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Fast acquisition of MR images is possible through sparse encoding in the Fourier/Wavelet domain under the incoherent measurement constraint required by the theory of compressed sensing (CS) for stable reconstruction. In one such sparse encoding method, we utilize the wavelet tree structure to undersample the wavelet-encoded MRI k-spaces with tailored spatially-selective RF excitation pulses. The resulting undersampled k-spaces contain many more significant coefficients than randomly undersampled k-spaces. Thus, the quality of CS reconstruction based on these undersampled k-spaces is improved, and such an encoding scheme may reduce the patient scan time for MRI and fMRI. Using a fully sampled Fourier encoded 3-D digital brain phantom as the gold standard, a mathematical framework with full-reference and visual image quality matrices has been proposed to assess the CS reconstruction performance in wavelet-encoded MRI. The quality of MR images recovered from undersampled k-space by different reconstruction methods is computed. The undersampling rates and noise levels in k-space are considered in evaluating the robustness of CS reconstruction. The simulation results show that the performance of CS reconstruction in wavelet-encoded MRI is more accurate and stable than in Fourier-encoded MRI with the same undersampling rate and noise level, at a significantly reduced scan time.

Traditional MRI scanners acquire samples of the encoded image in a spatial frequency (Fourier) domain, known as k-space. The recently developed theory of Compressed Sensing (CS) has shown that a natural image could be acquired and reconstructed from a reduced number of linear projection measurements at sub-Nyquist sample rates. CS is well suited to fast acquisition of MRI by sparse encoding in k-space. However, the commonly used Fourier-encoding MRI scheme only weakly satisfies the incoherent measurements constraint required for CS. MR images can be reconstructed from a more sparse wavelet-encoded k-space than from Fourier-encoded k-space with the same resolution. Wavelet-encoded MRI is flexible in spatially-selective excitations to yield an encoding scheme similar to the "universal encoding". In this work, a CS-MRI scheme has been investigated to accurately reconstruct MR images from sparsely wavelet-encoded k-space. This sparse encoding is achieved with tailored spatially-selective RF pulses, which are designed with Battle-Lemarie wavelet functions. The Fourier transforms of the Battle-Lemarie wavelet functions used as RF pulse profiles are smooth and decay rapidly to zero. This technique provides short RF pulses with relatively precise spatial excitation profiles. Simulator results show that the proposed encoding scheme has different characteristics than conventional Fourier and wavelet encoding methods, and this scheme could be applied to fast MR image acquisition at relatively high resolution.

This work aims at automated segmentation of major lesions observed in early stages of uterine cervical cancer. Automated segmentation reduces subjective variability and cost in current manual evaluation methods used to determine the biopsy locations for diagnosis. Two different methods, a non-convex optimisation approach and mathematical morphological approach, are used to segment the aceto-white region. Within this region other abnormalities, such as mosaic patterns, are classified by fuzzy c-means using a textural feature obtained from skeletonised vascular structures. These vascular structures are extracted by a series of morphological operations. Minimisation of uncertainties for degraded images is also discussed.

Automated detection of subtle changes in sequential images requires development of procedures that ensure true subtraction of two images by proper registration and normalization of illumination. The difference image can then be further processed to analyze the spectral components and the gray level variations. An innovative and generalized approach to accurate image subtraction incorporating illumination normalization and correction for translation, rotation, and scale difference in two sequential images is considered. The entire procedure yielding true image subtraction has been implemented in a PC-based image processing system and can be executed by menu-driven operations. The effectiveness of this approach is illustrated through subtraction of registered sequential fundus images and analysis of the spectral and gray-level variations of the difference images.

The coupling of carefully designed experiments with proper analysis of functional magnetic resonance imaging (fMRI) data provides us with a powerful as well as noninvasive tool to help us understand cognitive processes associated with specific brain regions and hence could be used to detect abnormalities induced by a diseased state. The hypothesisdriven General Linear Model (GLM) and the data-driven Independent Component Analysis (ICA) model are the two most commonly used models for fMRI data analysis. A hybrid ICA-GLM model combines the two models to take advantages of benefits from both models to achieve more accurate mapping of the stimulus-induced activated brain regions. We propose a modified hybrid ICA-GLM model with probabilistic ICA that includes a noise model. In this modified hybrid model, a probabilistic principle component analysis (PPCA)-based component number estimation is used in the ICA stage to extract the intrinsic number of original time courses. In addition, frequency matching is introduced into the time course selection stage, along with temporal correlation, F-test based model fitting estimation, and time course combination, to produce a more accurate design matrix for GLM. A standard fMRI dataset is used to compare the results of applying GLM and the proposed hybrid ICA-GLM in generating activation maps.

Many common modalities of medical images acquire high-resolution and multispectral images, which are subsequently processed, visualized, and transmitted by subsampling. These subsampled images compromise resolution for processing ability, thus risking loss of significant diagnostic information. A hybrid multiresolution vector quantizer (HMVQ) has been developed exploiting the statistical characteristics of the features in a multiresolution wavelet-transformed domain. The global codebook generated by HMVQ, using a combination of multiresolution vector quantization and residual scalar encoding, retains edge information better and avoids significant blurring observed in reconstructed medical images by other well-known encoding schemes at low bit rates. Two specific image modalities, namely, X-ray radiographic and magnetic resonance imaging (MRI), have been considered as examples. The ability of HMVQ in reconstructing high-fidelity images at low bit rates makes it particularly desirable for medical image encoding and fast transmission of 3D medical images generated from multiview stereo pairs for visual communications.