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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.