Biomedical subjects

D Cabello

Publications and source records attributed to D Cabello.

8 recordsLinked to original sources

PubMed2003

Discriminant snakes for 3D reconstruction of anatomical organs.

In this work a new statistic deformable model for 3D segmentation of anatomical organs in medical images is proposed. A statistic discriminant snake performs a supervised learning of the object boundary in an image slice to segment the next slice of the image sequence. Each part of the object boundary is projected in a feature space generated by a bank of Gaussian filters. Then, clusters corresponding to different boundary pieces are constructed by means of linear discriminant analysis. Finally, a parametric classifier is generated from each contour in the image slice and embodied into the snake energy-minimization process to guide the snake deformation in the next image slice. The discriminant snake selects and classifies image features by the parametric classifier and deforms to minimize the dissimilarity between the learned and found image features. The new approach is of particular interest for segmenting 3D images with anisotropic spatial resolution, and for tracking temporal image sequences. In particular, several anatomical organs from different imaging modalities are segmented and the results compared to expert tracings.

Algorithms↗

PubMed1999

Automatic segmentation of lung fields on chest radiographic images.

In this work we have implemented a system for the automatic segmentation of lung fields in chest radiographic images. The image analysis process is carried out in three levels. In the first one we perform operations on the image that are independent from domain knowledge. This knowledge is implicitly and not very elaborately used in the intermediate level and used in an explicit manner in the high level block, globally corresponding to the idea of progressive segmentation. The representation of knowledge in the high level block is in the form of production rules. The control structure is in general bottom-up but there are certain hybrid control stages, in which the control is driven by the region model (main organs) we are seeking. We have applied the global system to a set of 45 posteroanterior (PA) chest radiographs, obtaining a mean degree of overlap with contours drawn by radiologists of 87%.

Algorithms↗

PubMed1998

Computer-aided diagnosis: a neural-network-based approach to lung nodule detection.

In this work, we have developed a computer-aided diagnosis system, based on a two-level artificial neural network (ANN) architecture. This was trained, tested, and evaluated specifically on the problem of detecting lung cancer nodules found on digitized chest radiographs. The first ANN performs the detection of suspicious regions in a low-resolution image. The input to the second ANN are the curvature peaks computed for all pixels in each suspicious region. This comes from the fact that small tumors possess and identifiable signature in curvature-peak feature space, where curvature is the local curvature of the image data when viewed as a relief map. The output of this network is thresholded at a chosen level of significance to give a positive detection. Tests are performed using 60 radiographs taken from routine clinic with 90 real nodules and 288 simulated nodules. We employed free-response receiver operating characteristics method with the mean number of false positives (FP's) and the sensitivity as performance indexes to evaluate all the simulation results. The combination of the two networks provide results of 89%-96% sensitivity and 5-7 FP's/image, depending on the size of the nodules.

Diagnosis, Computer-Assisted↗

PubMed1998

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%.

Algorithms↗

PubMed1991

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 cardiologist's 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 cardiologist's 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.

Algorithms↗

PubMed1989

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.

Algorithms↗

PubMed1986

A computational frame to study social behaviour in animals.

This paper presents new methods and procedures for studying collective behaviour in rats. The animals are assumed to be indistinguishable one from another and the behaviour of the group is represented analysed and interpreted in terms of the temporal evolution of a finite state probabilistic automaton. The automaton states are defined by measures on the clustering degree considered as a social response variable. The electronic system developed to carry out the cluster analysis and the automatic control of the social behaviour in the experimental environment includes a multimicroprocessor interacting with a 'social box' in which, together with classical sensors and effectors, a phototransistor based position sensor is included. Preliminary experiments show the discriminative power of the cluster automaton concerning sexual differences and emotivity, as well as the extensive of a basic mechanism of clustering as a collective response to stress. Pharmacologically, the new experimental medium proposed in this paper may be used to detect a new range of products affecting social but not individual behaviour. Also, well-known products, which in normal doses produce no detectable modification of individual behaviour, might have detectable effects on the collective level. Be this as it may, the experimental environment described constitutes a further experimental facility for the analysis and control of animal behaviour.

Animals↗

PubMedDate not supplied

A Markov random field model for bony tissue classification.

3D biomedical images are a valuable source of information for clinical diagnosis. In areas such as bone remodeling, fracture prediction and prosthesis design, the external geometry of the bones needs to be precisely defined and injuries identified. A system that automatically interprets and presents a 3D reconstruction of the bone can be very useful, although this task cannot be carried out without specific knowledge of the domain. This knowledge may be represented by a set of constraints over properties and relationships between regions. In this work we present a Markov random field model for identification of injuries in the proximal tibia.

Bone and Bones↗