Wavelet Based Segmentation of Hyperspectral Colon Tissue Imagery
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by Kashif M. Rajpoot, Nasir M. Rajpoot
http://www.dcs.warwick.ac.uk/research/improc/pubs/./Rajpoot2003b.PDF
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Abstract:
Abstract Segmentation is an early stage for the automated classification of tissue cells between normal and malignant types. We present an algorithm for unsupervised segmentation of hyperspectral human colon tissue cell images into its constituent parts by exploiting the spatial relationship between these constituent parts. This is done by employing a modification of the conventional wavelet based texture analysis, on the projection of hyperspectral image data in the first principal component direction. Results show that our algorithm is comparable to other more computationally intensive methods which exploit spectral characteristics of the hyperspectral imagery data.
Citations
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| 11 | A Compact Multiresolution Representation: The Wavelet Model – Mallat - 1987 |
| 9 | Signal processing for hyperspectral image exploitation – Shaw, Manolakis - 2002 |
| 7 | Texture Classification Using Discriminant Wavelet Packet Subbands – Rajpoot - 2002 |
| 3 | Hyperspectral colon tissue cell classification – Rajpoot, Rajpoot - 2004 |
| 1 | Principal Component Analysis (PCA), Lecture Notes – Marks |
