Informatica

Eye fundus imaging is a useful, non-invasive tool in disease progress tracking, in early detection of disease and other cases. Often, the disease diagnosis is made by an ophthalmologist and automatic analysis systems are used only for support. There are several commonly used features for disease detection, one of them is the artery and vein ratio measured according to the width of the main vessels. Arteries must be separated from veins automatically in order to calculate the ratio, therefore, vessel classification is a vital step. For most analysis methods high quality images are required for correct classification. This paper presents an adaptive algorithm for vessel measurements without the necessity to tune the algorithm for concrete imaging equipment or a specific situation. The main novelty of the proposed method is the extraction of blood vessel features based on vessel width measurement algorithm and vessel spatial dependency. Vessel classification accuracy rates of 0.855 and 0.859 are obtained on publicly available eye fundus image databases used for comparison with another state of the art algorithms for vessel classification in order to evaluate artery-vein ratio ($AVR$). The method is also evaluated with images that represent artery and vein size changes before and after physical load. Optomed OY digital mobile eye fundus camera Smartscope M5 PRO is used for image gathering.

Glaucoma is one of the most insidious eye diseases the occurrence and progression of which a human does not feel. This article provides a brief overview of the eye nerve parameterization methods and algorithms. Parameterization itself is an important task that provides and uniquely defines the structure of the optic nerve disc and further can be used in disease detection or other studies that require a parametric estimate of the eye fundus pattern. So far, planimetric completely automated parameterization of excavation from eye fundus images has not been investigated in detail in the scientific literature. In this article, the authors describe an automated excavation and parameterization algorithm and make the correlation analysis of parameters obtained by both automated and interactive techniques. The obtained results are then compared with those produced by Optical Coherence and Heidelberg Retina Tomography. Finally, the article discusses glaucoma disease detection abilities using the estimated parameters of the eye fundus structures, obtained by different parameterization techniques.

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).

New information technologies provide a possibility of collecting a large amount of fundus images into databases. It allows us to use automated processing and classification of images for clinical decisions. Automated localization and parameterization of the optic nerve disc is particularly important in making a diagnosis of glaucoma, because the main symptoms in these cases are relations between the optic nerve and cupping parameters. This article describes the automated algorithm for the optic nerve disc localization and parameterization by an ellipse within colour retinal images. The testing results are discussed as well.

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.

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.

Cataract is very frequent disease of human eye and the diagnosis of this disease is not difficult. However, it is important to describe it quantitatively, but it is difficult using only the slit lamp. Ultrasound examinations are widely used in ophthalmology. Piezoelectric crystals generate ultrasound waves of 5–50 MHz. Short pulses of 2 to 3 cycles are sent from transducer into the eye. These pulses go through the tissues of the eye with the speed that is inversely proportional to the density and elasticity of the eye. Acoustic parameters of biologic tissues are described by velocity and attenuation coefficient. It is known that in soft tissues the attenuation coefficient is approximately proportional to the frequency – high frequency components of echoes are attenuated more than the lower frequency components. Results of ultrasound attenuation characteristics of human nuclear cataract are presented. It was shown that ultrasound attenuation of nuclear cataract could be used as “second opinion” for physicians decision support.

Application of knowledge discovery in databases (data mining) for medical decision support is discussed in this work. The aim of the study was to use decision support algorithm for the differential diagnosis of intraocular tumors using parameters from eye images obtained by the ultrasound examination. Application of predictive modeling algorithm for decision tree formation using See5.0/C5.0 data mining system is presented. The decision tree was build using tumor geometry and microstructure parameters. The use of decision tree allows to differentiate tumors from other tumor-like formations. Low percentage of diagnostic errors shows that decision tree is reliable enough to offer it as “second opinion” for physician's decision support.