Accurate estimation of ICA weight matrix by implicit constraint imposition using Lie group.

This paper presents a new stochastic algorithm to optimize the independence criterion-mutual information-among multivariate data using local, global, and hybrid optimizers, in conjunction with techniques involving a Lie group and its corresponding Lie algebra, for implicit imposition of the orthonormality constraint among the estimated sources. The major advantage of the proposed algorithm is the increased accuracy with which the weight matrix in the independent component analysis (ICA) model is estimated, compared to conventional schemes. When the local optimizer with Lie group techniques and the fast fixed-point (fastICA) algorithm were experimented by inputting the same set of random vectors, the former method superseded the conventional one by producing accurate weight matrix estimates in a majority of the test cases. Importantly, in our approach, the use of a Lie group to "lock" the weight matrix estimates onto the constraint surface enabled easy realization of the hybrid optimizers to yield near-global-optimum solutions consistently in most of the test cases, compared to well-known global optimizers. The inherent computational overhead in the hybrid optimizers was lowered by preprocessing the input data and periodically integrating the local optimizers with the global one. The proposed algorithms were applied to six-dimensional multispectral satellite image data to emphasize their usefulness in terms of accurate ICA weight matrix estimation.

A robust 3-D IVUS transducer tracking using single-plane cineangiography.

During an intravascular ultrasound (IVUS) intervention, a catheter with an ultrasound transducer is introduced in the body through a blood vessel, and then, pulled back to image a sequence of vessel cross sections. Unfortunately, there is no 3-D information about the position and orientation of these cross-section planes, which makes them less informative. To position the IVUS images in space, some researchers have proposed complex stereoscopic procedures relying on biplane angiography to get two X-ray image sequences of the IVUS transducer trajectory along the catheter. To simplify this procedure, we and others have elaborated algorithms to recover the transducer 3-D trajectory with only a single view X-ray image sequence. In this paper, we present an improved method that provides both automated 2-D and 3-D transducer tracking based on pullback speed as a priori information. The proposed algorithm is robust to erratic pullback speed and is more accurate than the previous single-plane 3-D tracking methods.

Contour detection of the cornea from OCT radial images.

In this paper, we present a new approach for contour detection in the frame of OCT images of the cornea. With the aim of detecting upper and lower curves that define the cornea in radial slices, we have elaborated a three step specific method. The first step consists in finding epithelium and endothelium points (we call them anchor points). Then the image is preprocessed to enhance the contrast and to reduce speckle. In the final step both anchor points are used as starting points for the algorithm: we trace the contour of the cornea pixel by pixel from these two points with a weight criterion. Taking into account geometrical knowledge of the cornea enhances the robustness of the technique. We validate our approach on a bank of scans including low quality images.

Parameter estimation in stochastic mammogram model by heuristic optimization techniques.

The appearance of disproportionately large amounts of high-density breast parenchyma in mammograms has been found to be a strong indicator of the risk of developing breast cancer. Hence, the breast density model is popular for risk estimation or for monitoring breast density change in prevention or intervention programs. However, the efficiency of such a stochastic model depends on the accuracy of estimation of the model's parameter set. We propose a new approach-heuristic optimization-to estimate more accurately the model parameter set as compared to the conventional and popular expectation-maximization (EM) algorithm. After initial segmentation of a given mammogram, the finite generalized Gaussian mixture (FGGM) model is constructed by computing the statistics associated with different image regions. The model parameter set thus obtained is estimated by particle swarm optimization (PSO) and evolutionary programming (EP) techniques, where the objective function to be minimized is the relative entropy between the image histogram and the estimated density distributions. When our heuristic approach was applied to different categories of mammograms from the Mini-MIAS database, it yielded lower floor of estimation error in 109 out of 112 cases (97.3 %), and 101 out of 102 cases (99.0%), for the number of image regions being five and eight, respectively, with the added advantage of faster convergence rate, when compared to the EM approach. Besides, the estimated density model preserves the number of regions specified by the information-theoretic criteria in all the test cases, and the assessment of the segmentation results by radiologists is promising.