Segmentation of bone structures in 3D CT images based on continuous max- ow optimization

In this paper an algorithm to carry out the automatic segmentation of bone structures in 3D CT images has been implemented. Automatic segmentation of bone structures is of special interest for radiologists and surgeons to analyze bone diseases or to plan some surgical interventions. This task is very complicated as bones usually present intensities overlapping with those of surrounding tissues. This overlapping is mainly due to the composition of bones and to the presence of some diseases such as Osteoarthritis, Osteoporosis, etc. Moreover, segmentation of bone structures is a very time-consuming task due to the 3D essence of the bones. Usually, this segmentation is implemented manually or with algorithms using simple techniques such as thresholding and thus providing bad results. In this paper gray information and 3D statistical information have been combined to be used as input to a continuous max- ow algorithm. Twenty CT images have been tested and di erent coe cients have been computed to assess the performance of our implementation. Dice and Sensitivity values above 0.91 and 0.97 respectively were obtained. A comparison with Level Sets and thresholding techniques has been carried out and our results outperformed them in terms of accuracy.Ministerio de ciencia e innovación TEC2010-21619-C04-02Junta de Andalucía P11-TIC-772

Sistema de reconocimiento de caracteres de alta velocidad basado en eventos

Spike-based processing technology is capable of very high speed throughput, as it does not rely on sensing and processing sequences of frames. Besides, it allows building complex and hierarchically structured cortical-like layers for sophisticated processing. In this paper we summarize the fundamental properties of this sensing and processing technology applied to artificial vision systems and the AER (Address Event Representation) protocol used in hardware spiking systems. Finally a four-layer system is described for character recognition. The system is slightly based on the Fukushima´s Neocognitron. Realistic simulations using figures of already existing AER devices are provided, which show recognition delays under 10μs.Ministerio de Ciencia e Innovación (VULCANO) TEC2009-10639-C04-0

Red neuronal convolucional rápida sin fotogramas para reconocimientos de dígitos

Comunicación presentada al "XXVI Simposio de la URSI" celebrado en Leganés (España) del 7 al 9 de Septiembre del 2011.In this paper a bio-inspired six-layer convolutional network (ConvNet) non-frame based for digit recognition is shown. The system has been trained with the backpropagation algorithm using 32x32 images from the MNIST database. The system can be implemented with already physically available spike-based electronic devices. 10000 images have been coded into events separated 50ns to test the non-frame based ConvNet system. The simulation results have been obtained using actual performance figures for existing AER (Address Event Representation) hardware components. We provide simulation results of the system showing recognition delays of a few microseconds from stimulus onset with a recognition rate of 93%. The complete system consists of 30 convolution modules.Ministerio de Ciencia e Innovación (VULCANO) TEC2009-10639-C04-01Andalucía (Brain System) P06-TIC-0141

Simulador de sistemas AER basados en eventos

XXIII Simposium Nacional de la Unión Científica Internacional de Radio (URSI 2008). Madrid, 22-24 Septiembre 2008.Address-Event-Representation (AER) is a communications protocol for transferring (visual) information between chips, originally developed for bio-inspired vision and audition systems. Such systems may consist of a complicated multi-layer hierarchical structure with many chips that transmit events among them in real time, while performing some complex processing (for example, convolutions, competitions, etc). This sensing and processing technology is capable of very high speed throughput, because it does not rely on sensing and processing sequences of frames, and because it allows for complex hierarchically structured cortical-like layers for sophisticated processing. In this paper we present an effective tool that simulates the behaviour of such kind of structures. AER stream sources are fed to the software simulation tool and AER streams at all nodes of the network are computed. The tool has been developed in MATLAB and is event driven. It has been conceived as an open tool, so that any user can add extra functional blocks easily, or provide more elaborate or more simplified descriptions of already available blocks.Ministerio de Ciencia y Tecnología 2006-11730-C03-01 (Samanta2)Unión europea EU IST-2001-34124 (Caviar)Junta de Andalucía P06-TIC-0141

Spike-Based Convolutional Network for real-time processing

In this paper we propose the first bio-inspired sixlayer convolutional network (ConvNet) non-frame based that can be implemented with already physically available spikebased electronic devices. The system was designed to recognize people in three different positions: standing, lying or up-sidedown. The inputs were spikes obtained with a motion retina chip. We provide simulation results showing recognition delays of 16 milliseconds from stimulus onset (time-to-first spike) with a recognition rate of 94%. The weight sharing property in ConvNets and the use of AER protocol allow a great reduction in the number of both trainable parameters and connections (only 748 trainable parameters and 123 connections in our AER system (out of 506998 connections that would be required in a frame-based implementation).Ministerio de Educación y Ciencia TEC2006-11730-C03-01Junta de Andalucía P06-TIC-0141

Color-texture image segmentation based on multistep region growing

A new method for color image segmentation is proposed. It is based on a novel region-growing technique with a growth tolerance parameter that changes with step size, which depends on the variance of the actual grown region. Contrast is introduced to determine which value of the tolerance parameter is taken, choosing the one that provides the region with the highest contrast in relation to the background. Color and texture information are extracted from the image by means of a novel idea: the construction of a color distance image and a texture energy image. The color distance image is formed by calculating CIEDE2000 distance in the L*a*b* color space. The texture energy image is extracted from some statistical moments. Then, a novel texture-controlled multistep region-growing process is performed for the segmentation. One advantage of the method is that it is not designed to work with a particular kind of images. This method is tested on 80 natural color images of the Corel photo stock collection with excellent results. Numerical evidence of the quality of these results is provided by comparing them with the manual segmentation of five experts and with another color and texture segmentation algorith

Maximal Contrast Adaptive Region Growing for CT Airway Tree Segmentation

In this paper we propose a fully self-assessed adaptive region growing airway segmentation algorithm. We rely on a standardized and self-assessed region-based approach to deal with varying imaging conditions. Initialization of the algorithm requires prior knowledge of trachea location. This can be provided either by manual seeding or by automatic trachea detection in upper airway tree image slices. The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing region. Extensive validation is provided for a set of 20 chest CT scans. Our method exhibits very low leakage into the lung parenchyma, so even though the smaller airways are not obtained from the region growing, our fully automatic technique can provide robust and accurate initialization for other method

Colorimetric calibration of images captured under unknown illuminants

In this paper the problem of acquiring colorimetrically-calibrated images under multiple uncontrolled illuminants is studied. One of the main applications is diagnosis of different injuries by skin colour analysis, these images would be captured in hospitals where lighting conditions are uncontrolled. To gain some control over illumination, a xenon flash has been used in an attempt to dominate the ambient illumination. A Macbeth ColorChecker DC has been required as a test target to make measurements of observed colour using a digital camera under various illumination conditions. A colorimetric calibration algorithm that allows to convert RGB values under unknown illuminant to RGB values under D50 illuminant is also presented. The use of this algorithm avoids pixel values dependence on lighting conditions

Perceptual color clustering for color image segmentation based on CIEDE2000 color distance

In this paper, a novel technique for color clustering with application to color image segmentation is presented. Clustering is performed by applying the k-means algorithm in the L*a*b* color space. Nevertheless, Euclidean distance is not the metric chosen to measure distances, but CIEDE2000 color difference formula is applied instead. K-means algorithm performs iteratively the two following steps: assigning each pixel to the nearest centroid and updating the centroids so that the empirical quantization error is minimized. In this approach, in the first step, pixels are assigned to the nearest centroid according to the CIEDE2000 color distance. The minimization of the empirical quantization error when using CIEDE2000 involves finding an absolute minimum in a non-linear equation and, therefore, an analytical solution cannot be obtained. As a consequence, a heuristic method to update the centroids is proposed. The proposed algorithm has been compared with the traditional k-means clustering algorithm in the L*a*b* color space with the Euclidean distance. The Borsotti parameter was computed for 28 color images. The new version proposed outperformed the traditional one in all cases

Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks?

This article belongs to the Special Issue "Image Processing and Analysis for Preclinical and Clinical Applications"Basal Cell Carcinoma (BCC) is the most frequent skin cancer and its increasing incidence is producing a high overload in dermatology services. In this sense, it is convenient to aid physicians in detecting it soon. Thus, in this paper, we propose a tool for the detection of BCC to provide a prioritization in the teledermatology consultation. Firstly, we analyze if a previous segmentation of the lesion improves the ulterior classification of the lesion. Secondly, we analyze three deep neural networks and ensemble architectures to distinguish between BCC and nevus, and BCC and other skin lesions. The best segmentation results are obtained with a SegNet deep neural network. A 98% accuracy for distinguishing BCC from nevus and a 95% accuracy classifying BCC vs. all lesions have been obtained. The proposed algorithm outperforms the winner of the challenge ISIC 2019 in almost all the metrics. Finally, we can conclude that when deep neural networks are used to classify, a previous segmentation of the lesion does not improve the classification results. Likewise, the ensemble of different neural network configurations improves the classification performance compared with individual neural network classifiers. Regarding the segmentation step, supervised deep learning-based methods outperform unsupervised onesMinisterio de Economía y Competitividad DPI2016-81103-RFEDER-US, Junta de Andalucía US-1381640Fondo Social Europeo Iniciativa de Empleo Juvenil EJ3-83-