@article {218, title = {An adaptive Monte Carlo approach to phase-based multimodal image registration}, journal = {IEEE Transactions on Information Technology in Biomedicine}, volume = {14}, year = {2010}, pages = {173 - 179}, abstract = {

In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations, using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson type-VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations, where little to no overlapping regions exist.

}, keywords = {adaptive Monte Carlo scheme, adaptive sampling approach, Algorithms, Computer-Assisted, Diagnostic Imaging, geometric transformation, Humans, image interpretation, image phase moments, image processing, image registration, iterative methods, Least-Squares Analysis, medical image processing, Monte Carlo Method, Monte Carlo methods, multidimensional sampling distribution, multidimensional solution space, multiresolution algorithm, optimal image alignment, Pearson type VII error, phase based multimodal image registration, random solution candidate generation, sampling methods}, issn = {1089-7771}, doi = {http://dx.doi.org/10.1109/TITB.2009.2035693}, author = {A Wong} } @article {234, title = {Interactive modeling and evaluation of tumor growth}, journal = {Journal of Digital Imaging}, volume = {23}, year = {2010}, pages = {755 - 768}, abstract = {

This paper addresses the need to quantify tumor growth and detect changes as this information is relevant to manage the patient treatment and to aid biotechnological efforts to cure cancer (Silva et al. 2008). An interactive tumor segmentation technique is used to recover the shape and size of tumors without imposing shape constraints. This segmentation algorithm provides good convergence, is robust to the initialization conditions, and requires simple and intuitive user interactions. A parametric approach to model tumor growth analytically is proposed in this paper. The preliminary experimental results are encouraging. The segmentation method is shown to be robust and simple to use, even in situations where the tumor boundary definition is challenging. Also, the experiments indicate that the proposed model potentially can be used to extrapolate the available data and help predict the tumor size (assuming unconstrained growth). Additionally, the proposed method potentially can provide a quantitative reference to compare the tumor shrinkage rate in cancer treatments.

}, keywords = {Active contours, cancer detection, chest radiographs, Computed tomography (CT), computer analysis, Computer Vision, computer-assisted diagnosis, Diagnostic Imaging, digital image processing, image segmentation, lung cancer}, issn = {0897-1889}, url = {http://dx.doi.org/10.1007/s10278-009-9234-4}, author = {D Koff and J Scharcanski and L da Silva and A Wong} } @inproceedings {330, title = {Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)}, booktitle = {30th Annual Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2008}, month = {08/2008}, address = {Vancouver, British Columbia, Canada}, abstract = {

A novel interactive approach called Enhanced Intelligent Scissors (EIS) is presented for segmenting regions of interest in medical images. The proposed interactive medical image segmentation algorithm addresses the issues associated with segmenting medical images and allows for fast, robust, and flexible segmentation without requiring accurate manual tracing. A robust complex wavelet phase-based representation is used as an external local cost to address issues associated with contrast non-uniformities and noise typically found in medical images. The boundary extraction problem is formulated as a Hidden Markov Model (HMM) and the novel approach to the second-order Viterbi algorithm with state pruning is used to find the optimal boundary in a robust and efficient manner based on the extracted external and internal local costs, thus handling much inexact user boundary definitions than existing methods. Experimental results using MR and CT images show that the proposed algorithm achieves accurate segmentation in medical images without the need for accurate boundary definition as per existing Intelligent Scissors methods. Furthermore, usability testing indicate that the proposed algorithm requires significantly less user interaction than Intelligent Scissors.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, Diagnostic Imaging, Humans, image enhancement, image interpretation, Information Storage and Retrieval, Markov Chains, Models, Pattern Recognition, Reproducibility of Results, Statistical, Visible Human Projects}, doi = {http://dx.doi.org/10.1109/IEMBS.2008.4649855}, author = {A Mishra and A Wong and W Zhang and D A. Clausi and P Fieguth} }