This study focuses on the design of electrical stimulator for retinal prosthesis. The stimulator must be designed such that the occurrence of electrolysis or any irreversible process in the electrodes and flexible lead is prevented in order to achieve safe stimulation over long periods using the large number of electrodes. Some types of biphasic current pulse circuits, charge balance circuits, and AC power delivery circuits were developed to address this issue. Electronic circuitry must be introduced in the stimulator to achieve the large number of electrodes required to obtain high quality of vision. The concept of a smart electrode, in which a microchip is embedded inside an electrode, is presented for future retinal prostheses with over 1000 electrodes.

In this letter, a novel and highly efficient haze removal algorithm is proposed for haze removal from only a single input image. The proposed algorithm is built on the atmospheric scattering model. Firstly, global atmospheric light is estimated and coarse atmospheric veil is inferred based on statistics of dark channel prior. Secondly, the coarser atmospheric veil is refined by using a fast Tri-Gaussian filter based on human retina property. To avoid halo artefacts, we then redefine the scene albedo. Finally, the haze-free image is derived by inverting the atmospheric scattering model. Results on some challenging foggy images demonstrate that the proposed method can not only improve the contrast and visibility of the restored image but also expedite the process.

This paper presents a method for registering retinal images. Retinal image registration is crucial for the diagnoses and treatments of various eye conditions and diseases such as myopia and diabetic retinopathy. Retinal image registration is challenging because the images have non-uniform contrasts and intensity distributions, as well as having large homogeneous non-vascular regions. This paper provides a new retinal image registration method by effectively combining expectation maximization principal component analysis based mutual information (EMPCA-MI) with salient features. Experimental results show that our method is more efficient and robust than the conventional EMPCA-MI method.

In retinal blood vessel extraction through background removal, the vessels in a fundus image which appear in a higher illumination variance area are often missing after the background is removed. This is because the intensity values of the vessel and the background are nearly the same. Thus, the estimated background should be robust to changes of the illumination intensity. This paper proposes retinal blood vessel extraction using background estimation. The estimated background is calculated by using a weight surface fitting method with a high degree polynomial. Bright pixels are defined as unwanted data and are set as zero in a weight matrix. To fit a retinal surface with a higher degree polynomial, fundus images are reduced in size by different scaling parameters in order to reduce the processing time and complexity in calculation. The estimated background is then removed from the original image. The candidate vessel pixels are extracted from the image by using the local threshold values. To identify the true vessel region, the candidate vessel pixels are dilated from the candidate. After that, the active contour without edge method is applied. The experimental results show that the efficiency of the proposed method is higher than the conventional low-pass filter and the conventional surface fitting method. Moreover, rescaling an image down using the scaling parameter at 0.25 before background estimation provides as good a result as a non-rescaled image does. The correlation value between the non-rescaled image and the rescaled image is 0.99. The results of the proposed method in the sensitivity, the specificity, the accuracy, the area under the receiver operating characteristic (ROC) curve (AUC) and the processing time per image are 0.7994, 0.9717, 0.9543, 0.9676 and 1.8320 seconds for the DRIVE database respectively.

We review recently obtained results for CMOS (Complementary Metal Oxide Semiconductor) imaging devices used in biomedical applications. The topics include dish type image sensors, deep-brain implantation devices for small animals, and retinal prosthesis devices. Fundamental device structures and their characteristics are described, and the results of in vivo experiments are presented.

Since retina blood vessels (RBV) are a major factor in ophthalmological diagnosis, it is essential to detect RBV from a fundus image. In this letter, we proposed the detection method of RBV using a morphological analysis and support vector machine classification. The proposed RBV detection method consists of three strategies: pre-processing, features extraction and classification. In pre-processing, noises were reduced and RBV were enhanced by anisotropic diffusion filtering and illumination equalization. Features were extracted by using the image intensity and morphology of RBV. And a support vector machine (SVM) classification algorithm was used to detect RBV. The proposed RBV detection method was simulated and validated by using the DRIVE database. The averages of accuracy and TPR are 0.94 and 0.78, respectively. Moreover, by comparison, we confirmed that the proposed RBV detection method detected RBV better than the recent RBV detections methods.

Adaptation process of retina helps human visual system to see a high dynamic range scene in real world. This paper presents a simple static local adaptation method for high dynamic range image compression based on a retinal model. The proposed simple model aims at recreating the same sensations between the real scene and the range compressed image on display device when viewed after reaching steady state local adaptation respectively. Our new model takes the display adaptation into account in relation to the scene adaptation based on the retinal model. In computing local adaptation, the use of nonlinear edge preserving bilateral filter presents a better tonal rendition in preserving the local contrast and details while avoiding banding artifacts normally seen in local methods. Finally, we demonstrate the effectiveness of the proposed model by estimating the color difference between the recreated image and the target visual image obtained by trial and error method.

A new method to extract retinal blood vessels from a colour fundus image is described. Digital colour fundus images are contrast enhanced in order to obtain sharp edges. The green bands are selected and transformed to correlation coefficient images by using two sets of Gaussian kernel patches of distinct scales of resolution. Blood vessels are then extracted by means of a new algorithm, directional recursive region growing segmentation or D-RRGS. The segmentation results have been compared with clinically-generated ground truth and evaluated in terms of sensitivity and specificity. The results are encouraging and will be used for further application such as blood vessel diameter measurement.

We propose herein a motion detection artificial vision model which uses analog electronic circuits. The proposed model is comprised of four layers. The first layer is a differentiation circuit of the large CR coefficient, and the second layer is a differentiation circuit of the small CR coefficient. Thus, the speed of the movement object is detected. The third layer is a difference circuit for detecting the movement direction, and the fourth layer is a multiple circuit for detecting pure motion output. When the object moves from left to right the model outputs a positive signal, and when the object moves from right to left the model outputs a negative signal. We first designed a one-dimensional model, which we later enhanced to obtain a two-dimensional model. The model was shown to be capable of detecting a movement object in the image. Using analog electronic circuits, the number of connections decrease and real-time processing becomes feasible. In addition, the proposed model offers excellent fault tolerance. Moreover, the proposed model can be used to detect two or more objects, which is advantageous for detection in an environment in which several objects are moving in multiple directions simultaneously. Thus, the proposed model allows practical, cheap movement sensors to be realized for applications such as the measurement of road traffic volume or counting the number of pedestrians in an area. From a technological viewpoint, the proposed model facilitates clarification of the mechanism of the biomedical vision system, which should enable design and simulation by an analog electric circuit for detecting the movement and speed of objects.

An electroretinogram (ERG) represents the global responses of the retina to a visual stimulus and shows accumulated responses of each layer of the retina relative to the signal processing mechanisms occurring within the retina. Thus, investigating the reaction types of each ERG wave provides information required for diagnosis and for identifying the signal processing mechanisms in the retina. In this study, an ERG signal is generated by simulating the volume conductor field response for each retina layer, which are then summed algebraically. The retina model used for the simulation is Shah's Computer Retina model, which is the most reliable model developed so far. When the generated ERG is compared with a typical clinical ERG it exhibits a close similarity. Based on changing the parameters of the ERG model, a diagnostic investigation is performed with a variation in the ERG waveform.

Computational sensor (smart sensor, vision chip in other words) is a very small integrated system, in which processing and sensing are unified on a single VLSI chip. It is designed for a specific targeted application. Research activities of computational sensor are described in this paper. There have been quite a few proposals and implementations in computational sensors. Firstly, their approaches are summarized from several points of view, such as advantage vs. disadvantage, neural vs. functional, architecture, analog vs. digital, local vs. global processing, imaging vs. processing, new processing paradigms. Then, several examples are introduced which are spatial processings, temporal processings, A/D conversions, programmable computational sensors. Finally, the paper is concluded.

The performance of AC plasma displays has been improved in the area of brightness and contrast, while significant advances in image quality are still required for the HDTV quality. In particular, in full color motion video, motion artifacts and lack of color depth are still visible in some situations. These motional artifacts are mitigated as the number of the subfields increases, usually at the cost of losing brightness or increasing driving circuitry. Therefore, it is still one of our great concerns to find out the optimized subfield configuration through weighting and order of each subfield, and their coding of combination. For evaluation and improvement of motion picture disturbance, we have established a procedure that fully simulates the image quality of displays which utilize the subfield driving scheme. The simulation features virtually located sensor pixels on human retina, eye-tracking sensor windows, and a built-in spatial low pass filter. The model pixelizes the observers retina like a sensor chip in a CCD camera. An eye-tracking sensor window is assigned to every light emission from the display, to calculate the emissions from one to four adjoining pixels along the trajectory of motion. Through this model, a scene from original motion picture without disturbance is transformed into the still image with simulated disturbance. The integration of the light emission from adjoining pixels through the window, also functions as a built-in spatial low pass filter to secure the robust output, considering the MTF of the human eye. Both simulation and actual 42-in-diagonal PDPs showed close results under various conditions, showing that the model is simple, but reasonable. Through the simulation, general properties of the subfield driving scheme for gray scale have been elucidated. For example, a PWM-like coding offers a better performance than an MSB-split coding in many cases. The simulation also exemplifies the motion picture disturbance as a non-linear filter process caused by the dislocation of bit weightings, suggesting that tradeoffs between disturbance and resolution in motion area are mandatory.

This paper described discrete time Cellular Neural Networks (DT-CNN) with two types of neuron circuits for image coding from an analog format to a digital format and their VLSI implementations. The image coding methods proposed in this paper have been investigated for a purpose of transmission of a coded image and restoration again without a large loss of an original image information. Each neuron circuti of a network receives one pixel of an input image, and processes it with binary outputs data fed from neighboring neuron circuits. Parallel dynamics quantization methods have been adopted for image coding methods. They are performed in networks to decide an output binary value of each neuron circuit according to output values of neighboring neuron circuits. Delayed binary outputs of neuron circuits in a neighborhood are directly connected to inputs of a current active neuron circuit. Next state of a network is computed form a current state at some neuron circuits in any time interval. Models of two types of neuron circuits and networks are presented and simulated to confirm an ability of proposed methods. Also, physical layout designs of coding chips have been done to show their possibility of VLSI realizations.

Three dimensional (3-D) optics offers potential advantages to the massively-parallel systems over electronics from the view point of information transfer. The purpose of this paper is to survey some aspects of the 3-D optical interconnection technology for the future massively-parallel computing systems. At first, the state-of-art of the current optoelectronic array devices to build the interconnection networks are described, with emphasis on those based on the semiconductor technology. Next, the principles, basic architectures, several examples of the 3-D optical interconnection systems in neural networks and multiprocessor systems are described. Finally, the issues that are needed to be solved for putting such technology into practical use are summarized.

Threre are two types of nonlinear associative silicon retinas. One is a sparse Hopfield type neural network which is called a H-type retina and the other is its dual network which is called a DH-type retina. The input information sequences of H-type and HD-type retinas are given by nodes and links as voltages and currents respectively. The error correcting capacity (minimum basin of attraction) of H-type and DH-type retinas is decided by the minimum numbers of links of cutset and loop respectively. The operation principle of the regeneration is based on the voltage or current distribution of the neural field. The most important nonlinear operation in the retinas is a dynamic quantization to decide the binary value of each neuron output from the neighbor value. Also, the edge is emphasized by a line-process. The rates of compression of H-type and DH-type retinas used in the simulation are 1/8 and (2/3) (1/8) respectively, where 2/3 and 1/8 mean rates of the structural and binarizational compression respectively. We could have interesting and significant simulation results enough to make a chip.