On improving the efficiency of tensor voting.

This paper proposes two alternative formulations to reduce the high computational complexity of tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. The first scheme consists of numerical approximations of the votes, which have been derived from an in-depth analysis of the plate and ball voting processes. The second scheme simplifies the formulation while keeping the same perceptual meaning of the original tensor voting: The stick tensor voting and the stick component of the plate tensor voting must reinforce surfaceness, the plate components of both the plate and ball tensor voting must boost curveness, whereas junctionness must be strengthened by the ball component of the ball tensor voting. Two new parameters have been proposed for the second formulation in order to control the potentially conflictive influence of the stick component of the plate vote and the ball component of the ball vote. Results show that the proposed formulations can be used in applications where efficiency is an issue since they have a complexity of order O(1). Moreover, the second proposed formulation has been shown to be more appropriate than the original tensor voting for estimating saliencies by appropriately setting the two new parameters.

Novel immunogenic antigens increase classification accuracy in meningioma to 93.84%.

There is growing evidence that simultaneous analysis of multiple autoantibody reactions can be utilized for diagnosis of neoplasms. Using a set of 57 meningioma-associated antigens, we recently separated meningioma patients from individuals without known disease with an accuracy of 90.3%. Here, we ask whether a largely increased set of immunogenic antigens can further improve this discrimination. We used an array with 1,827 human recombinant clones and measured reactivity of serum autoantibodies against the clones by a novel automated image analysis procedure. We were able to separate meningioma sera from sera of healthy controls with a specificity of 95.62%, a sensitivity of 91.83% and an accuracy of 93.84%. Of the analyzed clones, 23 in-frame clones were highly informative for the classification of meningioma vs. normal sera as shown by their AUC values. These results demonstrate that the accuracy of a serum-based diagnostic can be readily and considerably improved by screening extended sets of proteins.

Improving seroreactivity-based detection of glioma.

Seroreactivity profiling emerges as valuable technique for minimal invasive cancer detection. Recently, we provided first evidence for the applicability of serum profiling of glioma using a limited number of immunogenic antigens. Here, we screened 57 glioma and 60 healthy sera for autoantibodies against 1827 Escherichia coli expressed clones, including 509 in-frame peptide sequences. By a linear support vector machine approach, we calculated mean specificity, sensitivity, and accuracy of 100 repetitive classifications. We were able to differentiate glioma sera from sera of the healthy controls with a specificity of 90.28%, a sensitivity of 87.31% and an accuracy of 88.84%. We were also able to differentiate World Health Organization grade IV glioma sera from healthy sera with a specificity of 98.45%, a sensitivity of 80.93%, and an accuracy of 92.88%. To rank the antigens according to their information content, we computed the area under the receiver operator characteristic curve value for each clone. Altogether, we found 46 immunogenic clones including 16 in-frame clones that were informative for the classification of glioma sera versus healthy sera. For the separation of glioblastoma versus healthy sera, we found 91 informative clones including 26 in-frame clones. The best-suited in-frame clone for the classification glioma sera versus healthy sera corresponded to the vimentin gene (VIM) that was previously associated with glioma. In the future, autoantibody signatures in glioma not only may prove useful for diagnosis but also offer the prospect for a personalized immune-based therapy.