A method that uses the fuzzy ISODATA clustering algorithm and Fourier analysis is proposed for automated detection of heart left ventricle contours. This operation is used for quantitative analysis of cardiac function. The computation begins by finding the phase image. The fuzzy ISODATA algorithm is first applied to this image to generate a number of clusters that correspond to the organ substructures (ventricles, atria). Second, the ventricles cluster is isolated and the intensities of its points are replaced by the corresponding ones from the original (end diastolic) frame. Finally, a reduced image representing the ventricular region is obtained and an additional clustering is performed to find the left ventricular boundary automatically. This algorithm is tested by application of 105 sets of 16 images each. These results are compared with the measurements obtained with two semi-automatic methods used, respectively, on the Philips and the Sopha Medical gamma cameras.
An approach to automated outlining the left ventricular contour and its bounded area in gated isotopic ventriculography is proposed. Its purpose is to determine the ejection fraction (EF), an important parameter for measuring cardiac function. The method uses a modified version of the fuzzy C-means (MFCM) algorithm and a labeling technique. The MFCM algorithm is applied to the end diastolic (ED) frame and then the (FCM) is applied to the remaining images in a "box" of interest. The MFCM generates a number of fuzzy clusters. Each cluster is a substructure of the heart (left ventricle,...). A cluster validity index to estimate the optimum clusters number present in image data point is used. This index takes account of the homogeneity in each cluster and is connected to the geometrical property of data set. The labeling is only performed to achieve the detection process in the ED frame. Since the left ventricle (LV) cluster has the greatest area of the cardiac images sequence in ED phase, a framing operation is performed to obtain, automatically, the "box" enclosing the LV cluster. THe EF assessed in 50 patients by the proposed method and a semi-automatic one, routinely used, are presented. A good correlation between the two methods EF values is obtained (R = 0.93). The LV contour found has been judged very satisfactory by a team of trained clinicians.
Extraction of the Left Ventricle (LV) contours is essential in the quantitative analysis of cardiac function. We present a new method that employs Fuzzy Isodat . a (FI) and conne . ctcd compo nents Labeling algorithms to automatically detect the LV contours. The FI algorithm is applied to each cardiac image to generate a given number of fuzzy subsets. Each heart's substructure ,
In this paper, a data fusion system for the automatic assessment of left Ventricular (LV) myocardium viability is presented. The system fuses LV contractile firnction parameters extracted from tagged Magnetic Resonance Images (MRI) and glucose metabolism rate imaged by a Positron Emission Tomography (PET) scanner. A registration package, which includes 2 0 and 3 0 visualization, has been designed.
The fusion system is based on Soft Computing techniques. It is a hierarchical modular network consisting of four Adaptive Network-based FuzzyInference Systems and is able to learn and adapt itself as well as integrates expert knowledge. The particular advantage of our system is that the MRI and PET complement each other leading to an accurate assessment of the myocardial viability. The presented system is a valuable tool for clinical and research applications.
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