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Computational imaging (CI) systems are hybrid imagers in which the optical and post-processing sub-systems are jointly optimized to maximize the task-specific performance. In this dissertation we consider a form of CI system that measures the linear projections (i.e., features) of the scene optically, and it is commonly referred to as feature-specific imaging (FSI). Most of the previous work on FSI has been concerned with image reconstruction. Previous FSI techniques have also been non-adaptive and restricted to the use of ambient illumination.We consider two novel extensions of the FSI system in this work. We first present an adaptive feature-specific imaging (AFSI) system and consider its application to a face-recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. We present both statistical and information-theoretic adaptation mechanisms for the AFSI system. The sequential hypothesis testing framework is used to determine the number of measurements required for achieving a specified misclassification probability. We demonstrate that AFSI system requires significantly fewer measurements than static-FSI (SFSI) and conventional imaging at low signal-to-noise ratio (SNR). We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage. Experimental results validating the AFSI system are presented.Next we present a FSI system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all of the reflected light onto a single photodetector. We refer to this system as feature-specific structured imaging (FSSI). Principal component features are used to define the illumination patterns. The optimal LMMSE operator is used to generate object estimates from the measurements. We demonstrate that this new imaging approach reduces imager complexity and provides improved image quality in high noise environments. We then generalize the FSSI system by making use of random projections (i.e., using no object prior) to define the illumination patterns. Object estimates are generated using L1-norm minimization and gradient-projection sparse reconstruction algorithms. Experimental results validating the FSSI system are presented.

Highly metastatic cancer cells are more likely to escape and form metastases, and only minimal improvements in treatment can be achieved. Despite metas- tases being the primary cause of cancer-related mortality, they often proceed unnoticed. Current imaging modalities rely solely on the morphological fea- tures of the tumor for characterization, rather than cellular differences. Our goal is to develop an MR cellular imaging capability for characterizing the po- tential of breast cancer cells to metastasize and enable early cancer detection using manganese. Experiments on breast cell lines demonstrated that aggres- sive cancer cells significantly enhanced on T1 -weighted MR images as a result of a higher uptake and retention of manganese. These results suggest that dif- ferences in uptake of manganese can help the detection and characterization of breast cancers. The proposed technique can also be useful for other cancers, and could bring a critically needed dimension to cancer imaging.

Highly metastatic cancer cells are more likely to escape and form metastases, and only minimal improvements in treatment can be achieved. Despite metas- tases being the primary cause of cancer-related mortality, they often proceed unnoticed. Current imaging modalities rely solely on the morphological fea- tures of the tumor for characterization, rather than cellular differences. Our goal is to develop an MR cellular imaging capability for characterizing the po- tential of breast cancer cells to metastasize and enable early cancer detection using manganese. Experiments on breast cell lines demonstrated that aggres- sive cancer cells significantly enhanced on T1 -weighted MR images as a result of a higher uptake and retention of manganese. These results suggest that dif- ferences in uptake of manganese can help the detection and characterization of breast cancers. The proposed technique can also be useful for other cancers, and could bring a critically needed dimension to cancer imaging.

Treatment response assessment in advanced head and neck cancer patients using Positron Emission Tomography (PET) has potential to provide significant clinical benefit. PET quantification methods can be either static or dynamic. The static approach is simple and is widely used. The simplified dynamic PET quantification method is a promising approach as it provides a reasonable trade-off between accuracy and clinical practicality. This method requires a blood sample which makes it not ideal since the PET quantification accuracy may be compromised due to small activity and volume of the blood sample. The implementation of image-based simplified dynamic PET quantification in head and neck cancer patients requires partial volume correction due to small vessel sizes and limitted PET spatial resolution. The objective of this thesis is to evaluate the accuracy of current PET quantification methods for response assessment in advanced head and neck cancer patients and to develop a novel and robust partial volume correction technique to improve PET quantification. First, the static PET quantification method using fixed size ROI is evaluated. Significant variation in response assessment was observed suggesting that static PET quantification using a fixed-size ROI should be approached with caution in heterogeneous tumours. Second, the accuracy of blood activity measurements and its effect on the accuracy of quantitative response assessment is evaluated. Significant inaccuracies in the blood sample based simplified dynamic PET quantification method are identified. The results support a need to develop an image-based simplified dynamic PET quantification method with partial volume correction. Finally, a novel partial volume correction technique was developed, validated, and its robustness was investigated. In comparison to previously published partial volume correction techniques, it performed better with noisy PET images and it was more robust for errors in PET-CT registration. The partial volume correction technique was also implemented and validated in sinogram space to provide additional advantages such as applicability to iterative reconstructions. The proposed partial volume correction technique enables the use of image-based simplified dynamic PET quantification in advanced head and neck cancer patients. Furthermore, the technique establishes a framework for future research to address the inherent low spatial resolution of PET.

Treatment response assessment in advanced head and neck cancer patients using Positron Emission Tomography (PET) has potential to provide significant clinical benefit. PET quantification methods can be either static or dynamic. The static approach is simple and is widely used. The simplified dynamic PET quantification method is a promising approach as it provides a reasonable trade-off between accuracy and clinical practicality. This method requires a blood sample which makes it not ideal since the PET quantification accuracy may be compromised due to small activity and volume of the blood sample. The implementation of image-based simplified dynamic PET quantification in head and neck cancer patients requires partial volume correction due to small vessel sizes and limitted PET spatial resolution. The objective of this thesis is to evaluate the accuracy of current PET quantification methods for response assessment in advanced head and neck cancer patients and to develop a novel and robust partial volume correction technique to improve PET quantification. First, the static PET quantification method using fixed size ROI is evaluated. Significant variation in response assessment was observed suggesting that static PET quantification using a fixed-size ROI should be approached with caution in heterogeneous tumours. Second, the accuracy of blood activity measurements and its effect on the accuracy of quantitative response assessment is evaluated. Significant inaccuracies in the blood sample based simplified dynamic PET quantification method are identified. The results support a need to develop an image-based simplified dynamic PET quantification method with partial volume correction. Finally, a novel partial volume correction technique was developed, validated, and its robustness was investigated. In comparison to previously published partial volume correction techniques, it performed better with noisy PET images and it was more robust for errors in PET-CT registration. The partial volume correction technique was also implemented and validated in sinogram space to provide additional advantages such as applicability to iterative reconstructions. The proposed partial volume correction technique enables the use of image-based simplified dynamic PET quantification in advanced head and neck cancer patients. Furthermore, the technique establishes a framework for future research to address the inherent low spatial resolution of PET.

A pose estimation method is proposed for measuring the position and orientation of a biopsy needle. The technique is to be used as a touchless needle guide system for guidance of percutaneous procedures with 4D ultrasound. A pair of uncalibrated, light-weight USB cameras are used as inputs. A database is prepared offline, using both the needle line estimated from camera-captured images and the true needle line recorded from an independent tracking device. A nonparametric learning algorithm determines the best fit model from the database. This model can then be used in real-time to estimate the true position of the needle with inputs from only the camera images. Simulation results confirm the feasibility of the method and show how a small, accurately made database can provide satisfactory results. In a series of tests with cameras, we achieved an average error of 2.4mm in position and 2.61° in orientation. The system is also extended to real ultrasound imaging, as the two miniature cameras capture images of the needle in air and the ultrasound system captures a volume as the needle moves through the workspace. A new database is created with the estimated 3D position of the needle from the ultrasound volume and the 2D position and orientation of the needle calculated from the camera images. This study achieved an average error of 0.94 mm in position and 3.93° in orientation.

Yersinia ruckeri is the bacterial agent causing enteric redmouth disease (ERM) in rainbow trout leading to economic losses in intensive aquaculture. There are two main serovars and several minor groups based on O-antigens. The first goal of this thesis was to examine the difference between serotypes of Y. ruckeri in the course of infection in fish by applying in vivo bioluminescent imaging in rainbow trout (Oncorhynchus mykiss) and coho salmon (Oncorhynchus kisutch). In infection trials, the bioluminescent strains were infective, but the bioluminescent signal was not detected in fish infected with bacterial loads of 107 colony forming units per gram of kidney tissue. Skin and scales and the kidney blocked the luminescent signal emitted from the bacteria. The second goal of this thesis was to identify genetic markers which correlate with traditional O-antigen serotyping reactions. Using the sequences of genes which are part of the lipopolysaccharide biosynthetic pathway, oligonucleotide primers were designed to be complimentary to a fragment of wzx, the O-antigen flippase, and to wzy, the O-antigen polymerase, of serotype 1 Y. ruckeri strain RS 11. When these primers were used in polymerase chain reaction, an 1183 bp fragment of wzx and a 755 bp fragment of wzy were seen with DNA from 8 serovar 1 and 9 serovar 1a strains and not from other serovars identified by rabbit anti-sera agglutination. Southern blotting suggested there was little homology between serovar 1 wzx and wzy, and the same genes of the remaining serovars if present.

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