Hyperspectral medical images unmixing for cancer screening based on rotational independent component analysis

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Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8261 LNCS pp. 336 - 343
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Hyperspectral images have shown promising performance in many applications, especially extracting information from remotely sensed geometric images. One obvious advantage is its good ability to reflect the physical meaning from a point view of spectrum, since even two very similar materials would present an obvious difference by a hyperspectral imaging system. Recent work has made great progress on the hyperspectral fluorescence imaging techniques, which makes the elaborate spectral observation of cancer areas possible. Cancer cells would be distinguishable with normal ones when the living body is injected with fluorescence, which helps organs inside the living body emit lights, and then the signals can be obtained by the passive imaging sensor. This paper discusses the ability to screen the cancers by means of hyperspectral bioluminescence images. A rotational independent component analysis method is proposed to solve the problem. Experiments evaluate the superior performance of the proposed ICA-based method to other blind source separation methods: 1) The ICA-based methods do perform well in detect the cancer areas inside the living body; 2) The proposed method presents more accurate cancer areas than other state-of-the-art algorithms. © 2013 Springer-Verlag Berlin Heidelberg.
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