Diego Cabello

Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system

A preliminary study to approach the problem of reliably detecting life threatening ventricular arrhythmias in real time is described. An algorithm (DIAGNOSIS) has been developed in order to classify ECG signal records on the basis of the computation of four simple parameters calculated from a representation in the frequency domain. This algorithm uses a set of rules constituting an operative classification scheme based on the comparison of the parameters with a set of pre-established thresholds. This allows us to differentiate four general categories: ventricular fibrillation-flutter, ventricular rhythms, imitative artefacts and predominant sinus rhythm.

A snake for CT image segmentation integrating region and edge information

Abstract The 3D representation and solid modeling of knee bone structures taken from computed tomography (CT) scans are necessary processes in many medical applications. The construction of the 3D model is generally carried out by stacking the contours obtained from a 2D segmentation of each CT slice, so the quality of the 3D model strongly depends on the precision of this segmentation process. In this work we present a deformable contour method for the problem of automatically delineating the external bone (tibia and fibula) contours from a set of CT scan images. We have introduced a new region potential term and an edge focusing strategy that diminish the problems that the classical snake method presents when it is applied to the segmentation of CT images. We introduce knowledge about the location of the object of interest and knowledge about the behavior of edges in scale space, in order to enhance edge information. We also introduce a region information aimed at complementing edge information. The novelty in that is that the new region potential does not rely on prior knowledge about image statistics; the desired features are derived from the segmentation in the previous slice of the 3D sequence. Finally, we show examples of 3D reconstruction demonstrating the validity of our model. The performance of our method was visually and quantitatively validated by experts.

Computer‐aided diagnoses: Automatic detection of lung nodules

This work describes a computational scheme for automatic detection of suspected lung nodules in a chest radiograph. A knowledge-based system extracts the lung masks over which we will apply the nodule detection process. First we obtain the normalized cross-correlation image. Next we detect suspicious regions by assuming a threshold. We examine the suspicious regions using a variable threshold which results in the growth of the suspicious areas and an increase in false positives. We reduce the large number of false positives by applying the facet model to the suspicious regions of the image. An algorithmic classification process gives a confidence factor that a suspicious region is a nodule. Five chest images containing 30 known nodules were used as a training set. We evaluated the system by analyzing 30 chest images with 40 confirmed nodules of varying contrast and size located in various parts of the lungs. The system detected 100% of the nodules with a mean of six false positives per image. The accuracy and specificity were 96%.

Cellular neural networks and active contours: a tool for image segmentation

Abstract In this paper Cellular Neural Networks (CNN) are applied to image segmentation based on active contour techniques. The approach is based on deformable contours which evolve pixel by pixel from their initial shapes and locations until delimiting the objects of interest. The contour shift is guided by external information from the image under consideration which attracts them towards the target characteristics (intensity extremes, edges, etc.) and by internal forces which try to maintain the smoothness of the contour curve. This CNN-based proposal combines the characteristics from implicit and parametric models. As a consequence a high flexibility and control for the evolution dynamics of the snakes are provided, allowing the solution of complex tasks as is the case of the topologic transformations. In addition the proposal is suitable for its implementation as an integrated circuit allowing to take advantages of the massively parallel processing in CNN to reduce processing time.

Discrete-time CNN for image segmentation by active contours

In this work we present a new image segmentation strategy which operates by means of active contours implemented on a multilayer cellular neural network. The approach consists of an expanding and thinning process, guided by external information from a contour which evolves until it reaches the final desired position in the image processed.

A snake for model-based segmentation of biomedical images

Abstract In this work we present a snake based approach for the segmentation of images of computerized tomography (CT) scans. We introduce a new term for the internal energy and another one for external energy which solve common problems associated with classical snakes in this type of images. A simplified minimizing method is also presented.

Fuzzy K-nearest neighbor classifiers for ventricular arrhythmia detection

We report a study of the efficiency of 4 classifiers (the K-nearest-neighbor and single-nearest-prototype algorithms, each as parametrized by both Fuzzy C-Means and Fuzzy Covariance clustering) in the detection of ventricular arrhythmias in ECG traces characterized by 4 features derived from 7 spectral parameters. Principal components analysis was used in conjunction with a cardiologists deterministic classification of 90 ECG traces to fix the number of trace classes to 5 (ventricular fibrillation/flutter, sinus rhythm, ventricular rhythms with aberrant complexes and 2 classes of artefact). Forty of the 90 traces were then defined as a test set; 5 different learning sets (numbering 25, 30, 35, 40 and 45 traces) were randomly selected from the remaining 50 traces; each learning set was used to parametrize both the classification algorithms using both fuzzy clustering algorithms and the parametrized classification algorithms were then applied to the test set. Optimal K for K-nearest-neighbor algorithms and optimal cluster volumes for Fuzzy Covariance algorithms were sought by trial and error to minimize classification differences with respect to the cardiologists classification. Fuzzy Covariance clustering afforded significantly better perception of cluster structure than the Fuzzy C-Means algorithm, and the classifiers performed correspondingly with an overall empirical error ratio of just 0.10 for the K-nearest-neighbor algorithm parametrized by Fuzzy Covariance.

CMOS-3D Smart Imager Architectures for Feature Detection

This paper reports a multi-layered smart image sensor architecture for feature extraction based on detection of interest points. The architecture is conceived for 3-D integrated circuit technologies consisting of two layers (tiers) plus memory. The top tier includes sensing and processing circuitry aimed to perform Gaussian filtering and generate Gaussian pyramids in fully concurrent way. The circuitry in this tier operates in mixed-signal domain. It embeds in-pixel correlated double sampling, a switched-capacitor network for Gaussian pyramid generation, analog memories and a comparator for in-pixel analog-to-digital conversion. This tier can be further split into two for improved resolution; one containing the sensors and another containing a capacitor per sensor plus the mixed-signal processing circuitry. Regarding the bottom tier, it embeds digital circuitry entitled for the calculation of Harris, Hessian, and difference-of-Gaussian detectors. The overall system can hence be configured by the user to detect interest points by using the algorithm out of these three better suited to practical applications. The paper describes the different kind of algorithms featured and the circuitry employed at top and bottom tiers. The Gaussian pyramid is implemented with a switched-capacitor network in less than 50 μs, outperforming more conventional solutions.

Design of the processing core of a mixed-signal CMOS DTCNN chip for pixel-level snakes

This paper introduces the processing core of a full-custom mixed-signal CMOS chip intended for an active-contour-based technique, the so-called pixel-level snakes (PLS). Among the different parameters to optimize on the top-down design flow our methodology is focused on area. This approach results in a single-instruction-multiple-data chip implemented by a discrete-time cellular neural network with a correspondence between pixel and processing element. This is the first prototype for PLS; an integrated circuit with a 9/spl times/9 resolution manufactured in a 0.25 -/spl mu/m CMOS STMicroelectronics technology process. Awaiting for experimental results, HSPICE simulations prove the validity of the approach introduced here.

Image segmentation based on active contours using discrete time cellular neural networks

We present a new proposal for image segmentation using deformable models, as an application of discrete-time cellular neural networks (DTCNN). This approach is based on active contours (also called snakes) which evolve until reaching a final desired location. The contours are guided by both external information from the image under consideration which attracts them towards salient characteristics of the scene, and internal energy from the contour image which tries to maintain the smoothness in the curve shape. The massively parallel processing in DTCNN and the use of local information permit a VLSI implementation, suitable for real time applications.