Volumetric texture description and discriminant feature selection for MRI

This paper considers the problem of classification of Magnetic Resonance Images using 2D and 3D texture measures. Joint statistics such as co-occurrence matrices are common for analysing texture in 2D since they are simple and effective to implement. However, the computational complexity can be prohibitive especially in 3D. In this work, we develop a texture classification strategy by a sub-band filtering technique that can be extended to 3D. We further propose a feature selection technique based on the Bhattacharyya distance measure that reduces the number of features required for the classification by selecting a set of discriminant features conditioned on a set training texture samples. We describe and illustrate the methodology by quantitatively analysing a series of images: 2D synthetic phantom, 2D natural textures, and MRI of human knees

Determination of Rh(D) genotype: use of human monoclonal antibodies in flow cytometry.

The proper orthogonal decomposition (POD) has been widely used in fluid dynamic applications for extracting dominant flow features. The “gappy” POD is an extension to this method that allows the consideration of incomplete data sets. In this paper, the gappy POD is extended to handle unsteady flow reconstruction problems, such as those encountered when limited flow measurement data is available. In addition, a systematic approach for effective sensor placement is formulated within the gappy framework. Two applications are considered. The first aims to reconstruct the unsteady flow field using a small number of surface pressure measurements for a subsonic airfoil undergoing plunging motion. The second considers estimation of POD modal content of a cylinder wake flow for active control purposes. In both cases, using the dominant POD basis vectors and a small number of sensor signals, the gappy approach is found to yield accurate flow reconstruction results.Singapore-MIT Alliance (SMA

A new matching algorithm for high resolution mass spectra

AbstractWe present a new matching algorithm designed to compare high-resolution spectra. Whereas existing methods are bound to compare fixed intervals of ion masses, the accurate mass spectrum (AMS) distance method presented here is independent of any alignment. Based on the Jeffreys-Matusitas (JM) distance, a difference between observed peaks across pairs of spectra can be calculated, and used to find a unique correspondence between the peaks. The method takes into account that there may be differences in resolution of the spectra. The algorithm is used for indexing in a database containing 80 accurate mass spectra from an analysis of extracts of 80 isolates representing the nine closely related species in the Penicillium series Viridicata. Using this algorithm we can obtain a retrieval performance of ≈97–98% that is comparable with the best of the existing methods (e.g., the dot-product distance). Furthermore, the presented method is independent of any variable alignment procedures or binning

A Multi-Agent Based Approach To Clustering: Harnessing The Power of Agents

Abstract. A framework for multi-agent based clustering is described whereby individual agents represent individual clusters. A particular feature of the framework is that, after an initial cluster configuration has been generated, the agents are able to negotiate with a view to improving on this initial clustering. The framework can be used in the context of a number of clustering paradigms, two are investigated: K-means and KNN. The reported evaluation demonstrates that negotiation can serve to improve on an initial cluster configuration