Tracking the LV in Velocity MR Images Using Fuzzy Clustering Ahmed Ismail Shihab and Peter Burger Imperial College of Science, Technology and Medicine Department of Computing 180 Queens Gate, London SW7 2BZ Abstract. Tracking the LV in cine MR cardiac images is a challenging computing application that is also relevant to the needs of clinicians. Using fuzzy clustering as the method of segmentation, this paper reports on whether velocity data can improve the accuracy of the results obtained through only tissue data. 1 PURPOSE Our application consists of analysing MR image cine sequences acquired at the mid-ventricular plane of the heart. We describe our use of the fuzzy -means clustering algorithm to track the LV area across a ‘heartbeat’. The images we use are conventional MR tissue density images as well as velocity images produced using a phase-sensitive MRI technique. The cine sequences of images are aligned with the short-axis of the left ventricle (LV). The velocity data is rendered as 3 images, , and , corresponding to the cartesian components of the velocity vector field at each pixel. The reference coordinate system has the - plane lying on the plane of imaging (aligned with the short-axis of the left ventricle) and the axis perpendicular to it (aligned with the LV long-axis). Figure 1. Examples of tissue density images: frames 0, 2, 6, 10 and 14 in an image sequence. The image sequences contain 16 frames. The sequences start at systole and end at early diastole. The time space between each frame and the next is approximately 40 ms. Figure 1 displays example frames from a sequence. Figure 2 displays only the first frame of each of the three velocity components. Figure 2. Examples of velocity images, frame 0 of , , and . Clustering algorithms have been used for image analysis, particularly segmentation, probably since the early seventies. The motivation for this use is that image intensity values tend to cluster in ways that e-mail address: ais@doc.ic.ac.uk