Shaou-Gang Miaou

A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information

This paper proposes a new approach to detect the fall of the elderly. The detection system uses a MapCam (omni-camera) to capture images and performs image processing over the images. The personal information of each individual is considered in the processing task. The MapCam is used to capture 360deg scenes simultaneously and eliminate any blind viewing zone. The personal information is combined into the system and makes it smarter by customizing the system for each individual. With personal information such as height, weight, and electronic health history, we can adjust the detection sensitivity on a case by case basis to reduce unnecessary alarms, and put more attention on the elderly with special diseases or conditions. We perform a simple experiment to verify the feasibility of our approach. The experimental results show that the successful rates of fall detections with and without personal information are 79.8% and 68%, respectively. The Kappa value of the system is 0.798 which is higher than 0.75, showing that we have a reliable system

Wavelet-based ECG compression using dynamic vector quantization with tree codevectors in single codebook

In this paper, we propose a novel vector quantizer (VQ) in the wavelet domain for the compression of electrocardiogram (ECG) signals. A vector called tree vector (TV) is formed first in a novel structure, where wavelet transformed (WT) coefficients in the vector are arranged in the order of a hierarchical tree. Then, the TVs extracted from various WT subbands are collected in one single codebook. This feature is an advantage over traditional WT-VQ methods, where multiple codebooks are needed and are usually designed separately because numerical ranges of coefficient values in various WT subbands are quite different. Finally, a distortion-constrained codebook replenishment mechanism is incorporated into the VQ, where codevectors can be updated dynamically, to guarantee reliable quality of reconstructed ECG waveforms. With the proposed approach both visual quality and the objective quality in terms of the percent of root-mean-square difference (PRD) are excellent even in a very low bit rate. For the entire 48 records of Lead II ECG data in the MIT/BIH database, an average PRD of 7.3% at 146 b/s is obtained. For the same test data under consideration, the proposed method outperforms many recently published ones, including the best one known as the set partitioning in hierarchical trees.

Wavelet-based lossy-to-lossless ECG compression in a unified vector quantization framework

In a prior work, a wavelet-based vector quantization (VQ) approach was proposed to perform lossy compression of electrocardiogram (ECG) signals. We investigate and fix its coding inefficiency problem in lossless compression and extend it to allow both lossy and lossless compression in a unified coding framework. The well-known 9/7 filters and 5/3 integer filters are used to implement the wavelet transform (WT) for lossy and lossless compression, respectively. The codebook updating mechanism, originally designed for lossy compression, is modified to allow lossless compression as well. In addition, a new and cost-effective coding strategy is proposed to enhance the coding efficiency of set partitioning in hierarchical tree (SPIHT) at the less significant bit representation of a WT coefficient. ECG records from the MIT/BIH Arrhythmia and European ST-T Databases are selected as test data. In terms of the coding efficiency for lossless compression, experimental results show that the proposed codec improves the direct SPIHT approach and the prior work by about 33% and 26%, respectively.

A quality-on-demand algorithm for wavelet-based compression of electrocardiogram signals

For the compression of medical signals such as electrocardiogram (ECG), excellent reconstruction quality of a highly compressed signal can be obtained by using a wavelet-based approach. The most widely used objective quality criterion for the compressed ECG is called the percent of root-mean-square difference (PRD). In this paper, given a user-specified PRD, an algorithm is proposed to meet the PRD demand by searching for an appropriate bit rate in an automatic, smooth, and fast manner for the wavelet-based compression. The bit rate searching is modeled as a root-finding problem for a one-dimensional function, where an unknown rate-distortion curve represents the function and the desired rate is the root to be sought. A solution derived from root-finding methods in numerical analysis is proposed. The proposed solution is incorporated in a well-known wavelet-based coding strategy called set partitioning in hierarchical trees. ECG signals taken from the MIT/BIH database are tested, and excellent results in terms of convergence speed, quality variation, and coding performance are obtained.

A Lossless Compression Method for Medical Image Sequences Using JPEG-LS and Interframe Coding

Hospitals and medical centers produce an enormous amount of digital medical images every day, especially in the form of image sequences, which requires considerable storage space. One solution could be the application of lossless compression. Among available methods, JPEG-LS has excellent coding performance. However, it only compresses a single picture with intracoding and does not utilize the interframe correlation among pictures. Therefore, this paper proposes a method that combines the JPEG-LS and an interframe coding with motion vectors to enhance the compression performance of using JPEG-LS alone. Since the interframe correlation between two adjacent images in a medical image sequence is usually not as high as that in a general video image sequence, the interframe coding is activated only when the interframe correlation is high enough. With six capsule endoscope image sequences under test, the proposed method achieves average compression gains of 13.3% and 26.3 % over the methods of using JPEG-LS and JPEG2000 alone, respectively. Similarly, for an MRI image sequence, coding gains of 77.5% and 86.5% are correspondingly obtained.

Quality driven gold washing adaptive vector quantization and its application to ECG data compression

The gold washing (GW) adaptive vector quantization (AVQ) (GW-AVQ) is a relatively new scheme for data compression. The adaptive nature of the algorithm provides the robustness for wide variety of the signals. However, the performance of GW-AVQ is highly dependent on a preset parameter called distortion threshold (dth) which must be determined by experience or trial-and-error. We propose an algorithm that allows us to assign an initial dth arbitrarily and then automatically progress toward a desired dth according to a specified quality criterion, such as the percent of root mean square difference (PRD) for electrocardiogram (ECG) signals. A theoretical foundation of the algorithm is also presented. This algorithm is particularly useful when multiple GW-AVQ codebooks and, thus, multiple dths are required in a subband coding framework. Four sets of ECG data with entirely different characteristics are selected from the MIT/BIH database to verify the proposed algorithm. Both the direct GW-AVQ and a wavelet-based GW-AVQ are tested. The results show that a user specified PRD can always be reached regardless of the ECG waveforms, the initial selection of dth or whether a wavelet transform is used in conjunction with the GW-AVQ. An average result of 6% in PRD and 410 bits/s in compressed data rate is obtained with excellent visual quality.

Multichannel ECG compression using multichannel adaptive vector quantization

Adaptive vector quantization (AVQ) is a recently proposed approach for electrocardiogram (ECG) compression. The adaptability of the approach can be used to control the quality of reconstructed signals. However, like most of other ECG compression methods, AVQ only deals with the single-channel ECG, and for the multichannel (MC) ECG, coding ECG signals on a channel by channel basis is not efficient, because the correlation across channels is not exploited. To exploit this correlation, an MC version of AVQ is proposed. In the proposed approach, the AVQ index from each channel is collected to form a new input vector. The vector is then vector quantized adaptively using one additional codebook called index codebook. Both the MIT/BIH database and a clinical Holter database are tested. The experimental results show that, for exactly the same quality of reconstructed signals, the MC-AVQ performs better than single-channel AVQ in terms of bit rate. A theoretical analysis supporting this result is also demonstrated in this paper. For the same and relatively good visual quality, the average compressed data rate/channel is reduced from 293.5 b/s using the single-channel AVQ to 238.2 b/s using the MC-AVQ in the MIT/BIH case.

Automatic quality control for wavelet-based compression of volumetric medical images using distortion-constrained adaptive vector quantization

The enormous data of volumetric medical images (VMI) bring a transmission and storage problem that can be solved by using a compression technique. For the lossy compression of a very long VMI sequence, automatically maintaining the diagnosis features in reconstructed images is essential. The proposed wavelet-based adaptive vector quantizer incorporates a distortion-constrained codebook replenishment (DCCR) mechanism to meet a user-defined quality demand in peak signal-to-noise ratio. Combining a codebook updating strategy and the well-known set partitioning in hierarchical trees (SPIHT) technique, the DCCR mechanism provides an excellent coding gain. Experimental results show that the proposed approach is superior to the pure SPIHT and the JPEG2000 algorithms in terms of coding performance. We also propose an iterative fast searching algorithm to find the desired signal quality along an energy-quality curve instead of a traditional rate-distortion curve. The algorithm performs the quality control quickly, smoothly, and reliably.

Transmitting SPIHT compressed ECG data over a next-generation mobile telecardiology testbed

Recently, the set partitioning in hierarchical tree (SPIHT) was shown to be an excellent algorithm for ECG compression. However, how it performs in a cellular phone based wireless environment for telemedicine applications is not known. In this paper, a joint design for SPIHT-based ECG data compression method over a next-generation mobile telecardiology testbed based on the 3G cellular phone standard is proposed and the performance of the testbed for the compressed ECG data segments selected from the MIT-BIH arrhythmia database is evaluated in terms of BER (bit error rate), PRD (percent of root-mean-square difference), compression ratio (CR), transmission time, and diagnostic quality. The simulation results show that during the successful transmission of compressed ECG (when BER is less than 10/sup -5/), a CR of 8:1 provides a 87.5% reduction in total transmission time and a higher CR up to 20 can reduce up to 95% of the required time to transmit the ECG. Furthermore, most characteristics of the received ECG waveform, such as P wave, QRS complex, and T wave, can be reserved with clinically acceptable quality.

WAVELET-BASED LOSSY-TO-LOSSLESS MEDICAL IMAGE COMPRESSION USING DYNAMIC VQ AND SPIHT CODING

As the coming era of digitized medical information, a close-at-hand challenge to deal with is the storage and transmission requirement of enormous data, including medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose a dynamic vector quantization (DVQ) scheme with distortion-constrained codebook replenishment (DCCR) mechanism in wavelet domain. In the DVQ-DCCR mechanism, a novel tree-structure vector and the well-known SPIHT technique are combined to provide excellent coding performance in terms of compression ratio and peak signal-to-noise ratio for lossy compression. For the lossless compression in similar scheme, we replace traditional 9/7 wavelet filters by 5/3 filters and implement the wavelet transform in the lifting structure. Furthermore, a detection strategy is proposed to stop the SPIHT coding for less significant bit planes, where SPIHT begins to lose its coding efficiency. Experimental results show that the proposed algorithm is supe...