Blood vessels can be conceptualized anatomically
as an intricate network, or tree-like structure (or vasculature), of hollow
tubes of different sizes and compositions including arteries, arterioles,
capillaries, venules, and veins. Their continuing integrity is vital to nurture
life: any damage to them could lead to profound complications, including
stroke, diabetes, arteriosclerosis, cardiovascular diseases and hypertension,
to name only the most obvious. Vascular diseases are often life-critical for
individuals, and present a challenging public health problem for society. The
drive for better understanding and management of these conditions naturally
motivates the need for improved imaging techniques. The detection and analysis
of the vessels in medical images is a fundamental task in many clinical
applications to support early detection, diagnosis and optimal treatment.
In line with the proliferation of imaging modalities, there is an ever-increasing demand for automated vessel analysis systems for which where blood vessel segmentation is the first and most important step. As blood vessels can be seen as linear structures distributed at different orientations and scales in an image, various kernels (or enhancement filters) have been proposed to enhance them in order to ease the segmentation problem. In particular, a local phase based filter recently introduced by Lathen et al seems to be superior to intensity based filters as it is immune to intensity inhomogeneity and is capable of faithfully enhancing vessels of different widths.
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