TY - JOUR
ID - 7803
TI - Compression of Breast Cancer Images By Principal Component Analysis
JO - International Journal of Advanced Biological and Biomedical Research
JA - IJABBR
LA - en
SN - 2383-2762
AU - Saraswat, Monika
AU - Wadhwani, A. K.
AU - Dubey, Manish
AD - Electrical, RGPV/MITS, Gwalior, M.P, India
Y1 - 2013
PY - 2013
VL - 1
IS - 7
SP - 767
EP - 776
KW - SNR
KW - MSE
KW - PSNR
KW - Mammograms
KW - PCA
DO -
N2 - 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]
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L1 - http://www.ijabbr.com/article_7803_c1bfed921b1cb6eddb3d3d9a00f75143.pdf
ER -