Is there anybody who knows how to do this? This is an assignment for my Bio-imag

Question

Is there anybody who knows how to do this? This is an assignment for my Bio-imaging course and I really need help! Here's how it works:

1):

Write a MATLAB code to load the image, "Image_Foot_x-ray.jpg" and answer the following questions:

- Find two zones of relatively consistent image brightness, one in the tissue area and one in the bone area, and use MATLAB functions to calculate the image Signal-to-noise ratio (SNR) and Contrast-to-noise ratio (CNR).

- If the length of the larger screw in real-world units is 38mm, what is the spatial resolution of the image?

2):

Consider the following two ideal representations of X ray images ? one was taken from the side of an object and one from the front. The numbers represent the intensity of the image captured. We have been told that the object is a thin-walled square box made of paper surrounded by air, and that the object contains water, in the middle of which there is a square rod of unknown material stretching from top to bottom.

What is the attenuation coefficient of the material that the rod is made of?

Explanation / Answer

All SRR images were reconstructed using the IBP approach proposed by Irani and Peleg [5]. IBP was chosen because it has been widely used for super resolution reconstruction in the past and because of its easy implementation. A flowchart illustrating the IBP approach is provided in Figure 1. Initially, an HR image  is approximated from the average of multiple LR images  that have been geometrically transformed, , to the same orientation prior to averaging. A new set of LR images , are obtained by simulating the imaging process (blurring , and down sampling) in the predicted HR image . For our case, a 1D Gaussian kernel with a FWHM equal to the LR slice thickness was used along the slice-select direction in the HR image for blurring because it closely matched the excitation profile used in the original image acquisition sequence. If the predicted HR image  is the same as the true HR image , then the simulated LR images  should be equal to the observed LR images . Therefore, the difference between the observed and simulated LR images  is upsampled and backprojected on to  using linear interpolation. This results in an updated HR image  that can be downsampled and the simulated LR images  are compared to the observed LR images . These steps are iteratively repeated till the maximum error at the th iteration according to  is less than a preset threshold. All SRR software was developed using Matlab v.2009

Measurement of SNR and CNR

Axial images were used to determine trabecular bone/marrow space SNR, and coronal images were used to determine cartilage and muscle SNR. SNR was calculated using the

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