Abstract:
Rheumatic Heart disease is an outcome of continuous episodes of rheumatic fever. It directly
damages heart valve especially mitral valve, which leads to thickened leaflet and fused tips. So
results in mitral stenosis a condition with narrowed valve opening which results in reduction of
flow of blood. Echo-cardiogram provides valuable information about heart condition for exact and
timely detection of heart disease but segmentation of ultrasound images has been considered as a
perplexing task due to its intensity combination or low contrast. Manual segmentation is a complex
process depends mainly upon the intensity of the images but often it includes local information
too. It also requires highly trained team of doctors, radiologists and technicians. The proposed
method incorporate automatically initialization of active contour with distance regularized level
set segmentation algorithm in order to segment the region of interest. Our approach combines the
advantages of level sets that uses the prior knowledge like shape, intensity, contours and edges of
object to be segmented. The aim of the task is to segment anterior mitral valve (AML). The results
of segmentation is demonstrated quantitatively with automatic curvature calculation, spline
modelling, and Skeletoniztion and Dice similarity coefficient (DSC) matrices. Results are
compared with the ground truth provided by highly trained manual segmented dataset. Data set
used in this task is provided by the Real Hospital Portuguese, in Recife, Brazil. The results are
verified using WHF criteria. The accuracy came out to be 82%. Proposed methodology also
compared with Piers method.
