Compression of Breast Cancer Images By Principal Component Analysis
Journal: International Journal of Advanced Biological and Biomedical Research (Vol.1, No. 7)Publication Date: 2013-07-01
Authors : Monika Saraswat; A. K. Wadhwani; Manish Dubey;
Page : 767-776
Keywords : SNR; MSE; PSNR; Mammograms; PCA;
Abstract
The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most relevant information of X. These eigenvectors are called principal components [8]. Assume that n images in a set are originally represented in matrix form as Ui∈ Rr ×c, i = 1,......,n, where r and c are, repetitively, the number of rows and columns of the matrix. In vectorized representation (matrix-to-vector alignment) each Ui is a N = r × c- dimensional vector ai computed by sequentially concatenating all of the lines of the matrix Ui. To compute the Principal Components the covariance matrix of U is formed and Eigen values, with the corresponding eigenvectors, are evaluated. The Eigen vectors forms a set of linearly independent vectors, i.e., the base {φ} n i=1 which consist of a new axis system [10]
Other Latest Articles
- The Effect of Water Quality and Irrigation Methods on Moisture and Salinity Distribution of Soil
- Analysis of Heart Rate Variability During Meditative and Non-Meditative State Using Analysis of Variance
- Contraceptive Efficacy of Hydro-Methanolic Fruit Extract of Xylopia Aethiopica in Male Albino Rats
- Estimation of Combining Ability and Gene Action for Agro-Morphological Characters of Rapeseed (Brassica Napus L.) Using Line×Tester Mating Design
- Study of Distribution Pattern and Density of Vegetative Cover in Steppe and Forest Areas of Isfahan University of Technology
Last modified: 2019-07-05 02:37:11