Selection Criteria of Measurement Matrix for Compressive Sensing Based Medical Image Reconstruction
Ketki C. Pathak1, Jignesh Sarvaiya2, Anand D Darji3, Pooja Panchal4

1Ketki C. Pathak, Sarvajanik College of Engineering and Technology, Surat.

2Jignesh Sarvaiya, G Sardar Vallabbhai National Institute of Technology, Surat.

3Anand D Darji, G Sardar Vallabbhai National Institute of Technology, Surat.

4Pooja Panchal, Gujarat Technological University, Surat.

Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 24 May 2019 | PP: 660-667 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F11320486S319/19©BEIESP

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Abstract: In this article we design a measurement matrix based on compressive sensing for a medical image in order to achieve a low-cost medical image. In Compressive Sensing based reconstruction of an image, number of samples is smaller than conventional Nyquist theorem suggests. In this paper firstly, we apply DWT(Discrete Wavelet Transform)/DCT(Discrete Cosine Transform) transformations on medical image, and then we use Gaussian random matrices, Bernoulli random matrices, Partial orthogonal random matrices, Partial Hadamard matrices, Toeplitz matrices, and QC_LDPC matrices for medical images, respectively. The compressed medical images are reconstructed with different matching pursuit algorithms: OMP (Orthogonal Matching Pursuit), L1 algorithm and GBP (Greedy Basis Pursuit). Using the same amount of measurement, we select the matrix with the best reconstruction as a measurement matrix for medical images. The reconstruction PSNR values, SSIM values, CR values and reconstruction time were used to compare experimental results. The visual quality of reconstructed medical images is of prime importance for medical images. According to the experiment results, the visual quality of reconstructed medical images with OMP matching pursuit and DWT transform is better than other algorithms so that this paper selects Partial Hadamard matrices with DWT transformation and OMP matching pursuit as medical image measurement matrix.

Keywords: Compressive Sensing, Medical Imaging, Measurement Matrices, Recovery Algorithm.
Scope of the Article: Measurement & Performance Analysis