Analysis of Pre-Processing Techniques for Ophthalmic Images
Maria Ali, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture Peshawar, Pakistan.
Abdullah Khan, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture Peshawar, Pakistan.
Muhammad Nouman Atta, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture Peshawar, Pakistan.
Arbab Waseem Abbas, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture Peshawar, Pakistan.
Arshad Khan, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture Peshawar, Pakistan.
Muhammad Asim, Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture Peshawar, Pakistan.
Corresponding Author:
Maria Ali (mariaalikhan515@gmail.com)
Abstract:
Image processing is manipulating digital images to enhance them, reduce their size, or extract useful information from them. This can involve a variety of techniques, such as filtering, segmentation, edge detection, feature extraction, image transformation, and image recognition. Images can often be too large or too small that make difficulties for the image processing algorithms to extract useful information from them. This can lead to poor results, as the algorithm may not be able to effectively process the image. Poor image quality can also lead to poor results, as image recognition algorithms may not be able to extract useful information from a low-quality image. Different image processing algorithms may require different color spaces, and it is important to ensure that the image is in the correct color space for the algorithm to be effective. Therefore, this paper used a data augmentation program that creates multiple variations of a given image. This paper applies different image processing techniques such as color space extracting, rotation, adaptive thresholding, morphological operations, image gradients, canny edge detection, contours, histogram equalization, template matching and color manipulation. This paper manipulates the given image in order to create multiple variations of the same image, which can be used to improve the performance of machine learning algorithms.
Keywords:
Adaptive Threshold; Edge Detection; Template Matching; Rotation; Brightness; Color Manipulation