ABSTRACT:
Identification and classification of intracardiac masses in echocardiogram is one of the significant processes in the diagnosis of cardiovascular disease. A robust back propagation neural network (RBPNN) technique is used to conquer every single conventional-issue utilizing the echocardiogram image analysis for this work, which consists of four phases such as noise removal, automatic segmentation, feature extraction, and intracardiac masses classification. Initially, the noise is diminished from the echocardiogram images utilizing the adaptive vector median filter (AVMF). Then, linear iterative vessel segmentation (LIVS) is applied for automatic segmentation of the masses followed by the extraction of texture features using the multiscale local binary pattern (MS-LBP) approach. Finally, RBPNN is employed to classify the heart mass from the images of echocardiogram with the layered kernel for the system combination. Extensive simulation results obtained using proposed AVMF-MS-LBP based RBPNN approach disclosed the superiority over existing intracardiac mass detection and classification approaches in terms of accuracy of 98.85%.
Keywords:
Echocardiogram; Cardiac Masses; Linear Iterative Vessel Segmentation; Multiscale Local Binary Pattern; Robust Back Propagation Neural Network
HIGHLIGHTS
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Cardiac mass image noise is diminished by Adaptive Vector Median Filter.
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The masses were automatically segmented dependent on Linear Iterative Vessel Segmentation strategy followed by texture features extracted utilizing the Multiscale Local Binary Pattern method.
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The classification done by Robust back propagation neural network.