Histopathological image analysis with connections to genomics

The fields of imaging and genomics in cancer research have been mostly studied independently, but recently available datasets have made investigation into the synergy of these two fields possible. This work demonstrates the efficacy of computational histopathological image analysis to extract meaningful quantitative nuclear and cellular features from hematoxylin and eosin stained images that have meaningful connections to genomic data. Additionally, with the advent of whole slide images, significantly more data representing the variation in nuclear characteristics and tumor heterogeneity is available, which can aid in developing new analytical tools, such as the proposed convolutional neural network for nuclear segmentation, which produces state-of-the-art segmentation results on challenging cases seen in normal pathology. This robust segmentation tool is essential for capturing reliable features for computational pathology. Additionally, whole slide images capture rich spatial information about tumors, which presents a challenge, but also an opportunity for the development of new image processing tools to capture this spatial information, which could be considered for future work. Other histopathological image modalities and relevant machine learning tools are also considered for elucidating cellular processes of cancer

Evaluation of stereo matching for mobile platforms with applications for assisting the visually impaired

The rising interest in immersive entertainment and enhanced image and video content, along with the development of stereo cameras for mobile platforms, motivates the presented evaluation of stereo matching algorithms for mobile devices. This work investigates this potential for stereo matching on a mobile device for real-time applications, in terms of computation time and quality of depth inference. Several algorithms are tested on an Android tablet housing a Tegra 3 processor using images captured from the on-board consumer-grade cameras. Despite distortions incurred by the lower quality cameras and the computational constraints of a tablet, results show that a simple block matching approach can perform reasonable inference at a rate of 10 frames-per-second. Other methods are shown to be too computationally demanding for real-time applications, as even the fastest alternative local method, using adaptive support weights, requires up to 20 seconds per frame on a 320x360 image. Results also show the impact of lower quality ``real-world'' images on inference performance on algorithms. Additionally, real-time stereo matching on a mobile device is applied to a novel application of assisting the visually impaired with navigation. A system is proposed using a simple block matching algorithm that infers the depth of the scene and communicates the presence of obstacles via sound to the user. The system is housed entirely within the mobile device, overcoming a primary hindrance of many users of assistive technology. The use of a mobile device also allows for an intuitive, interactive experience for the user with the depth information directly via the touch screen of the device. This system demonstrates the added functionality of real-time depth estimation on a mobile device and the potential for aiding the visually impaired with navigation