CONTRAST ENHANCEMENT FOR PCA FUSION OF MEDICAL IMAGES

Abstract

: Image Fusion is one of the major research fields in image processing. Image fusion process can be defined as the integration of information from a number of registered images without the introduction of distortion. It is often not possible to get an image that contains all relevant objects in focus. One way to overcome this problem is image fusion, in which one can acquire a series of pictures with different focus settings and fuse them to produce an image with extended depth of field which helps in clinical diagnosis. Image fusion techniques can improve the quality and increase the application of these data. The proposed paper uses multi-image Contrast enhancement for PCA fusion of medical images. The objective of this paper is to propose a technique for fusion of human brain MRI images based on Principal Component Analysis and to improve the visibility of medical images by applying contrast enhancement existing techniques. The PCA fusion technique adopted here improve resolution of the images. The PCA algorithm builds a fused image of several input images as a weighted superposition of all input images. The resulting images contains enhanced information as compared to individual images and also apply Contrast Enhancement technique to improve visibility of medical image details without introducing unrealistic visual appearances and/or unwanted artefacts. It also gives the quality comparison study of original medical images before fusion, after applying PCA and various existing techniques for contrast enhancement for those medical images.

Keywords: Adaptive Histogram Equalization, Contrast Enhancement, Histogram Equalization, Image fusion, Principal Component Analysis