1a (10 points) Create a blank image of size 512 X 512 using the zeros function (i.e.zeros(512)). Create a square in the image at the intersection of rows 100:125 and columns 150:175, by replacing the elements at those indices with the number 1. Generate the synthetic image using the imagesc function. To make this image look like a medical image, change the colormap to gray, by typing colormap gray Command Window (or your function/script if you are writing one). Take a screenshot of this image. 1b (15 points) Convolve the synthetic image created in 1a with the following two kernels: a 3x3 of ones and a 7x7 of ones. You can create the kernel using the ones function. Use the conv2 function in MATLAB to perform convolution. Again, use imagesc to visualize the output of the convolution with each one of the kernels. Take a screenshot of the image with the 3x3 kernel and the image with the 7x7 kernel. 1c (5 points) Discuss the appearance of the three images. Describe how you could use convolution to quantify the difference between better and poorer performing medical imaging systems. 1a (10 points) Create a blank image of size 512 X 512 using the zeros function (i.e.zeros(512)). Create a square in the image at the intersection of rows 100:125 and columns 150:175, by replacing the elements at those indices with the number 1. Generate the synthetic image using the imagesc function. To make this image look like a medical image, change the colormap to gray, by typing colormap gray Command Window (or your function/script if you are writing one). Take a screenshot of this image. 1b (15 points) Convolve the synthetic image created in 1a with the following two kernels: a 3x3 of ones and a 7x7 of ones. You can create the kernel using the ones function. Use the conv2 function in MATLAB to perform convolution. Again, use imagesc to visualize the output of the convolution with each one of the kernels. Take a screenshot of the image with the 3x3 kernel and the image with the 7x7 kernel. 1c (5 points) Discuss the appearance of the three images. Describe how you could use convolution to quantify the difference between better and poorer performing medical imaging systems