Thesis (PH. D.)--Michigan State University. Electrical Engineering, 2008. / Title from PDF t.p. (viewed on Aug. 28, 2009) Includes bibliographical references (p. 91-99). Also issued in print.

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Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains ix, 68 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 40-41).

Thesis (Ph. D.)--Lehigh University, 2006. / Includes vita. Includes bibliographical references (leaves 114-124).

In this thesis, a modified 2D Discrete Cosine Transform based electro-optic and IR image fusion algorithm is proposed and implemented on an FPGA platform. The platform is a custom FPGA board which uses ALTERA Stratix III family FPGA. The algorithm is also compared with state of the art image fusion algorithms by means of an image fusion software application GUI developed in Matlab&reg / . The proposed algorithm principally takes corresponding 4x4 pixel blocks of two images to be fused and transforms them by means of 2D Discrete Cosine Transform. Then, the L2 norm of each block is calculated and used as the weighting factor for the AC values of the fused image block. The DC value of the fused block is the arithmetic mean of the DC coefficients of both input blocks. Based on this mechanism, the whole two images are processed in such a way that the output image is a composition of the processed 4x4 blocks. The proposed algorithm performs well compared to the other state of the art image fusion algorithms both in subjective and objective quality evaluations. In hardware, v the implemented algorithm can accept input videos as fast as 65 MHz pixel clock with a resolution of 1024x768 @60 Hz.

Endometriosis is an oestrogen-dependent gynaecological condition defined as the presence of endometrial tissue outside the uterus cavity. The condition is predominantly found in women in their reproductive years, and associated with significant pelvic and abdominal chronic pain and infertility. The disease is believed to affect approximately 33% of women by a recent study. Currently, surgical intervention, often laparoscopic surgery, is the gold standard for diagnosing the disease and it remains an effective and common treatment method for all stages of endometriosis. Magnetic resonance imaging (MRI) of the patient is performed before surgery in order to locate any endometriosis lesions and to determine whether a multidisciplinary surgical team meeting is required. In this dissertation, our goal is to use image processing techniques to aid surgical planning. Specifically, we aim to improve quality of the existing images, and to automatically detect bladder endometriosis lesion in MR images as a form of bladder wall thickening. One of the main problems posed by abdominal MRI is the sparse anisotropic frequency sampling process. As a consequence, the resulting images consist of thick slices and have gaps between those slices. We have devised a method to fuse multi-view MRI consisting of axial/transverse, sagittal and coronal scans, in an attempt to restore an isotropic densely sampled frequency plane of the fused image. In addition, the proposed fusion method is steerable and is able to fuse component images in any orientation. To achieve this, we apply the Riesz transform for image decomposition and reconstruction in the frequency domain, and we propose an adaptive fusion rule to fuse multiple Riesz-components of images in different orientations. The adaptive fusion is parameterised and switches between combining frequency components via the mean and maximum rule, which is effectively a trade-off between smoothing the intrinsically noisy images while retaining the sharp delineation of features. We first validate the method using simulated images, and compare it with another fusion scheme using the discrete wavelet transform. The results show that the proposed method is better in both accuracy and computational time. Improvements of fused clinical images against unfused raw images are also illustrated. For the segmentation of the bladder wall, we investigate the level set approach. While the traditional gradient based feature detection is prone to intensity non-uniformity, we present a novel way to compute phase congruency as a reliable feature representation. In order to avoid the phase wrapping problem with inverse trigonometric functions, we devise a mathematically elegant and efficient way to combine multi-scale image features via geometric algebra. As opposed to the original phase congruency, the proposed method is more robust against noise and hence more suitable for clinical data. To address the practical issues in segmenting the bladder wall, we suggest two coupled level set frameworks to utilise information in two different MRI sequences of the same patients - the T2- and T1-weighted image. The results demonstrate a dramatic decrease in the number of failed segmentations done using a single kind of image. The resulting automated segmentations are finally validated by comparing to manual segmentations done in 2D.

In this thesis, image dynamic range enhancement methods are studied in order to solve the problem of representing high dynamic range scenes with low dynamic range images. For this purpose, two main image dynamic range enhancement methods, which are high dynamic range imaging and exposure fusion, are studied. More detailed analysis of exposure fusion algorithms are carried out because the whole enhancement process in the exposure fusion is performed in low dynamic range, and they do not need any prior information about input images. In order to evaluate the performances of exposure fusion algorithms, both objective and subjective quality metrics are used. Moreover, the correlation between the objective quality metrics and subjective ratings is studied in the experiments.