ENDOCARDIAL SEGMENTATION IN CONTRAST ECHOCARDIOGRAPHY VIDEO WITH DENSITY BASED SPATIO-TEMPORAL CLUSTERING Prashant Bansod, U. B. Desai SPANN Lab., Electrical Engg. Department, Indian Institute of Technology, Bombay, India Nitin Burkule Cardiologist, Asian Heart Hospital and Research Center, Mumbai, India Keywords: Contrast echocardiography, Left ventricle, Segmentation, Spatio-temporal clustering. Abstract: We present a spatio-temporal clustering algorithm for detection of endocardial contours in short axis (SAX) contrast echocardiographic image sequences. A semiautomatic method for segmentation of left ventricle in SAX videos is proposed which uses this algorithm and at the same time requires minimal expert intervention. Expert is required to specify a few candidate points belonging to the contour, only in the first frame of the sequence. The initial contour is approximated by fitting an ellipse in the region defined by the points speci- fied. This region is identified as the principal cluster corresponding to the left ventriclular cavity. Later the density based clustering was applied for regularization on the inital contour. We have extended the DBSCAN algorithm for identification of the principal cluster corresponding to the left ventricle from the image. The al- gorithm also incorporates the temporal information from the adjacent frames during the segmentation process. The algorithm developed was applied to 10 data sets over full cardiac cycle and the results were validated by comparing computer generated boundaries to those manually outlined by one expert. The maximum error in the contours detected was ±2.9mm. The spatio-temporal clustering algorithm proposed in this paper offers an efficient semiautomatic segmentation of heart chambers in 2D contrast echocardiography sequences. 1 INTRODUCTION Amongst the various medical imaging modalities, two dimensional (2D) echocardiography is valuable for patients with heart diseases. It is noninvasive, real time, easy to use in clinical environment and of- fers relatively low cost solution as compared to other modalities (Bridal et. al, 2003). However, for eval- uation of cardiac functional parameters, segmenta- tion is to be carried out. Manual segmentation as routinely carried by experts is time consuming and tedious due to large image data in different stan- dard echo views over a full cardiac cycle. Again the manual method also suffers from inter-observer and intra-observer variability in measurements (Maes et. al, 1993). Many researchers have shown image processing applications to enhance clinical utility of echocardiography by automated and semiautomated endocardial border delineation and for evaluation of functional cardiac parameters (Noble and Boukerroui, 2006). In fact there is a continuous growing de- mand for the automated segmentation and quantifi- cation to support professionals in diagnosis. In re- cent years automated segmentation of heart cham- bers and in particular the left ventricle has received significant attention in 2D and 3D echocardiograms. However automatic edge definition and subsequent segmentation in echocardiograhic images is difficult due to presence of speckle noise, poor contrast, inher- ent dropouts, inter-cavity structures and variability of data along with orientation and positioning of trans- ducer (Setaredhan and Soragham, 1996). In recent years numerous clinical studies have shown the clinical utility of myocardial contrast echocardiography (MCE) in quantification of my- ocardial perfusion, left ventricle (LV) volumes, LV contours and cardiac functional parameters (Cohen et.al., 1998). There have been few reports of research attempts towards the semiautomatic and fully auto- matic segmentation of left ventricle from 2D con- trast enhanced echo images (Wolfer et. al, 1999). A very rigorous work for the segmentation problem 204 Bansod P., B. Desai U. and Burkule N. (2008). ENDOCARDIAL SEGMENTATION IN CONTRAST ECHOCARDIOGRAPHY VIDEO WITH DENSITY BASED SPATIO-TEMPORAL CLUSTERING. In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 204-209 DOI: 10.5220/0001064102040209 Copyright c  SciTePress