Journal:Informatica
Volume 20, Issue 4 (2009), pp. 539–554
Abstract
Digital signal processing is one of the most powerful technologies, developed by achievements in science and electronics engineering. Achievements of this technology significantly influenced communications, medicine technique, radiolocation and other. Digital signal processors are usually used for effective solution of digital signal processing problems class. Today digital signal processors are widely used practically in all fields, in which information processing in real-time is needed. Creation of diagnostic medicine systems is one of perspective fields using digital signal processors. The aim of this work was to create digital mathematical model of blood circulation analysis system using digital signal processing instead of analogical nodes of device. In first stage – work algorithm of blood circulation analysis system and mathematical model of blood circulation analysis system in Matlab–Simulink environment was created. In second stage – mathematical model was tested experimentally. Mathematically imitated Doppler signal was sent to tissue and was reflected. The signal was processed in digitally, blood flow direction was marked and blood speed was evaluated. Experimentation was done with real signals that were recorded while investigating patients in eye clinics. Gained results confirmed adequacy of created mathematical model to real analogical blood circulation analysis system (Lizi et al., 2003).
Journal:Informatica
Volume 18, Issue 2 (2007), pp. 267–278
Abstract
A technique to improve an eye cataract early detection and quantitative evaluation of maturity using ultrasound was investigated. A broadband coherent signal, backscattered from an eye lens tissue, was digitized, recorded and processed. A new parameter – lens quality was proposed for the human eye cataract quantitative evaluation. Lens quality reflects two phenomena of ultrasound interaction with lens tissue – attenuation and scattering. Digital technique for echo-signal energy and time frequency analysis was applied, ultrasound waves scattering strength and spectral slope was calculated.
Experimental statistical investigations performed with signals divided into five groups – mature cataract, immature form of cataract, incipience cataract phase, healthy lenses and human eye phantom. Investigations have showed that value of specific quality in the test groups vary in the wide range from 1 to 60. This feature allows theoretically differentiate eye lenses cataract in different classes with defined boundaries. Presented results show that we with high reliability can differentiate lenses into three groups: healthy lenses (QL>50), lenses with incipient or immature cataract (QL=2-20) and lenses with mature cataract (QL<1).
The investigated method can be used for an eye lens classification and for early cataract detection This technique was used at the Department of Ophthalmology, Institute for Biomedical Research, Kaunas University of Medicine.
Journal:Informatica
Volume 16, Issue 4 (2005), pp. 541–556
Abstract
This paper presents a new approach for human cataract automatical detection based on ultrasound signal processing. Two signal decomposition techniques, empirical mode decomposition and discrete wavelet transform are used in the presented method. Performance comparison of these two decomposition methods when applied to this specific ultrasound signal is given. Described method includes ultrasonic signal decomposition to enhance signal specific features and increase signal to noise ratio with the following decision rules based on adaptive thresholding. The resulting detection performance of the proposed method using empirical mode decomposition was better to compare to discrete wavelet transform and resulted in 70% correctly identified “healthy subject” cases and 82%, 97% and 100% correctly identified “cataract cases” in the incipience, immature and mature cataract subject groups, respectively. Discussion is given on the reasons of different results and the differences between the two used signal decomposition techniques.