mitral valve prolapse classification from an echocardiography sequence using coherent point drift method based on fractal dimension

background: mitral valve prolapse (mvp) is now recognized as the most common cardiovalvular abnormality (cva) in the usa, but there has been much less agreement about other aspects of its epidemiology, diagnosis and clinical features. cva analysis still relies mainly on the visual assessment of echocardiographic imaging and manual measurements by experienced cardiologists. so, automating or semi-automating analysis of echocardiographic images for mvp detection is, therefore, highly desirable, but also challenging due to low image quality and high amount of speckle noise. objective: the main objective of this paper was to test the hypothesis that fractal dimension (fd) values are significantly higher in severe mvp and moderate mvp cases than those of healthy subjects in motion pattern of their mitral valves. materials and methods: in this paper, we present a noninvasive algorithm that does not require any segmentation. this approach has three steps: first, we extracted corners as landmarks from echocardiographic sequences based on susan corner detector. then, landmarks were tracked using coherent point drift (cpd) non-rigid registration. after that, the motion patterns of the landmarks were analyzed to extract features (fd) in order to classify the mitral valve behavior of 46 patients and 25 normals as normal, moderate or severe mvp. results: the results show that moderate mvp detection has a relatively good sensitivity (82%) and specificity (82%). conclusion: the motion pattern of landmarks in mvp patients, obtained by cpd method, differed from that of normals in such a way that the difference can be quantified and analyzed by fd.