Abstract— This paper provides clustered compressive sensing (CCS) based image processing using Bayesian framework applied to medical images. Some images, for example like magnetic resonance images (MRI) are usually very weak due to the presence of noise and due to the weak nature of the signal itself. Compressed sensing (CS) paradigm can be applied in order to boost such signals. We applied CS paradigm via Bayesian framework. Using different sparse prior information and in addition incorporating the special structure that can be found in sparse signal, CCS can be applied to improve image processing. This is shown in the results of this paper. First, we applied our analysis on Angiogram image, then on Shepp-logan phantom and finally on another MRI image. The results show that applying the clustered compressive sensing give better results than the non-clustered version.
Index Terms— Bayesian framework, sparse prior, clustered prior, posterior, compressive sensing, LASSO, clustered LASSO.
S. A. Tesfamicael is with Sør-Trondlag University College (HIST-ALT). He is also with the department of Electronics and Telecommunication (IET) at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway (e-mail: solomont@hist.no, tesfamic@iet.ntnu.no).
F. Barzideh is with the University of Stavanger (UiS) (e-mail: faraz.barzideh@gmail.com).
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Cite: Solomon A. Tesfamicael and Faraz Barzideh , " Clustered Compressive Sensing: Application on Medical Imaging," International Journal of Information and Electronics Engineering vol. 5, no. 1, pp. 46-50, 2015.
