Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

The (semi-) automatic detection of significant (feature) points is a key task in in vivo assessment of cancer staging and progression. However, this is a challenging task due to the relatively poor signalto- noise, limited resolution and variable intensity of medical images. We propose to use phase congruence (PC), the Morrone and Owens (1987) feature model, to extract local descriptors. We overcome the limitations of the currently accepted PC measure, estimate PC without using an image energy weighting factor. We show that: (i) relative phase values from a single scale are not equivalent to phase values from PC, and should not be used to assess local image structure; and (ii) our approach results in higher specificity to features of interest, and lower sensitivity to noise, as demonstrated in in vitro microscopy (e.g. tumour microvessels) and in vivo pre-clinical pancreatic cancer images. © 2009 IEEE.

Original publication




Conference paper

Publication Date



1219 - 1222