Vector field convolution medialness applied to neuron tracing
In this paper we propose a novel approach to the extraction of medial axis for grayscale objects. The method utilizes a computationally efficient vector field convolution to enhance the medialness feature. Local maxima of medialness are analyzed in scale space, yielding a robust medial axis for grayscale imagery. An important application of this work is the segmentation of neurons from noisy, cluttered microscopy images. Existing neuron segmentation methods depend heavily on accurate, noise-insensitive medial axis extraction. We propose the vector field convolution medialness operation as a first step in segmenting neurons. The proposed method requires no complex parameters or an initial binarization step. The efficacy of the method is demonstrated by a 60% reduction root mean squared error (2.9 pixels) as compared to an approach based on gradient vector flow.