A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Grey-Scale <s> The important criteria used in subjective evaluation of distorted images include the amount of distortion, the type of distortion, and the distribution of error. An ideal image quality measure should, therefore, be able to mimic the human observer. We present a new grayscale image quality measure that can be used as a graphical or a scalar measure to predict the distortion introduced by a wide range of noise sources. Based on singular value decomposition, it reliably measures the distortion not only within a distortion type at different distortion levels, but also across different distortion types. The measure was applied to five test images (airplane, boat, goldhill, Lena, and peppers) using six types of distortion (JPEG, JPEG 2000, Gaussian blur, Gaussian noise, sharpening, and DC-shifting), each with five distortion levels. Its performance is compared with PSNR and two recent measures. <s> BIB001 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Grey-Scale <s> Conversion of a color image into a grayscale image inclusive of salient features is a complicated process. The converted grayscale image may lose contrasts, sharpness, shadow, and structure of the color image. To preserve contrasts, sharpness, shadow, and structure of the color image a new algorithm has proposed. To convert the color image into grayscale image the new algorithm performs RGB approximation, reduction, and addition of chrominance and luminance. The grayscale images generated using the algorithm in the experiment confirms that the algorithm has preserved the salient features of the color image such as contrasts, sharpness, shadow, and image structure. <s> BIB002 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Grey-Scale <s> This paper presents a framework for the detection and measurement of retinal vessels in fundoscopy images. The proposed method segments retinal vessels, enabling the actual measurement of the vessel diameter. A Graphical User Interface (GUI) further supports a number of methods for the automatic and interactive measurement of vessel diameters at any selected point or region of interest. In addition, it provides methods for editing the vessel representation in order to recover from possible segmentation misclassifications. All the above steps are integrated into a clinical application capable of supporting vascular risk stratification in persons with hypertension. The performance of the segmentation is assessed on two publicly available DRIVE and STARE datasets. The experimental results show that the method can achieve a high accuracy on both datasets with very low computational cost, being comparative or slightly better than widely used supervised and unsupervised methods. <s> BIB003
(1) Here I gray is grey-scale image, I color is color image, = 0.2989, =0.5870, =0.1140, (n,m) is pixel location with grey-scale image, and (n,m,c) is a channel at pixel location (n,m) in the color image for channel c in red r, blue b, and green image channel  . There is the possibility of losing the important features of an image like structure, sharpness, contrast, and shadow during the conversion of the color image into grey-scale image. The study of BIB002 proposed an algorithm for converting the RGB image into the grey-scale image which can do RGB approximation, reduction, and addition of luminance and chrominance and sustain the structure, sharpness, contrast, and shadow of the original RGB color image in the resultant image. The study of BIB001 presented a formula based on the Singular Value Decomposition (SVD) to measure the graphical and scaler values of distortion caused by the noise sources. As a step of fundus retinal image processing, the raw RGB color image is converted into grey-scale using the green channel because the blood vessels of retinal microvasculature appear more contrasted through green channel BIB003 . Figure 9 shows the original image (a) and grey-scale image (b) after conversion.