AER Building Blocks for Multi-Layer Multi-Chip Neuromorphic Vision Systems

A 5-layer neuromorphic vision processor whose components communicate spike events asychronously using the address-eventrepresentation (AER) is demonstrated. The system includes a retina chip, two convolution chips, a 2D winner-take-all chip, a delay line chip, a learning classifier chip, and a set of PCBs for computer interfacing and address space remappings. The components use a mixture of analog and digital computation and will learn to classify trajectories of a moving object. A complete experimental setup and measurements results are shown.Unión Europea IST-2001-34124 (CAVIAR)Ministerio de Ciencia y Tecnología TIC-2003-08164-C0

Automated detection of microaneurysms by using region growing and fuzzy artmap neural network

Objective: To assess whether the methodological changes of this new algorithm improves the results of a previously presented strategy. Methods: We enhance the image and filter out the green channel of the digital color retinog- raphy. Multitolerance thresholding was applied to obtain candidate points and make a seed growing region by varying intensities. We took 15 characteristics from each region to train a fuzzy Artmap neural network using 42 retinal photographs. This network was then applied in the study of 11 good quality retinal photographs included in the diabetic retinopathy early detection screening program, with initial stages of retinopathy, obtained with the Topcon NW200 non-mydriatic retinal camera. Results: Two experienced ophthalmologists detected 52 microaneurysms in 11 images. The algorithm detected 39 microaneurysms and 3752 more regions, confirming 38 microa- neurysm and 135 false positives. The sensitivity is improved compared to the previous algorithm, from 60.53% to 73.08%. False positives have dropped from 41.8 to 12.27 per image. Conclusions: The new algorithm is better than the previous one, but there is still room for improvement, especially in the initial determination of seed

Detección Automática de Microaneurismas en Retinografías para Diagnóstico Precoz de Retinopatía Diabética

En este trabajo presentamos un prototipo de herramienta de detección automática de microaneurismas (MA) en retinografías en color. Este algoritmo evoluciona a partir de trabajos anteriores como la detección de microcalcificaciones en mamografías [1] o la detección de MA en angiografías fluoresceínicas (AF) [2][3]. El método para la detección automática de MA se divide en cinco partes: preprocesado de la retinografía, algoritmo de detección basado en la umbralización del error de predicción lineal en 2D, crecimiento de regiones, selección de características, y clasificación de los candidatos mediante una red neuronal del tipo Fuzzy ARTMAP. En total disponemos de 30 imágenes con 421 MA diagnosticados, de los cuales 101 se han utilizado para la clasificación. El algoritmo detecta correctamente 78 MA, presentando una sensibilidad del 77.23% y una media de 19.25 falsos positivos por imagen.Ministerio de Sanidad PI07/90379Ministerio de Sanidad PI07/9037

Segmentación del disco óptico mediante level-sets con información de color

La segmentación del Disco Óptico (DO) es un paso esencial para la extracción automática de estructuras anatómicas y lesiones retinianas. La mayoría de los algoritmos de segmentación de la literatura procesan exclusivamente un solo plano de la retinografía, descartando la información de color. En este artículo se presenta un nuevo algoritmo de segmentación del DO. En primer lugar se realiza un preprocesamiento para eliminar los vasos sanguíneos. A continuación se aplica un algoritmo de level-sets basado en bordes. La mayor contribución del artículo es la utilización de la información de color para el proceso de segmentación. Se calculan gradientes vectoriales en el espacio de color L*a*b* que son utilizados por el algoritmo de level-sets. En lugar de utilizar la norma Euclídea, se aplica la fórmula de diferencia de color CIE94 en los gradientes vectoriales. Se ha probado con 22 retinografías donde los médicos han detectado manualmente los bordes del DO. El algoritmo ha detectado automáticamente el DO en todos los casos, con un 92.35% de intersección entre el área marcada por los expertos y la detectada. La Distancia Media al Punto más Cercano está por debajo de 5 píxeles en el 100% de las imágenes.Ministerio de Ciencia e Innovación TEC 2010-21619-C04-0

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Nuevo Algoritmo para el Cálculo de la Relación Disco ÓpticoExcavación Basado en Distancias de Color

En este trabajo se presenta una nueva herramienta automática de diagnóstico asistido por computador (CAD) para programas de rastreo masivo de glaucoma mediante el cálculo de la relación de aspecto entre la excavación de la cabeza del nervio óptico y el disco óptico (Cup to Disk Ratio, CDR). El algoritmo combina métodos morfológicos, basados en intensidad y multitolerancia, junto a las técnicas de contornos activos y clustering o agrupación K-means adaptada a la percepción humana al trabajar sobre el espacio de color CIE L* a * b * haciendo uso de la distancia de color avanzada CIE94. Los resultados se han comparado con la segmentación manual a cargo de especialistas, demostrando la bondad del método. A su vez, se ha comprobado la mejora que supone la adaptación del algoritmo a la percepción humana comparando los resultados obtenidos con los que se alcanzarían con la distancia de color Euclídea

Detección automatizada de microaneurismas mediante crecimiento de regiones y red neuronal Fuzzy Artmap

Objetivo: Comprobar si las modificaciones metodológicas de este nuevo algoritmo mejoran el resultado de otra estrategia presentada anteriormente. Métodos: Se realza y filtra la imagen negada del canal verde de la retinografía digital en color. Se aplica una umbralización multitolerancia para obtener puntos candidatos y en cada semilla se realiza un crecimiento de regiones por variación de intensidades. Se toman 15 características de cada región y entrenamos una red neuronal Fuzzy Artmap con 42 retinografías. Se aplica la red en el estudio de 11 retinografías del programa de detección precoz de retinopatía diabética, de buena calidad, con lesiones iniciales, obtenidas con el retinógrafo no midriático Topcon NW200. Resultados: Dos oftalmólogos experimentados detectan 52 microaneurismas en las 11 imágenes. El algoritmo detecta 39 microaneurismas y 3.752 regiones más, confirmando 38 microaneurismas y 135 falsos positivos. La sensibilidad ha mejorado respecto al algoritmo anterior del 60,53 al 73,08%. Los falsos positivos has disminuido de 41,8 por imagen a 12,27. Conclusiones: El nuevo algoritmo presenta indudables mejoras respecto al anterior, pero aún se puede perfeccionar, sobre todo en la determinación inicial de semillas.Objective To assess whether the methodological changes of this new algorithm improves the results of a previously presented strategy. Methods We enhance the image and filter out the green channel of the digital color retinography. Multitolerance thresholding was applied to obtain candidate points and make a seed growing region by varying intensities. We took 15 characteristics from each region to train a Fuzzy Artmap neural network using 42 retinal photographs. This network was then applied in the study of 11 good quality retinal photographs included in the diabetic retinopathy early detection screening program, with initial stages of retinopathy, obtained with the Topcon NW200 non-mydriatic retinal camera. Results Two experienced ophthalmologists detected 52 microaneurysms in 11 images. The algorithm detected 39 microaneurysms and 3,752 more regions, confirming 38 microaneurysm and 135 false positives. The sensitivity is improved compared to the previous algorithm, from 60.53 to 73.08%. False positives have dropped from 41.8 to 12.27 per image. Conclusions The new algorithm is better than the previous one, but there is still room for improvement, especially in the initial determination of seeds

Robust Automated Tumour Segmentation on Histological and Immunohistochemical Tissue Images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification

Bright Field Microscopy as an Alternative to Whole Cell Fluorescence in Automated Analysis of Macrophage Images

Fluorescence microscopy is the standard tool for detection and analysis of cellular phenomena. This technique, however, has a number of drawbacks such as the limited number of available fluorescent channels in microscopes, overlapping excitation and emission spectra of the stains, and phototoxicity.We here present and validate a method to automatically detect cell population outlines directly from bright field images. By imaging samples with several focus levels forming a bright field -stack, and by measuring the intensity variations of this stack over the -dimension, we construct a new two dimensional projection image of increased contrast. With additional information for locations of each cell, such as stained nuclei, this bright field projection image can be used instead of whole cell fluorescence to locate borders of individual cells, separating touching cells, and enabling single cell analysis. Using the popular CellProfiler freeware cell image analysis software mainly targeted for fluorescence microscopy, we validate our method by automatically segmenting low contrast and rather complex shaped murine macrophage cells.The proposed approach frees up a fluorescence channel, which can be used for subcellular studies. It also facilitates cell shape measurement in experiments where whole cell fluorescent staining is either not available, or is dependent on a particular experimental condition. We show that whole cell area detection results using our projected bright field images match closely to the standard approach where cell areas are localized using fluorescence, and conclude that the high contrast bright field projection image can directly replace one fluorescent channel in whole cell quantification. Matlab code for calculating the projections can be downloaded from the supplementary site: http://sites.google.com/site/brightfieldorstaining

A Hierarchical Probabilistic Model for Rapid Object Categorization in Natural Scenes

Humans can categorize objects in complex natural scenes within 100–150 ms. This amazing ability of rapid categorization has motivated many computational models. Most of these models require extensive training to obtain a decision boundary in a very high dimensional (e.g., ∼6,000 in a leading model) feature space and often categorize objects in natural scenes by categorizing the context that co-occurs with objects when objects do not occupy large portions of the scenes. It is thus unclear how humans achieve rapid scene categorization