Split and Shift Methodology: Overcoming Hardware Limitations on Cellular Processor Arrays for Image Processing

Na era multimedia, o procesado de imaxe converteuse nun elemento de singular importancia nos dispositivos electrónicos. Dende as comunicacións (p.e. telemedicina), a seguranza (p.e. recoñecemento retiniano) ou control de calidade e de procesos industriais (p.e. orientación de brazos articulados, detección de defectos do produto), pasando pola investigación (p.e. seguimento de partículas elementais) e diagnose médica (p.e. detección de células estrañas, identificaciónn de veas retinianas), hai un sinfín de aplicacións onde o tratamento e interpretación automáticas de imaxe e fundamental. O obxectivo último será o deseño de sistemas de visión con capacidade de decisión. As tendencias actuais requiren, ademais, a combinación destas capacidades en dispositivos pequenos e portátiles con resposta en tempo real. Isto propón novos desafíos tanto no deseño hardware como software para o procesado de imaxe, buscando novas estruturas ou arquitecturas coa menor area e consumo de enerxía posibles sen comprometer a funcionalidade e o rendemento

Performance analysis of massively parallel embedded hardware architectures for retinal image processing

This paper examines the implementation of a retinal vessel tree extraction technique on different hardware platforms and architectures. Retinal vessel tree extraction is a representative application of those found in the domain of medical image processing. The low signal-to-noise ratio of the images leads to a large amount of low-level tasks in order to meet the accuracy requirements. In some applications, this might compromise computing speed. This paper is focused on the assessment of the performance of a retinal vessel tree extraction method on different hardware platforms. In particular, the retinal vessel tree extraction method is mapped onto a massively parallel SIMD (MP-SIMD) chip, a massively parallel processor array (MPPA) and onto an field-programmable gate arrays (FPGA)This work is funded by Xunta de Galicia under the projects 10PXIB206168PR and 10PXIB206037PR and the program Maria BarbeitoS

Multi texture analysis of colorectal cancer continuum using multispectral imagery

Purpose This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. Materials and Methods In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models. Results Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%. Conclusions These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images

Dynamically reconfigurable architecture for embedded computer vision systems

The objective of this research work is to design, develop and implement a new architecture which integrates on the same chip all the processing levels of a complete Computer Vision system, so that the execution is efficient without compromising the power consumption while keeping a reduced cost. For this purpose, an analysis and classification of different mathematical operations and algorithms commonly used in Computer Vision are carried out, as well as a in-depth review of the image processing capabilities of current-generation hardware devices. This permits to determine the requirements and the key aspects for an efficient architecture. A representative set of algorithms is employed as benchmark to evaluate the proposed architecture, which is implemented on an FPGA-based system-on-chip. Finally, the prototype is compared to other related approaches in order to determine its advantages and weaknesses

Observing and Improving the Reliability of Internet Last-mile Links

People rely on having persistent Internet connectivity from their homes and mobile devices. However, unlike links in the core of the Internet, the links that connect people's homes and mobile devices, known as "last-mile" links, are not redundant. As a result, the reliability of any given link is of paramount concern: when last-mile links fail, people can be completely disconnected from the Internet. In addition to lacking redundancy, Internet last-mile links are vulnerable to failure. Such links can fail because the cables and equipment that make up last-mile links are exposed to the elements; for example, weather can cause tree limbs to fall on overhead cables, and flooding can destroy underground equipment. They can also fail, eventually, because cellular last-mile links can drain a smartphone's battery if an application tries to communicate when signal strength is weak. In this dissertation, I defend the following thesis: By building on existing infrastructure, it is possible to (1) observe the reliability of Internet last-mile links across different weather conditions and link types; (2) improve the energy efficiency of cellular Internet last-mile links; and (3) provide an incrementally deployable, energy-efficient Internet last-mile downlink that is highly resilient to weather-related failures. I defend this thesis by designing, implementing, and evaluating systems

Investigation of Endogenous In-Vivo Sodium Concentration in Human Prostate Cancer Measured With 23Na Magnetic Resonance Imaging

Prostate cancer (PCa) is the most common malignancy in men. Aggressive prostate tumours must be identified, differentiated from indolent tumours, and treated to ensure survival of the patient. Currently, clinicians use a combination of multi-parametric magnetic resonance imaging (mpMRI) contrasts to improve PCa detection. While these techniques provide very good spatial resolution, the specificity is often insufficient to unequivocally identify malignant lesions. Utilizing specialized MRI hardware developed for sensitive in-vivo detection of sodium, this work has investigated differences in sodium concentration between healthy and malignant prostate tissue. Patients with biopsy-proven PCa underwent conventional mpMRI and sodium MRI followed by radical prostatectomy. Subsequent whole-mount histopathology of the excised prostate was then contoured according to Gleason Grade, a radiological assessment of tumour stage and aggressiveness for PCa. Tissue sodium concentration (TSC) measured by sodium MRI was successfully co-registered with standard image contrasts from multi-parametric MRI and also with pathologist confirmed histopathology as the gold standard. This proposed method provides quantitative, in-vivo sodium information from cancerous human prostates. The results of this study establish the relationship between TSC and malignant PCa, which could prove useful in initial characterization of the disease and for active surveillance of indolent lesions