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K. Gayathri
C.Abdul Hakeem College of Engineering and Technology
Computer Science & IT Electrical & Electronics Engineering

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An Optimal HSI Image Compression using DWT and CP D. Narmadha; K. Gayathri; K. Thilagavathi; N.Sardar Basha
International Journal of Electrical and Computer Engineering (IJECE) Vol 4, No 3: June 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (598.375 KB)

Abstract

The compression of hyperspectral images (HSIs) has recently become a very attractive issue for remote sensing applications because of their volumetric data. An efficient method for hyperspectral image compression is presented. The proposed algorithm, based on Discrete Wavelet Transform and CANDECOM/PARAFAC (DWT-CP), exploits both the spectral and the spatial information in the images. The core idea behind our proposed technique is to apply CP on the DWT coefficients of spectral bands of HSIs. We use DWT to effectively separate HSIs into different sub-images and CP to efficiently compact the energy of sub-images. We evaluate the effect of the proposed method on real HSIs and also compare the results with the well-known compression methods. The obtained results show a better performance when comparing with the existing method PCA with JPEG 2000 and 3D SPECK.DOI:http://dx.doi.org/10.11591/ijece.v4i3.6326 
Retinal Blood Vessels Extraction Based on Curvelet Transform and by Combining Bothat and Tophat Morphology K. Gayathri; D. Narmadha; K. Thilagavathi; K. Pavithra; M. Pradeepa
International Journal of Electrical and Computer Engineering (IJECE) Vol 4, No 3: June 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (728.043 KB)

Abstract

Retinal image contains vital information about the health of the sensory part of the visual system. Extracting these features is the first and most important step to analysis of retinal images for various applications of medical or human recognition. The proposed method consists of preprocessing, contrast enhancement and blood vessels extraction stages. In preprocessing, since the green channel from the coloured retinal images has the highest contrast between the subbands so the green component is selected. To uniform the brightness of image adaptive histogram equalization is used since it provides an image with a uniformed, darker background and brighter grey level of the blood vessels. Furthermore Curvelet transforms is used to enhance the contrast of an image by highlighting its edges in various scales and directions. Eventually the combination of Bothat and Tophat morpholological function followed by local thresholding is provided to classify the blood vessels. Hence the retinal blood vessels are separated from the background image.DOI:http://dx.doi.org/10.11591/ijece.v4i3.6327
Co-Authors D. Narmadha K. Pavithra K. Thilagavathi M. Pradeepa N.Sardar Basha