Linear Feature Detection Method and Apparatus

#The present invention relates to methods of automated image analysis and in particular to computer methods for feature detection in an image.

#The invention has been developed primarily for use as a method of linear feature detection for image analysis in computer vision and pattern recognition applications and will be described hereinafter with reference to this application. However it will be appreciated that the invention may not limited to this particular field of use. Neurite outgrowth detection is one of the application examples.

#Any discussion of the prior art throughout the specification should in no way be considered as an admission that such prior art is widely known or forms part of the common general knowledge in the field. In particular the references cited throughout the in specification should in no way be considered as an admission that such art is prior art, widely known or forms part of the common general knowledge in the field.

#A preferred embodiment of the invention will now be described, by way of example only, with reference to the accompanying drawings in which:

#FIG. 1 is a graphical depiction of the linear feature detection filter windows used in the multiple NMS technique according to the invention;

#FIG. 2(a) is a simple sample image for linear feature detection;

#The preferred embodiments of the invention provide a new algorithm for linear feature detection using multiple directional non-maximum suppression (NMS). This primarily involves NMS being applied to an image along multiple directions as defined by linear windows. Since there is no complicated filtering of the image involved, the algorithm is generally less computationally intensive than other methods.

#As described above, Hessian-based linear detection techniques or matched filters require a large amount of computational time and direct use of the image gradient is typically not very reliable. Instead of detecting the local direction of the linear features directly, the preferred embodiment detects the linear features by utilising the responses of multiple directional NMS.

#Linear features are defined as a sequence of points where the image has an intensity maximum in the direction of the largest variance, gradient, or surface curvature. A directional local maximum is a pixel that is not surrounded by pixels of higher grey values (where the lowest grey value corresponds to black, and the highest grey value being white) in a linear window overlaid on the image. Linear features in the image may either correspond to local maximum or minimum in the grey value of the pixels and accordingly the linear feature may be bright (high grey value) or dark (low grey value) depending on the object property and the imaging techniques used. The following description of the preferred embodiments will describe the detection of bright linear features only for succinctness. It will be appreciated by those skilled in the art that dark features may be similarly detected by simply inverting the intensity of the input image, or using non-minimum suppression rather than non-maximum suppression. It will also be appreciated by those skilled in the art that colour images may be modified such that colours of interest are assigned a higher “grey value”.