Scale dependency of image derivatives for feature measurement in curvilinear structures
Extraction of image features is a crucial step in many image analysis tasks. In feature extraction methods Gaussian derivative kernels are frequently utilized. Blurring of the image due to convolution with these kernels gives rise to feature measures different from the intended value in the original image. We propose to solve this problem by explicitly modeling the scale dependency of derivatives combined with measurement of derivatives at multiple scales. This approach is illustrated in methods for feature measurement in curvilinear structures. Results in 3D Confocal Images confirm that modelling of scale behavior of derivatives results in improved methods for center line localization in curved line structures and enables curvature and diameter measurement.
scale image derivatives feature measurement curvilinear structures localization curvature diameter measurement center line detection bias removal