A fast piecewise deformable method for multi-modality image registration
Medical image fusion is becoming increasingly popular for enhancing diagnostic accuracy by intelligently 'fusing' information obtained from two different images. These images may be obtained from the same modality at different time instances or from multiple modalities recording complementary information. Due to the nature of the human body and also due to patient motion and breathing, there is a need for deformable registration algorithms in medical imaging. Typical nonparametric (deformable) registration algorithms such as the fluid-based, demons and curvature-based techniques are computationally intensive and have been demonstrated for mono-modality registrations only. We propose a fast and deformable algorithm using a 2-tiered strategy wherein a global MI-based affine registration is followed by a local piecewise refinement. We have tested this method on CT and PET images and validated the same using clinical experts

title: A novel progressive thick slab paradigm for volumetric medical image compression and navigation
abstract: In this paper, we propose a novel thick slab paradigm which provides an efficient scheme to navigate through large three dimensional (3-D) medical data sets within the framework of wavelet based lossless volumetric compression. We also propose a 3-D wavelet-like multi-resolution decomposition to perform 3-D image compression, which provides better compression ratio and 3 times faster decoding than JPEG-2000 for large data sets of computed tomography modality, along with a clinical workflow for navigation application. The proposed method suits radiology applications better than JPEG-2000 as it improves the productivity of radiologists by reducing their waiting time while the data is being transferred from a remote database over the network, along with reduction in diagnosis time, by providing an efficient navigation scheme with high compression performance.

title: ExScal: elements of an extreme scale wireless sensor network
abstract: Project ExScal (for extreme scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 13km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.

title: Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis
abstract: Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C_1 ) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier framework is particularly effective for difficult classification problems involving a moderately large number of classes. The proposed method is illustrated on a problem related to classification of landcover using hyperspectral data: a 12-class AVIRIS subset with 180 bands. For this problem, the classification accuracies obtained were superior to most other techniques developed for hyperspectral classification. Moreover, the class hierarchies that were automatically discovered conformed very well with human domain experts’ opinions, which demonstrates the potential of using such a modular learning approach for discovering domain knowledge automatically from data.

title: Recognition of human actions using moment based features and artificial neural networks
abstract: This paper presents performance of view-based approach in automated recognition of predefined hand and gross body actions using artificial neural network. This approach represents motion by a static grey scale image template computed by collapsing the temporal components into the cumulative image-difference of frames. The seven invariant Hu moments are used as the feature vectors. The performance of the system is tested in real time using feed forward multilayer perceptron (MLP) based on back propagation.

title: Man-made structure detection in natural images using a causal multiscale random field
abstract: This paper presents a generative model based approach to man-made structure detection in 2D (two-dimensional) natural images. The proposed approach uses a causal multiscale random field suggested by Bouman and Shapiro (1994) as a prior model on the class labels on the image sites. However, instead of assuming the conditional independence of the observed data, we propose to capture the local dependencies in the data using a multiscale feature vector. The distribution of the multiscale feature vectors is modeled as mixture of Gaussians. A set of robust multi-scale features is presented that captures the general statistical properties of man-made structures at multiple scales without relying on explicit edge detection. The proposed approach was validated on real-world images from the Corel data set, and a performance comparison with other techniques is presented.

title: MEGA: Molecular Evolutionary Genetics Analysis software for microcomputers
abstract: A computer program package called MEGA has been developed for estimating evolutionary distances, reconstructing phylogenetic trees and computing basic statistical quantities from molecular data. It is written in C++ and is intended to be used on IBM and IBM-compatible personal computers. In this program, various methods for estimating evolutionary distances from nucleotide and amino acid sequence data, three different methods of phylogenetic inference (UPGMA, neighbor-joining and maximum parsimony) and two statistical tests of topological differences are included. For the maximum parsimony method, new algorithms of branch-and-bound and heuristic searches are implemented. In addition, MEGA computes statistical quantities such as nucleotide and amino acid frequencies, transition/transversion biases, codon frequencies (codon usage tables), and the number of variable sites in specified segments in nucleotide and amino acid sequences. Advanced on-screen sequence data and phylogenetic-tree editors facilitate publication-quality outputs with a wide range of printers. Integrated and interactive designs, on-line context-sensitive helps, and a text-file editor make MEGA easy to use.