Digital Image Processing

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Comparative Analysis of PCA, SPIHT and Haar Methods in Medical Image Compression


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1 F.E.T, SSTC, SSGI, Junwani, Bhilai, India
     

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Compression of medical image has acquired great attention attributable to its raising need to decrease the picture size while not compromising the diagnostically crucial medical data exhibited on the picture. PCA algorithmmay be used to help in image compression. Here PCA algorithm is characterized in two forms i.e. Standard PCA and Block-Based PCA. The block based PCA has 2 extended-PCA algorithms that manipulate the block data of the image are evaluated. The 1st algorithm is referred to as block-by-block PCA wherestandard PCA algorithm is utilized on every block of the picture. In the next algorithm- the block-to-row PCA, all of block data are initially concatenated into a row before the standard PCA algorithm is thereforeutilizedin the remodelled matrix. In this paper, the block based PCA and SPIHT primarily applied on the ROI region whereas General PCA and HAAR wavelet were applied to non-ROI region. An arbitrary shaped segmentation (Manual segmentation) is employed to trace the specified ROI on the image.The SPIHT is being compared with the block based PCA methods in terms of image quality and compression ratio while selecting either general PCA or HAAR wavelet on Non ROI. With this work, it’s observed that block-based PCA performs superior to the SPIHTwith regards toimage quality, producingsimilar compression ratio.


Keywords

Medical Image Compression, Principal Component Analysis (PCA), Block-Based PCA, Compression Ratio, Image Quality, HAAR, SPIHT.
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  • Comparative Analysis of PCA, SPIHT and Haar Methods in Medical Image Compression

Abstract Views: 243  |  PDF Views: 4

Authors

Chandrashekhar Kamargaonkar
F.E.T, SSTC, SSGI, Junwani, Bhilai, India
Monisha Sharma
F.E.T, SSTC, SSGI, Junwani, Bhilai, India

Abstract


Compression of medical image has acquired great attention attributable to its raising need to decrease the picture size while not compromising the diagnostically crucial medical data exhibited on the picture. PCA algorithmmay be used to help in image compression. Here PCA algorithm is characterized in two forms i.e. Standard PCA and Block-Based PCA. The block based PCA has 2 extended-PCA algorithms that manipulate the block data of the image are evaluated. The 1st algorithm is referred to as block-by-block PCA wherestandard PCA algorithm is utilized on every block of the picture. In the next algorithm- the block-to-row PCA, all of block data are initially concatenated into a row before the standard PCA algorithm is thereforeutilizedin the remodelled matrix. In this paper, the block based PCA and SPIHT primarily applied on the ROI region whereas General PCA and HAAR wavelet were applied to non-ROI region. An arbitrary shaped segmentation (Manual segmentation) is employed to trace the specified ROI on the image.The SPIHT is being compared with the block based PCA methods in terms of image quality and compression ratio while selecting either general PCA or HAAR wavelet on Non ROI. With this work, it’s observed that block-based PCA performs superior to the SPIHTwith regards toimage quality, producingsimilar compression ratio.


Keywords


Medical Image Compression, Principal Component Analysis (PCA), Block-Based PCA, Compression Ratio, Image Quality, HAAR, SPIHT.

References