d. Apply the mask for detection of falling lines with the angle of −45° line to
highlight these linear objects and show the resulting image.
e. Explain what object(s) in the image each of the masks was able to extract or
highlight the best. Which mask was the most useful mask for this image?
4.3 In description of the Laplacian of Gaussian edge detection method, we use two
subsystems, i.e., Laplacian and Gaussian smoothing in series. Combine the two
subsystems to describe the entire edge detection method with one mathematical expression. (Hint: Apply the Laplacian operator on the Gaussian smoothing
function.)
4.4 Load the image in the file “p_4_4.mat” and show the image. This image represents the 2-D representation of the 3-D reconstruction of multislice tomographic
image of a large part of the cardiovascular system. Repeat all steps of Problem
4.2 for this image.
4.5 L oad the image in the file “p_4_5.mat” and show the image. This is a photographic image of the heart in which different objects such as arteries and veins
of the heart are shown. Note the areas marked as SVC, AAO, PV, RB, RBA,
LB, PA, DAO, and LPA. For each of these regions, find the coordinates of a seed
point inside the region that well represents the region. Then, design a suitable
similarity criterion based on the gray-level ranges of the regions mentioned.
Using the seeds and the similarity criterion, perform seed growing to segment
the image. Discuss the results.
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Biomedical Signal and Image Processing