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Enhanced optical flow field of left ventricular motion using quasi-Gaussian DCT filter

Riyadi S, Mustafa MM, Hussain A, Maskon O, Nor IF

Adv Exp Med Biol, 2011;696:461-9.
PMID: 21431586 DOI: 10.1007/978-1-4419-7046-6_46

Left ventricular motion estimation is very important for diagnosing cardiac abnormality. One of the popular techniques, optical flow technique, promises useful results for motion quantification. However, optical flow technique often failed to provide smooth vector field due to the complexity of cardiac motion and the presence of speckle noise. This chapter proposed a new filtering technique, called quasi-Gaussian discrete cosine transform (QGDCT)-based filter, to enhance the optical flow field for myocardial motion estimation. Even though Gaussian filter and DCT concept have been implemented in other previous researches, this filter introduces a different approach of Gaussian filter model based on high frequency properties of cosine function. The QGDCT is a customized quasi discrete Gaussian filter in which its coefficients are derived from a selected two-dimensional DCT. This filter was implemented before and after the computation of optical flow to reduce the speckle noise and to improve the flow field smoothness, respectively. The algorithm was first validated on synthetic echocardiography image that simulates a contracting myocardium motion. Subsequently, this method was also implemented on clinical echocardiography images. To evaluate the performance of the technique, several quantitative measurements such as magnitude error, angular error, and standard error of measurement are computed and analyzed. The final motion estimation results were in good agreement with the physician manual interpretation.

Matched MeSH terms: Data Compression/statistics & numerical data
Multiresolution wavelet analysis for efficient analysis, compression and remote display of long-term physiological signals

Khuan LY, Bister M, Blanchfield P, Salleh YM, Ali RA, Chan TH

Australas Phys Eng Sci Med, 2006 Jun;29(2):216-28.
PMID: 16845928

Increased inter-equipment connectivity coupled with advances in Web technology allows ever escalating amounts of physiological data to be produced, far too much to be displayed adequately on a single computer screen. The consequence is that large quantities of insignificant data will be transmitted and reviewed. This carries an increased risk of overlooking vitally important transients. This paper describes a technique to provide an integrated solution based on a single algorithm for the efficient analysis, compression and remote display of long-term physiological signals with infrequent short duration, yet vital events, to effect a reduction in data transmission and display cluttering and to facilitate reliable data interpretation. The algorithm analyses data at the server end and flags significant events. It produces a compressed version of the signal at a lower resolution that can be satisfactorily viewed in a single screen width. This reduced set of data is initially transmitted together with a set of 'flags' indicating where significant events occur. Subsequent transmissions need only involve transmission of flagged data segments of interest at the required resolution. Efficient processing and code protection with decomposition alone is novel. The fixed transmission length method ensures clutter-less display, irrespective of the data length. The flagging of annotated events in arterial oxygen saturation, electroencephalogram and electrocardiogram illustrates the generic property of the algorithm. Data reduction of 87% to 99% and improved displays are demonstrated.

Matched MeSH terms: Data Compression/methods*
A new approach for arrhythmia classification using deep coded features and LSTM networks

Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR

Comput Methods Programs Biomed, 2019 Jul;176:121-133.
PMID: 31200900 DOI: 10.1016/j.cmpb.2019.05.004

BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.
METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.

Matched MeSH terms: Data Compression/methods*
Clinical evaluation of watermarked medical images

Zain JM, Fauzi AM, Aziz AA

Conf Proc IEEE Eng Med Biol Soc, 2007 10 20;2006:5459-62.
PMID: 17946306

Digital watermarking medical images provides security to the images. The purpose of this study was to see whether digitally watermarked images changed clinical diagnoses when assessed by radiologists. We embedded 256 bits watermark to various medical images in the region of non-interest (RONI) and 480K bits in both region of interest (ROI) and RONI. Our results showed that watermarking medical images did not alter clinical diagnoses. In addition, there was no difference in image quality when visually assessed by the medical radiologists. We therefore concluded that digital watermarking medical images were safe in terms of preserving image quality for clinical purposes.

Matched MeSH terms: Data Compression