Diabetic retinopathy is a major cause of blindness. Earliest signs of diabetic retinopathy are damage to blood vessels in the eye and then the formation of lesions in the retina. This paper presents an automated method for the detection of bright lesions (exudates) in retinal images. In this work, an adaptive thresholding based on a novel algorithm for pure splitting of the image is proposed. A coarse segmentation based on the calculation of a local variation for all image pixels is used to outline the boundaries of all candidates which have clear borders. A morphological operation is used to refine the adaptive thresholding results based on the coarse segmentation results. Using a clinician reference standard (ground truth), images with exudates were detected with 91.2% sensitivity, 99.3% specificity, and 99.5% accuracy. Due to its results the proposed method can achieve superior performance compared to existing techniques and is robust to image quality variability.

Earliest signs of diabetic retinopathy, the major cause of vision loss, are damage to the blood vessels and the formation of lesions in the retina. Early detection of diabetic retinopathy is essential for the prevention of blindness. In this paper we present a computer-aided system to automatically identify red lesions from retinal fundus photographs. After pre-processing, a morphological technique was used to segment red lesion candidates from the background and other retinal structures. Then a rule-based classifier was used to discriminate actual red lesions from artifacts. A novel method for blood vessel detection is also proposed to refine the detection of red lesions. For a standarised test set of 219 images, the proposed method can detect red lesions with a sensitivity of 89.7% and a specificity of 98.6% (at lesion level). The performance of the proposed method shows considerable promise for detection of red lesions as well as other types of lesions.

Diabetic retinopathy is a major cause of blindness, and its earliest signs include damage to the blood vessels and the formation of lesions in the retina. Automated detection and grading of hard exudates from the color fundus image is a critical step in the automated screening system for diabetic retinopathy. We propose novel methods for the detection and grading of hard exudates and the main retinal structures. For exudate detection, a novel approach based on coarse-to-fine strategy and a new image-splitting method are proposed with overall sensitivity of 93.2% and positive predictive value of 83.7% at the pixel level. The average sensitivity of the blood vessel detection is 85%, and the success rate of fovea localization is 100%. For exudate grading, a polar fovea coordinate system is adopted in accordance with medical criteria. Because of its competitive performance and ability to deal efficiently with images of variable quality, the proposed technique offers promising and efficient performance as part of an automated screening system for diabetic retinopathy.

In the field of ophthalmology, retinal image analysis is crucial to extract many details that help doctors identifying retinal diseases at early stages, such details are the blood vessels, optic disk, and fovea. The exponential increase in number of diabetic retinopathy patients necessitates the use of computers to help doctors to diagnose and treat retinal diseases of the human eyes. Computer vision effectively helps doctors by analyzing and treating human retinas. In the field of ophthalmology, a color image (RGB image) of the eye fundus is captured by ophthalmoscopy. In this work, an automatic technique for extraction of human retinal blood vessels is proposed. The proposed method is based on three main stages, namely image pre-processing, initial segmentation of blood vessels and image post-processing. In the first stage, the contrast-limited adaptive histogram equalization is applied to the green component image to improve its contrast, and then it is remerged again with the red and blue components. In the second stage, the blood vessels are extracted using mean-C thresholding. Finally, in the third stage, many morphological operations are used to refine the segmented blood vessels image. The proposed method is validated using the expert ground truths with the DRIVE dataset in terms of pixels, and experimental results show sensitivity, specificity, accuracy and positive predictive value of 0.770816, 0.977575, 0.959993 and 0.7611835, respectively. The performance measures are compared with many recent related works and found to outperform most of them. The superior performance of this method proves that it is promising for mass screening of human fundus images.

For some disabled people, Electroencephalogram (EEG) signals are used to interpret brain thinking to drive machines by creating interface between the human brain and such machines. EEG signals are naturally varied due to human thinking process, and can be manipulated to drive a wheelchair based DC motors in real-time without any muscular efforts. In this paper, EEG signals are used to control DC motors using a Brain Computer Interface (BCI) that includes an EEG sensor headset to capture brain signals. The extracted EEG signals are considered as reference signals and transmitted to a microcontroller via Bluetooth. An intelligent wheelchair (IW) with an EEG sensors is connected to an Arduino, that drives two DC motors, to control movement references to the specific EEG signals. For the proposed IW based EEG, life cycle cost (LCC), over 5 year lifetime, is about 2674$ compared with a manufactured passive wheelchair, which its LCC is 3957$. The experimental tests suggest that the proposed design of IW is efficient and low cost as well as allowing disabled people to more easily control their wheelchairs and to lead independent lives.