@inproceedings {199, title = {Despeckling vs averaging of retinal UHROCT tomograms: advantages and limitations}, booktitle = {Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XV}, year = {2011}, publisher = {SPIE}, organization = {SPIE}, abstract = {

Imaging time can be reduced using despeckled tomograms, which have similar image metrics to those obtained by averaging several low speed tomograms or many high speed tomograms. Quantitative analysis was used to compare the performance of two speckle denoising approaches, algorithmic despeckling and frame averaging, as applied to retinal OCT images. Human retinal tomograms were acquired from healthy subjects with a research grade 1060nm spectral domain UHROCT system with 5μm axial resolution in the retina. Single cross-sectional retinal tomograms were processed with a novel speckle denoising algorithm and compared with frame averaged retinal images acquired at the same location. Image quality metrics such as the image SNR and contrast-to-noise ratio (CNR) were evaluated for both cases.

}, keywords = {biomedical optics, image processing, medical imaging, optical coherence tomography, speckle}, doi = {http://dx.doi.org/10.1117/12.875867}, url = {http://link.aip.org/link/?PSI/7889/78893C/1}, author = {D D. Lee and K Bizheva and A Wong and J A. Eichel and P Fieguth and D A. Clausi}, editor = {J A. Izatt and J G. Fujimoto and V V. Tuchin} } @inproceedings {402, title = {Efficient and robust non-rigid least-squares rectification of medical images}, booktitle = {2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition}, year = {2006}, address = {Las Vegas, Nevada}, abstract = {

One of the major problems facing medical imaging is the presence of geometric distortions inherent in an imaging technique. Image registration techniques are often used to correct for such geometric perturbations. Recently, it was proposed that the SSD cost function can be evaluated efficiently using the Fast Fourier Transform (FFT) to determine the optimal translation between two images. However, spatial distortions in medical images can be highly non-rigid in nature. This paper extends this efficient approach to allow for non-rigid alignment between two images through the use of patch correspondence and robust statistical model estimation techniques. This feature-based method is designed to be highly robust, making it suitable for aligning medical images with various forms of geometric distortions. Experimental results demonstrate high overall image rectification performance.

}, keywords = {image rectification, least-squares, medical imaging, registration, robust statistics}, author = {A Wong and J Orchard} } @inproceedings {404, title = {Efficient multi-modal least-squares alignment of medical images using quasi-orientation maps}, booktitle = {2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition}, year = {2006}, address = {Las Vegas, Nevada}, abstract = {

In image registration, similarity metrics are used to determine the optimal alignment between two images. A common metric used for judging image similarity is the weighted sum of squared differences (SSD) cost function. Recently, it was demonstrated that the evaluation of the SSD cost function can be performed efficiently using the Fast Fourier Transform (FFT) to determine the optimal translation between two images based on pixel intensities. This paper extends this efficient approach by introducing the concept of quasiorientation maps as features into the alignment framework. This feature-based method is invariant to intensity mappings, making it suitable for aligning medical images acquired with different modalities. Experimental results demonstrate overall multi-modal image alignment performance to be superior to that of previous work.

}, keywords = {image alignment, least-squares, medical imaging, multi-modal registration, orientation matching}, author = {A Wong and W Bishop and J Orchard} } @article {576, title = {Multiscale statistical methods for the segmentation of signals and images}, journal = {IEEE Transactions on Image Processing}, volume = {9}, year = {2000}, pages = {456 - 468}, abstract = {

This paper addresses the problem of both segmenting and reconstructing a noisy signal or image. The work is motivated by large problems arising in certain scientific applications, such as medical imaging. Two objectives for a segmentation and denoising algorithm are laid out: it should be computationally efficient and capable of generating statistics for the errors in the reconstruction and estimates of the boundary locations. The starting point for the development of a suitable algorithm is a variational approach to segmentation (Shah 1992). This paper then develops a precise statistical interpretation of a one dimensional (1-D) version of this variational approach to segmentation. The 1-D algorithm that arises as a result of this analysis is computationally efficient and capable of generating error statistics. A straightforward extension of this algorithm to two dimensions would incorporate recursive procedures for computing estimates of inhomogeneous Gaussian Markov random fields. Such procedures require an unacceptably large number of operations. To meet the objective of developing a computationally efficient algorithm, the use of previously developed multiscale statistical methods is investigated. This results in the development of an algorithm for segmenting and denoising which is not only computationally efficient but also capable of generating error statistics, as desired

}, keywords = {biomedical MRI, boundary locations, brain, denoising, error statistics, Gaussian processes, image reconstruction, image segmentation, images, inhomogeneous Gaussian Markov random fields, interference suppression, Markov processes, medical image processing, medical imaging, multiscale methods, noisy image, noisy signal, reconstruction, recursive procedures, scientific applications, Segmentation, signal reconstruction, signals, statistical interpretation, variational approach, variational techniques}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/83.826782}, author = {M Schneider and P Fieguth and W C. Karl and A S. Willsky} }