@article {
author = {Saraswat, Monika and Wadhwani, A. K. and Dubey, Manish},
title = {Compression of Breast Cancer Images By Principal Component Analysis},
journal = {International Journal of Advanced Biological and Biomedical Research},
volume = {1},
number = {7},
pages = {767-776},
year = {2013},
publisher = {Sami Publishing Company},
issn = {2383-2762},
eissn = {2322-4827},
doi = {},
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]},
keywords = {SNR,MSE,PSNR,Mammograms,PCA},
url = {https://www.ijabbr.com/article_7803.html},
eprint = {https://www.ijabbr.com/article_7803_c1bfed921b1cb6eddb3d3d9a00f75143.pdf}
}