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Searching and mining medical time series databases is extremely challenging due to large, high entropy, and multidimensional datasets. Traditional time series databases are populated using segments extracted by a sliding window. The resulting database index contains an abundance of redundant time series segments with little to no alignment. This paper presents the idea of "salient segmentation"...
In clinical practice, electrocardiographs (ECG)are used in various ways. In the most simple case, directly after the ECG has been recorded, the doctor analyses it and makes the diagnosis. In other cases, e.g. when the abnormality can only be observed occasionally, at a previously unknown time, the ECG is being recorded continuously. Fast automatic recognition of abnormalities of ECG signals may substantially...
In this study, we have developed a prototype of wireless ECG biofeedback platform which consists of three parts: a full-featured signal acquisition platform, a full set of software applications for signal displaying, processing and analysis, and a matlab-based audio generator. Moreover, preliminary experiments on heart rate variability (HRV) have been performed to evaluate the performances of this...
Spinal cord analysis is an important problem in the study of various neurological diseases. Current segmentation and analysis methods in clinical use are slow and labor-intensive, especially for pathological data. ``Spinal Crawlers'' are a recently developed technique based on an artificial life framework for medical image analysis that complements classical deformable models (snakes and deformable...
In this paper we present a multiphase level set model for histology image segmentation. Global K-means energy is weighted by a Gaussian kernel to cluster image pixels in local neighborhoods. We group these local clusters into different source classes using a multiphase level set model to produce the final segmentation results. Our energy functional is formulated as the integral of local K-means energies...
This paper presents a new cellular automata-based unsupervised image segmentation technique that is motivated by the interactive grow-cut algorithm. In contrast to the traditional method which requires user-interaction to identify classes, the unsupervised grow-cut algorithm (UGC) starts with a random number of seed points and automatically converges to a natural segmentation. This is useful when...
This paper presents a novel algorithm for computer-assisted classification of cervical cancers using digitized histology images of biopsies. Texture analysis of the nuclei structure is very important for classification of cervical cancer histology. In this paper we present a two-tier classification strategy using Gabor filter banks for local classification and abnormality spread for global taxonomy...
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