory algorithm in Section 9.4 as the basis for the change detection algorithm described in Section 9.9.

9.4Can’s Vessel Extraction Algorithm

Can’s vessel extraction (or “tracing”) algorithm [8] uses an iterative process of tracing the vasculature based on a localized model. The model is based on two physical properties of vessels — that vessels are locally straight and consist of two parallel sides. As such, it works by searching for two parallel vessel boundaries, found based on detection of two parallel, locally straight edges. The entire algorithm consists of two stages.

Stage 1 (seed point initialization): The algorithm analyzes the image along a coarse grid to gather samples for determining greyscale statistics (contrast and brightness levels) and to detect initial locations of blood vessels using greyscale minima. These minima are considered as “seed points” at which a tracing algorithm will be initiated. False seed points are filtered out by testing for the existence of a pair of sufficiently strong parallel edges around the minima. A seed point is filtered out if