A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Enhancement of Fundus Retinal Image. <s> Retinal images are routinely acquired and assessed to provide diagnostic evidence for many important diseases, e.g. diabetes or hypertension. Because of the acquisition process, very often these images are non-uniformly illuminated and exhibit local luminosity and contrast variability. This problem may seriously affect the diagnostic process and its outcome, especially if an automatic computer-based procedure is used to derive diagnostic parameters. We propose here a new method to normalize luminosity and contrast in retinal images, both intra- and inter-image. The method is based on the estimation of the luminosity and contrast variability in the background part of the image and the subsequent compensation of this variability in the whole image. The application of this method on 33 fundus images showed an average 19% (max. 45%) reduction of luminosity variability and an average 34% (max. 85%) increment of image contrast, with a remarkable improvement, e.g., over low-pass correction. The proposed image normalization technique will definitely improve automatic fundus images analysis but will also be very useful to eye specialists in their visual examination of retinal images. <s> BIB001 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Enhancement of Fundus Retinal Image. <s> Images of the human retina vary considerably in their appearance depending on the skin pigmentation (amount of melanin) of the subject. Some form of normalisation of colour in retinal images is required for automated analysis of images if good sensitivity and specificity at detecting lesions is to be achieved in populations involving diverse races. Here we describe an approach to colour normalisation by shade-correction intra-image and histogram normalisation inter-image. The colour normalisation is assessed by its effect on the automated detection of microaneurysms in retinal images. It is shown that the Naive Bayes classifier used in microaneurysm detection benefits from the use of features measured over colour normalised images. <s> BIB002
Image enhancement is the procedure to synthesize the digital images for making the output more acceptable to display or for further analysis of that image. Image enhancement is also performed to adjust the contrast and normalize the images. Most often the fundus retinal images do not illuminate uniformly and show local luminosity and irregularity in contrast BIB001 . The fundus retinal images of human have different outlook due to the skin pigmentation of the subject. To gain a good sensitivity and specificity in detecting lesion of fundus retinal images following any automated analytical method, color normalization of that image is a must BIB002 . Some of the popular image enhancement techniques are removing the noise from the image, sharpening the edges of the image, blurring the image, and so on which can be accomplished by spatial domain filtering because spatial domain filtering can directly act on the images and change the pixel values of the image through some specific procedure  . The authors of BIB002 demonstrated a system of color normalization performing intraimage shade-correction interimage histogram normalization and they detected the microaneurysm in fundus retinal images applying the effect of their technique. The authors of BIB001 also proposed a method for normalizing the contrast and luminosity in both intra-and interimages of the human retina and the researchers came out with the average luminosity irregularity reduction of 19% covering maximum 45% and an average improvement of contrast of 34% covering maximum 85%.