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ABSTRACT: A contour-based automatic recognition system was applied to classify intraductal breast carcinoma into high nuclear grade and low nuclear grade in a digitized histologic image. The image discriminating characteristics were selected by their invariability condition to rotation and translation. They were acquired from cellular contours information. The totally interconnected multilayer perceptron network architecture was selected, and it was trained with the error back propagation algorithm. Forty cases were analyzed by the system and the diagnoses were compared with that of pathologist consensus, obtaining agreement in 97.5% (p < .00001 of cases). The system may become a very useful tool for the pathologist in the definitive classification of intraductal carcinoma.
The Breast Journal 07/1998; 4(4):238-44. · 1.64 Impact Factor
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ABSTRACT: This work presents 3D cardiac images rendering based on volumetrics of plane sections obtained from the cardiac wall segmentation. This work introduces a non-rigid model based on physics as a segmentation method. An object is constructed in the shape of a polygon of n vertices, with masses on each of them, interconnected with springs to each other. The model is deformed when exposed to a force field that moves it toward the desired contour. In order to obtain the volumetric reconstructed rendering of the ventricular cavity, the method was used on a series of apical views of the heart of a healthy person. The views belong to radial sections (equally spaced in angle) which, alter segmentation, and by means of a bilinear interpolation process, permitted the reconstruction of a 3D image of the ventricular cavity.
Computers in Cardiology, 1996; 10/1996
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ABSTRACT: The formation of the echocardiographic signal, that goes from the transducer to the 2D echo image, was simulated upon a 3D cardiac tissue image. Images of tissue are obtained by mathematical simulation, and a serie of histological slices of cardiac tissue. These slices were observed using an optical microscope at a calibration of 10 microns per pixel. Slices are corrected for translation and rotation errors in order to obtain the 3D reconstruction. Cardiac contraction dynamics are simulated with a simple three-transformation model Transducers are simulated with a cosine-moduled Gaussian three-dimensional spread function. The system outputs two signals: a radio-frecuency image and an echocardiographic image. Texture features are analyzed as a function of cardiac dynamics. Results' analysis shows the influence of the cellular and connective components of cardiac tissue in the echocardiographic response.
Computers in Cardiology, Indianapolis, U.S.A.; 09/1996
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ABSTRACT: Presents a study of intensity variations in the echocardiographic image during myocardial contraction. Both simulated and actual tissue were used to study this phenomenon. One of the approaches was to simulate echocardiographic response to cardiac tissue variations. In order to perform this task, we developed four modules which we have designated as: Tissue, Dynamics, Equipment and Texture. In the first module, we research two alternatives: mathematically simulated tissue and actual tissue obtained from histological samples of diseased hearts. The second module simulates contraction's dynamics, using a three-transformations model (displacement, rotation and contraction) on an acoustic-impedance image. The third module simulates the transducers by varying the ultrasonic beam's frequency and dispersion. The equipment outputs two signals: a radio-frequency image and an echocardiographic image. Both signals are then analyzed by the Texture module. This also includes an analysis of the mean values. The final goal of the project is to analyze tissue's characterization, in order to infer about the cardiac fiber's condition based on the space-time analysis of the two output signals.
Computers in Cardiology 1995; 10/1995
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ABSTRACT: Two tracking methods based on matching techniques were used to
detect the motion of ventricular walls in cardiac echocardiographic
images. Two types of Regions of Interest (ROI) were selected. The first
based on tracking blocks of pixels using Sum of Absolute Differences
(SAD) and the Correlation Coefficient (CORR), as matching criteria. The
second based on gray profile cross-sections along an axis, that passes
through the point of interest. In this other case, Covariance (COV) and
Correlation Coefficients (CORR) were used as the matching criteria. All
four methods were tested with echocardiographic images with different
quality levels. The interest points were chosen along the ventricle
borders and the myocardial middle zone
Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE; 12/1994
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ABSTRACT: A method was developed to detect the ventricle wall for the
parasternal short axis view in two-dimensional echocardiography. The
ventricular shape is modeled with a very simple parametric closed curve:
an ellipse defined by four parameters. A genetic algorithm (GA) guides
the elliptical shape and its position to find an optimum result. It
minimizes one border dependent function, obtained after sampling 56
points in the ellipse. These points are located at the septum region
(19) and at the ventricular posterior wall (37). The obtained solution
is considered excellent as a first approximation to the real border.
Then a second search complements the final solution. The ventricular
shape is followed along one image sequence, basing the search space on
the solutions given by the previous image. Medical knowledge about the
cardiac structure and the image acquisition process is added to evaluate
the function
Computers in Cardiology 1993, Proceedings.; 10/1993
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Engineering in Medicine and Biology Society, 1993. Proceedings of the 15th Annual International Conference of the IEEE; 02/1993
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Engineering in Medicine and Biology Society, 1993. Proceedings of the 15th Annual International Conference of the IEEE; 02/1993
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ABSTRACT: A tool for image texture analysis (TITA) has been developed for
application to two-dimensional echocardiographic images. Through a
user-friendly environment, spatio-temporal changes in texture can be
analyzed to characterize the tissue or to provide segmentation and
border detection. As an example of the application of TITA, the
implementation of an inverse difference moment based algorithm for the
extraction of endocardial boundaries from echocardiographic images is
presented. The proposed method has been applied to apical four chamber
and paraesternal short axis views, showing excellent results even in
difficult images. It is possible to conclude that the difference image
of echocardiographic frames contains sufficient information for accurate
detection of borders and regions
Computers in Cardiology 1992. Proceedings.; 11/1992
