The ability to detect features within confocal microscope images is important for the interpretation and analysis of such data. Most detectors are gradient-based, and so are sensitive to noise, and fail to accurately locate some feature types that are important in confocal microscopy. The local energy feature detector developed by Morrone and Owens marks locations where there is maximal congruence of phase in the Fourier components of an image. Points of maximal phase congruency occur at all common feature types: step and roof edges, line features and Mach bands. A 3D implementation of the local energy feature detector, suitable for confocal microscope data, is presented. The detector computes local energy by convolving an image with oriented pairs of 3D filters that are 3D versions of Morlet wavelets. To increase the speed of the convolution, the filters are designed in frequency-space and multiplied by the image's Fourier transform. Results are presented for real confocal images and a synthetic 3D image volume. These results are compared with those from a 3D implementation of the Sobel edge detector.

Accurate detection and localisation of two-dimensional (2D) image features (or 'key-points') is important for vision tasks such as structure from motion, stereo matching and line labelling. Despite this interest, no one has produced an adequate definition of 2D image features that encompasses the variety of features that should be included under this banner. In this paper, we present a new method for the detection of 2D image features that relies upon maximal 2D order in the phase domain of the image signal. Points of maximal phase congruency correspond to all the different types of 2D features detected by other schemes, including grey-level corners, line terminations, and a variety of junctions. An assessment of our implementation's performance is provided, in terms of its robustness, accuracy of detection and localisation of 2D image features.