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Automatic Classification of Intracardiac Tumor and Thrombi in Echocardiography Based on Sparse Representation
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Automatic Classification of Intracardiac Tumor and Thrombi in Echocardiography Based on Sparse Representation

Category : Image Processing


Sub Category : BIOMEDICAL


Project Code : IMP10


Project Abstract

Identification of intracardiac masses in echocardiograms is one important task in cardiac disease diagnosis. To improve diagnosis accuracy, a novel fully automatic classification method based on the sparse representation is proposed to distinguish intracardiac tumor and thrombi in echocardiography. First, a region of interest is cropped to define the mass area. Then, a unique globally denoising method is employed to remove the speckle and preserve the anatomical structure. Subsequently, the contour of the mass and its connected atrial wall are described by the K-singular value decomposition and a modified active contour model. Finally, the motion, the boundary as well as the texture features are processed by a sparse representation classifier to distinguish two masses. Ninety-seven clinical echocardiogram sequences are collected to assess the effectiveness.

 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:

          A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of cardiac disease (CD). The system is automatically generated from an initial annotated dataset, using a four stage methodology.

PROPOSED CONCEPT:

          Identification of intracardiac masses in echocardiograms is one important task in cardiac disease diagnosis. To improve diagnosis accuracy, a novel fully automatic classification method based on the sparse representation is proposed to distinguish intracardiac tumor and thrombi in echocardiography.

EXISTING  TECHNIQUE :

          FUZZY MODELING

PROPOSED ALGORITHM:

          K-SVD SPARSE REPRESENTATION

TECHNIQUE DEFINITION:

          The crisp set of rules is transformed into a fuzzy model using a fuzzy membership function instead of the crisp one, and fuzzy equivalents of the binary AND (∧) and OR (∨) operators, which are the T and S norms. The T and S norms are defined as the minimum and maximum operators, respectively.

ALGORITHM DEFINITION:

           The K-SVD aims at formulating the image by using a few linear combinations drawn from a large and redundant dictionary. Through an over complete dictionary, the original image is decomposed into a sparse coefficients matrix populated primarily with zeros. Only a few nonzero coefficients reveal the nature of the image, greatly reducing the complexity of the original image.

DRAWBACKS:

          In most hospitals, echocardiographic identifications are carried out by cardiologists manually. The diagnosis is time-consuming.

          Fuzzy rule-based decision support system in the diagnosis is still challenging in intracardiac masses identification due to the similar echocardiographic appearances of two masses and the suboptimal image quality including large amount of speckle noise, signal drop-out, artifacts, and missing contours.

 

ADVANTAGES:

          The advantage of the proposed method is its resistance to changes of the visual information in the analyzed image and to noise and artifacts, often present in echocardiograms. 

          Classification and segmentation of intracardiac masses in cardiac tumor echocardiograms.

          A few nonzero coefficients reveal the nature of the image, greatly reducing the complexity of the original image.

 


 
 
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