In this paper, we present a framework that one could use to set optimized parameter values, while performing
image registration using mutual information as a metric to be maximized. Our experiment details these steps
for the registration of X-ray Computer Tomography (CT) images with Positron Emission Tomography (PET)
images. Selection of different parameters that influence the mutual information between two images is crucial
for both accuracy and speed of registration. These implementation issues need to be handled in an orderly
fashion by designing experiments in their operating ranges. The conclusions from this study seem vital towards
obtaining allowable parameter range for a fusion software.
In this paper, we propose a study, which investigates the first-order and second-order distributions of T2 images from a magnetic resonance (MR) scan for an age-matched data set of 24 Alzheimer's disease and 17 normal patients. The study is motivated by the desire to analyze the brain iron uptake in the hippocampus of Alzheimer's patients, which is captured by low T2 values. Since, excess iron deposition occurs locally in certain regions of the brain, we are motivated to investigate the spatial distribution of T2, which is captured by higher-order statistics. Based on the first-order and second-order distributions (involving gray level co-occurrence matrix) of T2, we show that the second-order statistics provide features with sensitivity >90% (at 80% specificity), which in turn capture the textural content in T2 data. Hence, we argue that different texture characteristics of T2 in the hippocampus for Alzheimer's and normal patients could be used as an early indicator of Alzheimer's disease.
In this paper, we propose a block-based conditional entropy coding
scheme for medical image compression using the 2-D integer Haar
wavelet transform. The main motivation to pursue conditional
entropy coding is that the first-order conditional entropy is
always theoretically lesser than the first and second-order
entropies. We propose a sub-optimal scan order and an optimum
block size to perform conditional entropy coding for various
modalities. We also propose that a similar scheme can be used to
obtain a sub-optimal scan order and an optimum block size for
other wavelets. The proposed approach is motivated by a desire to
perform better than JPEG2000 in terms of compression ratio. We
hint towards developing a block-based conditional entropy coder,
which has the potential to perform better than JPEG2000. Though we
don't indicate a method to achieve the first-order conditional
entropy coder, the use of conditional adaptive arithmetic coder
would achieve arbitrarily close to the theoretical conditional
entropy. All the results in this paper are based on the medical
image data set of various bit-depths and various modalities.
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