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

Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors

Segmentation of biomedical images is essential for studying and characterizing anatomical structures, detection and evaluation of pathological tissues. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities of the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin

GPU-Accelerated Contour Extraction on Large Images Using Snakes

Active contours have been proven to be a powerful semiautomatic image segmentation approach, that seems to cope with many applications and different image modalities. However, they exhibit inherent drawbacks, including the sensibility to contour initialization due to the limited capture range of image edges and problems with concave boundary regions. The Gradient Vector Flow replaces the traditional image force and provides an enlarged capture range as well as enhanced concavity extraction capabilities, but it involves an expensive computational effort and considerably increased memory requirements at the time of computation. In this paper, we present an enhancement of the active contour model to facilitate semiautomatic contour detection in huge images. We propose a tile-based image decomposition accompanying an image force computation scheme on demand in order to minimize both computational and memory requirements. We show an efficient implementation of this approach on the basis of general purpose GPU processing providing for continuous active contour deformation without a considerable delay

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

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

A comparative study of algorithms for automatic segmentation of dermoscopic images

Melanoma is the most common as well as the most dangerous type of skin cancer. Nevertheless, it can be effectively treated if detected early. Dermoscopy is one of the major non-invasive imaging techniques for the diagnosis of skin lesions. The computer-aided diagnosis based on the processing of dermoscopic images aims to reduce the subjectivity and time-consuming analysis related to traditional diagnosis. The first step of automatic diagnosis is image segmentation. In this project, the implementation and evaluation of several methods were proposed for the automatic segmentation of lesion regions in dermoscopic images, along with the corresponding implemented phases for image preprocessing and postprocessing. The developed algorithms include methods based on different state of the art techniques. The main groups of techniques which have been selected to be studied and implemented are thresholding-based methods, region-based methods, segmentation based on deformable models, as well as a new proposed approach based on the bag-of-words model. The implemented methods incorporate modifications for a better adaptation to features associated with dermoscopic images. Each implemented method was applied to a database constituted by 724 dermoscopic images. The output of the automatic segmentation procedure for each image was compared with the corresponding manual segmentation in order to evaluate the performance. The comparison between algorithms was carried out regarding the obtained evaluation metrics. The best results were achieved by the combination of region-based segmentation based on the multi-region adaptation of the k-means algorithm and the subIngeniería de Sistemas Audiovisuale

Integrated Development and Parallelization of Automated Dicentric Chromosome Identification Software to Expedite Biodosimetry Analysis

Manual cytogenetic biodosimetry lacks the ability to handle mass casualty events. We present an automated dicentric chromosome identification (ADCI) software utilizing parallel computing technology. A parallelization strategy combining data and task parallelism, as well as optimization of I/O operations, has been designed, implemented, and incorporated in ADCI. Experiments on an eight-core desktop show that our algorithm can expedite the process of ADCI by at least four folds. Experiments on Symmetric Computing, SHARCNET, Blue Gene/Q multi-processor computers demonstrate the capability of parallelized ADCI to process thousands of samples for cytogenetic biodosimetry in a few hours. This increase in speed underscores the effectiveness of parallelization in accelerating ADCI. Our software will be an important tool to handle the magnitude of mass casualty ionizing radiation events by expediting accurate detection of dicentric chromosomes

Automatic Pixel-Parallel Extraction of the Retinal Vascular Tree Algorithm Design, On-Chip Implementation an Applications

[Resumen] La tesis doctoral propone un nuevo algoritmo para la extracción del árbol arterio-venoso en imágenes digitales de retina usando sistemas pixel paralelo que le confiere un procesamiento a alta velocidad, Inicialmente el problema de la extracción del árbol arterio venoso se estudió desde el punto de vista del procesamiento de imágenes utilizando técnicas pixel paralelo, concretamente bajo el paradigma de las Cellular Neural Networks. Este algoritmo utiliza una técnica de contornos activos, los Pixel level snakes (PLS) que permiten aprovechar las ventajas de los contornos activos, como es su capacidad de funcionamiento con contornos borrosos así como su robustez ante el ruido, y al mismo tiempo todo ello procesándose a una alta velocidad de computación. Esta técnica permite también su proyección en un dispositivo hardware específico. La primera versión del algoritmo fue diseñada basándose en el paradigma CNN. Los resultados obtenidos eran buenos bajo el punto de vista del procesado de imagen. Sin embargo, la complejidad de algunas de las operaciones propuestas en esta versión eran de una alta complejidad para ser implementados en los chips pixel paralelos actuales con capacidades SIMD (Single Instruction Multiple Data). Esta versión ha sido redefinida para ser implementada en un chip SIMD. Esta última versión ha sido analizada desde un punto de vista del ajuste de los resultados y desde el punto de vista de la velocidad de ejecución. Para el primer análisis se ha hecho uso de una base de datos pública, concretamente la DRIVE (Digital Retinal Image for Vessel Extraction). Para el análisis de los tiempos de ejecución, se implementó el algoritmo en un chip específico, el SCAMP-3 vision system. El análisis de ambos aspectos ha permitido observar, que el ajuste obtenido sobre los resultados es alto, aunque existen algoritmos con un ajuste mejor, y el tiempo de ejecución es realmente rápido y no existe ningún algoritmo en la bibliografía que mejore el tiempo obtenido con la implementación propuesta en esta tesis. Asimismo se ha realizado un estudio de la mejora que se podría obtener utilizando una técnica de solapamiento, puesto que debido a la alta resolución de las imágenes utilizadas, estas se han tenido que dividir en subventanas para su procesamiento. Este análisis ha demostrado que la mejora obtenida es mínima en comparación con el notable incremento del tiempo de ejecución, siendo descartada su utilización. Una vez demostrado el funcionamiento del algoritmo se ha procedido a su inclusión en aplicaciones prácticas que se encontraban ya funcionando utilizando algoritmos clásicos para la extracción del árbol arterio venoso. Las aplicaciones corresponden a dos ámbitos diferentes con necesidades propias, el ámbito médico y la autenticación de personas. Para la autenticación de personas se observó que el funcionamiento es igual que usando las versiones clásicas, manteniendo un 100% de efectividad en la identificación de personas. En el caso de la aplicación médica, se incluyó dentro de un sistema de estimación del índice arterio-venoso, mostrando un funcionamiento con valores similares

3D Segmentation & Measurement of Macular Holes

Macular holes are blinding conditions where a hole develops in the central part of retina, resulting in reduced central vision. The prognosis and treatment options are related to a number of variables including the macular hole size and shape. In this work we introduce a method to segment and measure macular holes in three-dimensional (3D) data. High-resolution spectral domain optical coherence tomography (SD-OCT) allows precise imaging of the macular hole geometry in three dimensions, but the measurement of these by human observers is time consuming and prone to high inter- and intra-observer variability, being characteristically measured in 2D rather than 3D. This work introduces several novel techniques to automatically retrieve accurate 3D measurements of the macular hole, including surface area, base area, base diameter, top area, top diameter, height, and minimum diameter. Specifically, it is introducing a multi-scale 3D level set segmentation approach based on a state-of-the-art level set method, and introducing novel curvature-based cutting and 3D measurement procedures. The algorithm is fully automatic, and we validate the extracted measurements both qualitatively and quantitatively, where the results show the method to be robust across a variety of scenarios. A segmentation software package is presented for targeting medical and biological applications, with a high level of visual feedback and several usability enhancements over existing packages. Specifically, it is providing a substantially faster graphics processing unit (GPU) implementation of the local Gaussian distribution fitting (LGDF) energy model, which can segment inhomogeneous objects with poorly defined boundaries as often encountered in biomedical images. It also provides interactive brushes to guide the segmentation process in a semi-automated framework. The speed of implementation allows us to visualise the active surface in real-time with a built-in ray tracer, where users may halt evolution at any timestep to correct implausible segmentation by painting new blocking regions or new seeds. Quantitative and qualitative validation is presented, demonstrating the practical efficacy of the interactive elements for a variety of real-world datasets. The size of macular holes is known to be one of the strongest predictors of surgical success both anatomically and functionally. Furthermore, it is used to guide the choice of treatment, the optimum surgical approach and to predict outcome. Our automated 3D image segmentation algorithm has extracted 3D shape-based macular hole measurements and described the dimensions and morphology. Our approach is able to robustly and accurately measure macular hole dimensions. This thesis is considered as a significant contribution for clinical applications particularly in the field of macular hole segmentation and shape analysis