Spatio-temporal feature extraction represents a challenge however critical step for the differential diagnosis of
non-mass-enhancing lesions. The atypical dynamical behavior of these lesions paired with non well-defined tumor
borders requires novel approaches to obtain representative features for a subsequent automated diagnosis. We evaluate the performance of mappings of pixelwise kinetic features within a tumor, morphological descriptors based on Minkowski functionals and a novel technique, the Zernike velocity moments, to capture the joint spatio-
temporal behavior within an image sequence. The highest sensitivity is achieved by the Zernike velocity moments
proving thus that dynamical and morphological behavior can not be separately analyzed based on features
extracted only for a distinct behavior or as a feature combination of these two but has to be a simultaneous
measure of these. The present paper provides the most detailed automated diagnosis of non-mass-enhancing
lesions so far in the literature.
The evaluation of kinetic and/or morphologic characteristics of non-masses represents a challenging task for an
automated analysis and is of crucial importance for advancing current computer-aided diagnosis (CAD) systems.
Compared to the well-characterized mass-enhancing lesions, non-masses have not well-dened and blurred tumor
borders and a kinetic behavior that is not easily generalizable and thus discriminative for malignant and benign
non-masses. To overcome these diculties and pave the way for novel CAD systems for non-masses, we will
evaluate several kinetic and morphologic descriptors separately, and a novel technique, the Zernike velocity
moments, to capture the joint spatio-temporal behavior of these lesions. We additionally consider the impact of
non-rigid motion compensation on a correct diagnosis.
Harmful algal blooms are believed to be increasing in occurrence and their toxins can be concentrated by filter-feeding shellfish and cause amnesia or paralysis when ingested. As a result fisheries and beaches in the vicinity of blooms may need to be closed and the local population informed. For this avoidance planning timely information on the existence of a bloom, its species and an accurate map of its extent would be prudent. Current research to detect these blooms from space has mainly concentrated on spectral approaches towards determining species. We present a novel statistics-based background-subtraction technique that produces improved descriptions of an anomaly's extent from remotely-sensed ocean colour data. This is achieved by extracting bulk information from a background model; this is complemented by a computer vision ramp filtering technique to specifically detect the perimeter of the anomaly. The complete extraction technique uses temporal-variance estimates which control the subtraction of the scene of interest from the time-weighted background estimate, producing confidence maps of anomaly extent. Through the variance estimates the method learns the associated noise present in the data sequence, providing robustness, and allowing generic application. Further, the use of the median for the background model reduces the effects of anomalies that appear within the time sequence used to generate it, allowing seasonal variations in the background levels to be closely followed. To illustrate the detection algorithm's application, it has been applied to two spectrally different oceanic regions.