Authors: Dibyadeep Nandi; Amira S. Ashour; Sourav Samanta; Sayan Chakraborty; Mohammed A.M. Salem; Nilanjan Dey
Addresses: Department of CSE, MCKV Institute of Engineering, Howrah, West Bengal, India ' Department of Electronics and Electrical Communications Engg., Faculty of Engineering, Tanta University, Egypt ' Department of CSE, University Institute of Technology, BU, Burdwan, West Bengal, India ' Department of CSE, Bengal College of Engineering, Durgapur, India ' Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt ' Department of CSE, Bengal College of Engineering, Durgapur, India
Abstract: Principal component analysis (PCA) is a mathematical procedure which uses sophisticated mathematical principles to transform a number of correlated variables into a smaller number of variables called principal components. In PCA, the information contained in a set of data is stored with reduced dimensions based on the integral projection of the dataset onto a subspace generated by a system of orthogonal axes. The reduced dimensions computational content is selected so that the significant data characteristics are identified with little information loss. Such a reduction is an advantage in several fields as for image compression, data representation, etc. It can also be widely used for feature extraction, image fusion, image compression, image segmentation, image registration, de-noising, etc. This paper presents a survey of the applications of PCA in the field of medical image processing. In this study, various medical image application-based PCA results are exhibited to prove its efficiency.
Keywords: principal component analysis; PCA; feature extraction; image compression; image fusion; image segmentation; image registration; image de-noising; medical images; image processing.
International Journal of Image Mining, 2015 Vol.1 No.1, pp.65 - 86
Available online: 23 Jun 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article