Existing polyp detection methods rely heavily on curvature-based characteristics to differentiate between lesions. These assume that the discrete triangulated surface mesh or volume closely approximates a smooth continuous surface. However, this is often not the case and because curvature is computed as a local feature and a second-order differential quantity, the presence of noise significantly affects its estimation. For this reason, a more global feature is required to provide an accurate description of the surface at hand. In this paper, a novel method incorporating a local neighborhood around the centroid of a surface patch is proposed. This is done using geodesic rings which accumulate curvature information in a neighborhood around this centroid. This geodesic-ring neighborhood approximates a single smooth, continuous surface upon which curvature and orientation estimation methods can be applied. A new global shape index, S is also introduced and computed. These curvature and orientation values will be used to classify the surface as either a bulbous polyp, ridge-like fold or semiplanar structure. Experimental results show that this method is promising (100% sensitivity, 100% specificity for lesions > 10 mm) for distinguishing between bulbous polyps, folds and planar-like structures in the colon.

Existing polyp detection methods rely heavily on curvature-based characteristics to differentiate between lesions. However, as curvature is a local feature and a second order differential quantity, simply inspecting the curvature at a point is not sufficient. In this paper, we propose to inspect a local neighbourhood around a candidate point using curvature maps. This candidate point is pre-identified using the geodesic centroid of a surface patch containing vertices with positive point curvature values corresponding to convex shaped protrusions. Geodesic rings are then constructed around this candidate point and point curvatures around these rings are accumulated to produce curvature maps. From this, a cumulative shape property, S for a given neighbourhood radius can be computed and used for identifying bulbous polyps which typically have a high S value, and its corresponding 'neck' region. We show that a threshold value of S > 0.48 is sufficient to discriminate between polyps and non polyps with 100% sensitivity and specificity for bulbous polyps > 10mm.

Computed tomographic colonography (CTC) is a promising alternative to traditional invasive colonoscopic methods used in the detection and removal of cancerous growths, or polyps in the colon. Existing algorithms for CTC typically use a classifier to discriminate between true and false positives generated by a polyp candidate detection system. However, these classifiers often suffer from a phenomenon termed the curse of dimensionality, whereby there is a marked degradation in the performance of a classifier as the number of features used in the classifier is increased. In addition an increase in the number of features used also contributes to an increase in computational complexity and demands on storage space. This paper demonstrates the benefits of feature selection with the aim at increasing specificity while preserving sensitivity in a polyp detection system. It also compares the performances of an individual (F-score) and mutual information (MI) method for feature selection on a polyp candidate database, in order to select a subset of features for optimum CAD performance. Experimental results show that the performance of SVM+MI seems to be better for a small number of features used, but the SVM+Fscore method seems to dominate when using the 30-50 best ranked features. On the whole, the AUC measures are able to reach 0.8-0.85 for the top ranked 20-40 features using MI or F-score methods compared with 0.65-0.7 when using all 100 features in the worst-case scenario.

A fundamental question in functional MRI (fMRI) data analysis is to declare pixels either activated or non-activated with respect to the experimental design. A new statistical test for detecting activated pixels in fMRI data is proposed. The test is based on comparing the dimension of the parametric models fitted to the voxels fMRI time series data with and without controlled activation-baseline pattern. The Bayesian information criterion, is used for this comparison. This test has the advantage of not requiring any user-specified threshold to be estimated. The effectiveness of the proposed fMRI activation detection method is illustrated on real experimental data.