Chronic postsurgical pain (CPSP) is a possible complication of various surgical procedures, which can impair patients' quality of life while also contributing to chronic opioid use. Multiple biopsychosocial factors put patients at risk for CPSP. Multimodal analgesia with the use of various pharmacologic and regional anesthetic techniques can help reduce the incidence and severity of CPSP. However, the relationship between various perioperative analgesic strategies and the development of CPSP is not fully understood. Although the use of multimodal analgesia will not automatically prevent CPSP and/or prolonged opioid consumption, there is potential to do so, especially by means of regional techniques.
Velocity Magnetic Resonance (MR) images are a novel form of medical images. A special gradientmodulation technique is utilised to capture motion velocity of tissue and blood. As well as the tissue density image, there are also other images that depict the velocity components along axes defined relative to the plane of imaging. The images are of the cardiac region and are aligned with the short-axis of the left ventricle. We present the results of clustering cardiac image sequences using the Fuzzy c-Means (FCM) algorithm. Our paper demonstrates how the application of clustering to one frame in the cine sequence of images can be utilised in order to track reasonably well the contraction and relaxation of the Left Ventricle. Our paper shows that this imaging technique is generally accurate and certainly adds to the information already contained in the tissue density images.
INTRODUCTIONMagnetic Resonance (MR) images picture anatomic detail by registering tissue density in the plane of imaging. Every pixel in an MR image carries a value that is proportional to the average tissue density registered by the MR scanner at the corresponding approximate location in the plane of imaging.Our application consists of analysing image cine sequences acquired at the mid-ventricular plane of the heart. The cine sequence of images is aligned with the short-axis of the left ventricle (LV). The number of images in the sequence is 16. For each of the density images, velocity images of the same anatomic plane of the ventricle are produced using a phase-sensitive MItT technique. The velocity data is rendered as 3 images, v , v, and v , corresponding to the cartesian components of the velocity vector field V. The reference coordinate system we use has the x-y plane lying on the plane of imaging (aligned with the short-axis of the left ventricle) and the z axis perpendicular to it (aligned with the LV long-axis).In this paper, we describe our use of the fuzzy c-means clustering algorithm to analyse the image sequence. The fuzzy c-means (FCM) algorithm is also sometimes called fuzzy k-means and fuzzy ISODATA. The monograph by James Bezdek [1] is the most widely cited reference for FCM. Using clustering algorithms for image analysis, particularly segmentation, probably goes back to the early seventies. The motivation behind its use is that image intensity values tend to cluster in ways that correspond to the physical description of the image. So, for example, in a picture of a dark-coloured aeroplane up in the sky, the sky's colour would cluster around "bright blue" ,while the aeroplane's colour would cluster around "dark grey".
Some significant events in sports matches occur too quickly to be detected by conventional video. Audio signals, normally sampled at a much higher rate, provide a way to detect such short events. Here, we employ approaches inspired by methods used in automatic speech recognition-use of templates of Mel Frequency Cepstral Coefficients (MFCCs) compiled over several adjacent time windows, together with Principal Components Analysis (PCA)-to classify sound events, including different tennis strokes, bounces of the ball, echos, speech and audience applause, occurring in the relatively controlled situation of major championship tennis matches. Good success rates were obtained for classification of the 1504 sound events in the available recordings. We go on to use Markov models to predict sequences of strokes (i.e. produce "synthetic rallies") and combine the predictions of the acoustic classifier and the Markov model, using a Bayesian approach to produce a hybrid classifier. These approaches could yield valuable information, of benefit to spectators, match officials and coaches in tennis and other sports (including cricket, baseball and golf) , for making video games (such as the Nintendo Wii) more realistic and also help identify "unusual" or "unexpected" salient sounds.
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