Biomedical Image Processing / Medical Image Processing
Parisa Gifani; Hamid Behnam; Zahra Alizadeh Sani
Volume 4, Issue 2 , June 2010, , Pages 149-160
Abstract
Dimensionality reduction is an important task in machine learning, to simplify data mining, image processing, classification and visualization of high-dimensional data by mitigating undesired properties of high-dimensional spaces. Manifold learning is a relatively new approach to nonlinear dimensionality ...
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Dimensionality reduction is an important task in machine learning, to simplify data mining, image processing, classification and visualization of high-dimensional data by mitigating undesired properties of high-dimensional spaces. Manifold learning is a relatively new approach to nonlinear dimensionality reduction. Algorithms for manifold learning are based on the intuition that the dimensionality of many data sets may be artificially high and each data point can be described as a function of only a few underlying parameters. Using this tool, intrinsic parameters of the system database, which are main distinction factors of data sets, are recognized and all of them lie on a manifold that shows the real relationship of parameters. One of the successful applications of these methods is in image analysis field. By this approach, each image is a data in high dimensional space that the pixels are its dimensions. Because echocardiography images obtained from a patient are different in quantitative parameters such as heartbeat periodic motion and noise, image sets are reduced to two-dimensional space by a proper manifold learning. In this article, after mapping echocardiography images in two-dimensional space, by using LLE and Isomap algorithms, similar images placed side by side and the relationships between the images according to the cyclic property of heartbeat became evident. The Results showed the weakness of Isomap algorithm and power of LLE algorithm in preserving the relation between consecutive frames. De-noising is an important application which extracted from this research.
Biomedical Image Processing / Medical Image Processing
Mehdi Marsousi; Javad Alirezaie; Armen Kocharian
Volume 2, Issue 3 , June 2008, , Pages 203-214
Abstract
In this paper, a new method for boundary detection of left ventricle in echocardiography images is proposed. We have modified B-Spline Snake algorithm to achieve much faster convergence and more reliability toward noises in echocardiography images. A novel approach for inserting new node points during ...
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In this paper, a new method for boundary detection of left ventricle in echocardiography images is proposed. We have modified B-Spline Snake algorithm to achieve much faster convergence and more reliability toward noises in echocardiography images. A novel approach for inserting new node points during iterations is applied to maintain a maximum distance between two adjacent nodes. This strategy is applied in order to simultaneously increase the smoothness of the contour and optimize the computational time. A multi-resolution strategy is also adapted to provide further robustness toward noises in the images. In addition, morphological operators are utilized to specify the initial contour automatically within the left ventricle chamber in echocardiography images. The parameters of node points are determined during each transition from coarser to finer resolution according to the average intensity of the sample points on the contour near each node point. The volumes of left ventricle in the end of both systolic and diastolic frames are calculated using modified Simpson method. The ejection fraction ratio is also calculated; this is frequently used by specialist before each surgery. Moreover, a method is introduced to draw the 3D model of left ventricle with the aid of basis function of B-Spline. The proposed method is assessed by comparison between the obtained results and clinical observations by expert radiologists and demonstrates a high accuracy.