Mustafa Özden

Adaptive artificial retina model to improve perception quality of retina implant recipients

Modeling of the retina in accordance with its physiological structure plays a key role in development of the high-performance image processing methods for sight restoration studies. In the literature, some Gaussian filter based artificial retinal processing models have been presented. In this study, as an alternative method to models which does not consider the characteristics of image content, adaptive DoG filter based artificial retina model that adaptively changes the bandwidths of the DoG filters according to local image data is developed. This model has two layers: the first layer models the interactions between receptors, horizontal and bipolar cells, the second layer models the interactions between bipolar, amacrine and ganglion cells of the retina layer. To evaluate the contribution of the model for image quality, some tests are performed by using video inputs. For this purpose, in these tests, spike counts based reconstructed images obtained by using this model, are analyzed. The retina model developed in this study which includes a 3-Dimensional Two-Stage Adaptive DOG (3D-ADOG) filter and the standard DOG filter based retina model are analyzed by using the statistical parameters of Mean Gray Level, Universal Quality Index and Histogram Similarity Ratio. In addition, the Histogram Similarity Ratio versus time is obtained and shown graphically for each model. From these performance analyses; it is concluded that 3D-ADOG filter-based retina model is closer to the original image in comparison with well-known classical DoG filter-based retina model and retinal implant systems based on this model will provide a better visual perception.

Spatiotemporal realization of an artificial retina model and performance evaluation through ISI- and spike count-based image reconstruction methods

Development of an artificial retina model that can mimic the biologic retina is a highly challenging task and this task is an important step in the development of a visual prosthesis. The receptive field structure of the retina layer is usually modeled as a 2D difference of Gaussian (DOG) filter profile. In the present study, as a different approach, a retina model including a 3D 2-stage DOG filter (3D-ADOG) that has an adaptively changing bandwidth with respect to the local image statistic is developed. Using this modeling, the adaptive image processing of the retina can be realized. The contribution of the developed model in terms of the image quality is evaluated via simulation studies using test images. The first simulation results, including only the spike count-based reconstruction for a test video sequence, were previously published. In this study, in addition to the spike count-based reconstruction, the interspike interval measure is also used in the simulation study. The reconstruction results are compared using the statistical parameters of the mean squared error (MSE), universal quality index (UQI), and histogram similarity ratio (HSR), which characterize the image likelihood. To evaluate the performance of the model versus time, time-dependent changes in the MSE, HSR, and UQI parameters are obtained and compared to the standard model. From these results, it is concluded that the 3D-ADOG filter-based retina model preserves the spatial details of the image and produces a larger number of different gray tone levels, which are important for the visual perception of an image, in comparison with the well-known classical DOG filter-based retina model. The retina implant systems based on this model can provide better visual perception for implant recipients.

A new image processing algorithm for visual prosthesis systems

Todays rapidly developing visual prosthesis systems can provide visual perception in the form of little light spots called as “phosphenes”. Major image processing studies in this area generally use only edge information in the images to form a visual stimulation, but this approach leads to information loss in the large extent. The aim of this study, therefore, is to implement an image processing algorithm that describes the visual scene more informatively in this phosphene domain. In this algorithm, we used contrast and different patterns to describe the regions or objects with different spatial features (such as color and texture,) in the image. These different patterns allow patients to better discriminate whether the regions have similar properties or not. By this way, the visual prosthesis recipients can gather extra information of the scene, addition to the edge information and their life quality may be increased. In this proceeding, main details of the developed algortihm are explained and results are presented.

An advanced ABR measurement system design based on C6713 digital signal processor

In this study, an ABR measurement system, which is used for determining auditory functional loss, threshold of hearing and brainstem neurologic disorders is designed and implemented. The system consists of four main stages. In the first stage, bioelectrical signals which occur after the transmission of stimulus signals are accumulated from skull skin by using electrodes. In the second stage, the obtained signals are transferred to analog module of the system and in this module, amplification and filtering processes are performed the analog signals. In the third stage, the analog signals are digitized by using the TMS320C6713 module and passed through several digital signal processing functions. In the last stage, demonstration and evaluation of the measurement result is carried out by the user interface software which is used for control of the system. Finally, an advanced ABR measurement system which is user-friendly and accessible to all phases of hardware and software and open to development for academic purpose, is designed and implemented.

Phospene image processing algorithm for simulation of the visual prostheses

Phospene image generation is an important step in development of the visual prostheses. Because, according to clinical studies, the patients see the small light spots called phospene when use visual prosthesis. In the literature, few studies have been published on this subject, and ever increasing need to better understand the perceptual and physiological aspects of prosthetic vision have emerged. The image processing algorithms are very important in generation of the phospene image that allows simulating the prosthetic vision systems. In this study, a prosthetic vision simulator algorithm, generates the phospene image by processing the visual inputs for various electrode configuration, has been developed. Algorithm uses 2D Gaussion function to model the phospene spots and considers phospene interaction between electrodes. The results of the algorithm have been presented and study has been concluded.

Image processing method for artificial sight systems based on contrast information

In this study, an image processing algorithm is developed for epiretinal implants. In this algorithm, it is presented an approach for adaptive frequency control that is one of the important parameters to generate stimuli. Standard 240×320 pixels video images in RGB format are processed as 10×10 pixel blocks. Consequently, it is designed to activate 24×32 pixels electrode matrix. As a retinal filter Gabor filter is preferred to DoG filter that is usually used in literature. The changes in pixels are modeled by using temporal filter. The results that belong to study and forward opinions are also presented.

Precision calibrator design and implementation for X-ray diagnostic devices

In this study it is aimed to design and develop an instrumental device which is capable of measuring some key parameters of X-ray tubes employed in many medical imaging devices including conventional X-ray imaging, fluoroscopy, mammography and dental X-ray imaging and those measured parameters which is critical for both imaging quality and patient safety aspects are acquired and evaluated extremely precise and very reliable with the help of latest technological advancements. Furthermore the designed instrumental device is supplemented with a Bluetooth connection capability which allows both remote controlling of the device and data transmission between the device and PC. Also a graphical user interface which is capable of recording multiple exposure data acquired via Bluetooth module and plotting comparison graphs in demand of user is also programmed in .NET environment. Remote controlling of the device and data transmission via Bluetooth avoided possible risks of working close to high voltage transformer of X-ray tube, hence gives an extra protection to medical device technicians. Taken measurements can be used to make periodic calibrations of medical imaging devices and this also helps reducing possible risks may be caused by overdosing during imaging.