No abstract

<p>Cardiac wall motions classification on 2-dimensional (2D) echocardiographic images is an important issue for quantitative diagnosiing of heart disease. Unfortunately, the bad quality of echocardiogram cause computationally classification on cardiac wall motions is still become a big homework for many researchers to provide the best result. Echocardiogram is produced by soundwaves which absolutely make its images have speckle noise in different intensity. Therefore, this research improves a set of methodology to classify cardiac wall motion semi-automatically. Raw echocardiogram will be enhanced and segmented to take the boundary of endocardium of left ventricular in PSAX cardiac images. New improvement of Semi-automatically methodology is approach on detecting the contour of endocardium and will be inputed as good features in Lucas-Kanade Optical Flow in all sequential echocargraphic images. On classifying cardiac wall motions, this research proposes two important features including length of displacement and flow direction. New proprosed flow determination algorithm and Euclidean distance is used to calculate those features. All the features will be trained by Neural Network (NN) and validated by Leave One Out (LOO) to get accurate result. NN method, which is validated by LOO, has the best result of 81.82% correctness than the other compared methods.</p>

Cardiac function assessment plays an important role in daily cardiology and ultrasound. Full automatic cardiac segmentation is a challenging study because cardiac ultrasound imaging has low contrast and irregular moves. In this research, full automatic cardiac segmentation for cardiac diseases is presented. The technique used Initial Center Boundary, Pre-processing, Triangle Segmentation and Optical Flow. The first step is determining the initial center boundary. The second step is using Pre-processing to eliminate noise. The third step is Triangle Segmentation to detect cardiac boundary and reconstruct the accurate border. The last step is applying Optical Flow method to detect and track the border for every frame in a cardiac video. The performance segmentation for assessment errors cardiac cavity obtained an average triangle 8.18%, snake 19.94% and watershed 15.97%. The experiments showed that triangle method is able to find and improve the segmentation of cardiac cavity images with accurate. The result can be seen that error between system and average of users is only less than 5.6%. This indicates that this method is effective to segment and tracking cardiac cavity in a cardiac video.