Nowadays, image processing is widely utilized in many applications and for various purposes. Scholars proposed and suggested various techniques of image processing. The neural network is one of the main processing techniques, which is a state-of-art method. This paper aims to investigate neural network techniques in the field of image processing. Moreover, medical imaging, as well as increasing trends of utilizing digital medical imaging, has gained huge attention in the health sectors. In this regard, this paper focuses on the effect of neural networks in optimizing medical image processing. In this context, the early diagnosis and detection of the eye have an important role in the avoidance of visual impairment, because of the fact that around 45 million people have visual impairments all over the world, according to the World Health Organization. For this reason, the current paper introduces a new method based on image processing for vascular segmentation based on a morphological active contour. Then, segmentation carried out based on morphological operations, fuzzy c-means, and watershed transform. The output of such segmentation methods was given to conventional neural network. The optimized feature values are then extracted. The threshold value is set to compare these optimized feature values. From this, the best segmentation methods will be obtained.

Edge detection plays an important role in image processing, pattern recognition and computer vision applications. Most of edge detection schemes are based on finding maximum in the first derivative of the image function or zero crossings in the second derivative of the image function. Various methods of edge detection for color images, including techniques extended from monochrome edge detection as well as vector space methods are presented. This research presents a comparative study on different methods of edge detection of color images. The methods are based on vector space, color space and numerical methods. Seven different colored images are test in this research. Performance is analyzed depending on Mean Square Error (MSE). The experimental results show that applying vector value (Jacobian method )will create a thick and disconnected edge with all operators Sobel, Prewitt and Log. While the least square method produce edges that are much thicker but continuous. The best performance was found when using YCbCr luminance (Y) and chrominance (Cb and Cr) method, the edges are sharpened, continuous, and not thickness. They are similar with Sobel and Prewitt operators nonetheless with some missing edges while it is better with Log operator.