ICTACT Journal on Image and Video Processing

S. Nirmala ¹, S. Rajalaxmi ²

Affiliations
1 Muthayammal Engineering College, IN
2 Department of Electrical and Electronics Engineering, Paavai Engineering College, IN

Abstract

Wall tracking and Endocardium segmentation in Echocardiography images is a prime requirement for the diagnosis of major cardiac diseases. To avoid manual procedures of wall tracing and to provide a quantitative aid in the diagnosis procedure to the cardiologist, a new approach based on Local Chan Vese Model is proposed. The Model is based on curve evolution, local statistical function and level set method, and the accuracy of result is based on contour placement. This initial contour is generated through Radial Charge Fitting curve which is an auto generated curve based on Gauss Law. It is found that the inclusion of Radial Charge Fitting Curve with Local Chan Vese Model provides an accurate Endocardium Segmentation. The proposed method is compared with the Local Chan Vese Model with manual initial contours. It is proved that the proposed Local Chan Vese Model with Radial Charge Fitting Curve is performing accurate Endocardium Segmentation with minimal iterations.

Keywords

Endocardium, Segmentation, Local Chan Vese Model, Initial Contour, Gauss Law, Electric Field Intensity.

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S. Sridevi ¹, S. Nirmala ²

Affiliations
1 Department of Electronics and Communication Engineering, Paavai Engineering College, IN
2 Centre for Advanced Research, Department of Electronics and Communication Engineering, Muthayammal Engineering College, IN

Abstract

Speckle noise is the most prevalent noise in clinical ultrasound images. It visibly looks like light and dark spots and deduce the pixel intensity as murkiest. Gazing at fetal ultrasound images, the impact of edge and local fine details are more palpable for obstetricians and gynecologists to carry out prenatal diagnosis of congenital heart disease. A robust despeckling filter has to be contrived to proficiently suppress speckle noise and simultaneously preserve the features. The proposed filter is the generalization of Rayleigh maximum likelihood filter by the exploitation of statistical tools as tuning parameters and use different shapes of quadrilateral kernels to estimate the noise free pixel from neighborhood. The performance of various filters namely Median, Kuwahura, Frost, Homogenous mask filter and Rayleigh maximum likelihood filter are compared with the proposed filter in terms PSNR and image profile. Comparatively the proposed filters surpass the conventional filters.

Keywords

Rayleigh Maximum Likelihood Estimator, Speckle Suppression, Statistical Inference, Quadrilateral Kernel, Homogeneity Region.

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