In this paper, we present a novel blind watermarking method with secret key by embedding ECG signals in medical images. The embedding is done when the original image is compressed using the embedded zero-tree wavelet (EZW) algorithm. The extraction process is performed at the decompression time of the watermarked image. Our algorithm has been tested on several CT and MRI images and the peak signal to noise ratio (PSNR) between the original and watermarked image is greater than 35 dB for watermarking of 512 to 8192 bytes of the mark signal. The proposed method is able to utilize about 15% of the host image to embed the mark signal. This marking percentage has improved previous works while preserving the image details.
This paper presents the results of morphological heart arrhythmia detection based on features of electrocardiography, ECG, signal. These signals are obtained from MIT/BIH arrhythmia database. The ECG beats were first modeled using Hermitian basis functions, (HBF). In this step, the width parameter, sigma, of HBF was optimized to minimize the model error. Then, the feature vector which consists of the parameters of the model is used as an input to k-nearest neighbor, kNN, classifier to examine the efficiency of the model. In our experiments, seven different types of arrhythmias have been considered. We achieved the sensitivity of 99.00% and specificity of 99.84% which are comparable to previous works. These results were obtained in less than 0.6 second which is suitable for real-time diagnosis of heart arrhythmias.
An electrocardiogram (ECG) feature extraction and classification system has been developed and evaluated using Quartus II 7.1 belong to Altera Ltd. In wavelet domain QRS complexes were detected and each complex was used to locate the peaks of the individual waves. Then, fuzzy classifier block used these features to classify ECG beats. Three types of arrhythmias and abnormalities were detected using the procedure. The completed algorithm was embedded into Field Programmable Gate Array (FPGA). The completed prototype was tested through software-generated signals, in which test scenarios covering several kinds of ECG signals on MIT-BIH Database. For the purpose of feeding signals into the FPGA, a software was designed to read signal files and import them to the LPT port of computer that was connected to FPGA. From the results, it was achieved that the proposed prototype could do real time monitoring of ECG signal for arrhythmia detection. We also implemented algorithm in a sequential structure device like AVR microcontroller with 16 MHZ clock for the same purpose. External clock of FPGA is 50 MHZ and by utilizing of Phase Lock Loop (PLL) component inside device, it was possible to increase the clock up to 1.2 GHZ in internal blocks. Final results compare speed and cost of resource usage in both devices. It shows that in cost of more resource usage, FPGA provides higher speed of computation; because FPGA makes the algorithm able to compute most parts in parallel manner.
Abstract. This work studies the convex relaxation approach to the left ventricle (LV) segmentation which gives rise to a challenging multi-region seperation with the geometrical constraint. For each region, we consider the global Bhattacharyya metric prior to evaluate a gray-scale and a radial distance distribution matching. In this regard, the studied problem amounts to finding three regions that most closely match their respective input distribution model. It was previously addressed by curve evolution, which leads to sub-optimal and computationally intensive algorithms, or by graph cuts, which result in heavy metrication errors (grid bias). The proposed convex relaxation approach solves the LV segmentation through a sequence of convex sub-problems. Each sub-problem leads to a novel bound of the Bhattacharyya measure and yields the convex formulation which paves the way to build up the efficient and reliable solver. In this respect, we propose a novel flow configuration that accounts for labelingfunction variations, in comparison to the existing flow-maximization configurations. We show it leads to a new convex max-flow formulation which is dual to the obtained convex relaxed sub-problem and does give the exact and global optimums to the original non-convex sub-problem. In addition, we present such flow perspective gives a new and simple way to encode the geometrical constraint of optimal regions. A comprehensive experimental evaluation on sufficient patient subjects demonstrates that our approach yields improvements in optimality and accuracy over related recent methods.
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