Other Biomedical Imaging Techniques
375
PROBLEMS
18.1 Read the image in file “p_18_1.jpg” and display the image. The image contains
a SEM image of an endothelial cell.*
a. Choose a convenient seed point in the region of the endothelial cell and use
seed growing to find the outline of the cell.
b. Find the counter of the cell and mark it in the image.
c. Calculate the length of the major and minor axes.
d. Use Fourier descriptors to compress the size of the counter information.
18.2 Co llect the fingerprints of the index finger and thumb of three friends and\or
family members.
a. Orient each of the fingerprints to have greater orientation accuracy.
b. Collect as many features and minutiae on each of the fingerprints.
c. Compare the fingerprints of the individuals comparing one of the main
features and three of the minutiae.
18.3 Read file “p_18_3.jpg” and display the image. Image “p_18_3.jpg” is an electrical impedance image of a human chest. † In this image, the gray scale signifies the relative magnitude of the electrical impedance. Use histogram-based
thresholding to outline the regions of equivalent impedance. Choose a midrange gray-scale level to find the regions that correspond to each other in the
four sections of the chest.
18.4 Read file “p_18_4.jpg” and display. Image “p_18_4.jpg” is a histology slide
of a disease call ragged red fiber, which is a genetic mitochondrial defect
related to the disease called MERRF syndrome (myoclonus epilepsy associated with ragged red fibers). ‡ MERRF syndrome is a muscular disorder that
falls in the category called mitochondrial encephalomyopathies. (Courtesy
of Winston Wiggins, Daisy Ridings and Alicia Roh, Carolinas Medical
Center, Charlotte, NC.)
a. Use a high-boost filter to improve the quality of the image.
b. U se segmentation methods to identify the z-band in the muscle fibers (one
z-band is indicated by a blue arrow).
c. Isolate the muscle cells that have clear normal muscle structure
(well-defined z-bands, etc.) from muscle cell that are deviating in structure. For this, calculate suitable image processing features for each cell
and perform a K-means clustering. Then, identify the cluster that best
represent normal cells.
* Courtesy of Mark G. Clemens, University of North Carolina at Charlotte, Charlotte, NC.
† Courtesy of Dr. Alexander V. Korjenevsky, Institute of Radio-Engineering and Electronics, Russian
Academy of Sciences, Moscow, Russia.
‡ Courtesy of Winston Wiggins, Daisy Ridings and Alicia Roh of Carolinas Medical Center, Charlotte,
NC.
375
PROBLEMS
18.1 Read the image in file “p_18_1.jpg” and display the image. The image contains
a SEM image of an endothelial cell.*
a. Choose a convenient seed point in the region of the endothelial cell and use
seed growing to find the outline of the cell.
b. Find the counter of the cell and mark it in the image.
c. Calculate the length of the major and minor axes.
d. Use Fourier descriptors to compress the size of the counter information.
18.2 Co llect the fingerprints of the index finger and thumb of three friends and\or
family members.
a. Orient each of the fingerprints to have greater orientation accuracy.
b. Collect as many features and minutiae on each of the fingerprints.
c. Compare the fingerprints of the individuals comparing one of the main
features and three of the minutiae.
18.3 Read file “p_18_3.jpg” and display the image. Image “p_18_3.jpg” is an electrical impedance image of a human chest. † In this image, the gray scale signifies the relative magnitude of the electrical impedance. Use histogram-based
thresholding to outline the regions of equivalent impedance. Choose a midrange gray-scale level to find the regions that correspond to each other in the
four sections of the chest.
18.4 Read file “p_18_4.jpg” and display. Image “p_18_4.jpg” is a histology slide
of a disease call ragged red fiber, which is a genetic mitochondrial defect
related to the disease called MERRF syndrome (myoclonus epilepsy associated with ragged red fibers). ‡ MERRF syndrome is a muscular disorder that
falls in the category called mitochondrial encephalomyopathies. (Courtesy
of Winston Wiggins, Daisy Ridings and Alicia Roh, Carolinas Medical
Center, Charlotte, NC.)
a. Use a high-boost filter to improve the quality of the image.
b. U se segmentation methods to identify the z-band in the muscle fibers (one
z-band is indicated by a blue arrow).
c. Isolate the muscle cells that have clear normal muscle structure
(well-defined z-bands, etc.) from muscle cell that are deviating in structure. For this, calculate suitable image processing features for each cell
and perform a K-means clustering. Then, identify the cluster that best
represent normal cells.
* Courtesy of Mark G. Clemens, University of North Carolina at Charlotte, Charlotte, NC.
† Courtesy of Dr. Alexander V. Korjenevsky, Institute of Radio-Engineering and Electronics, Russian
Academy of Sciences, Moscow, Russia.
‡ Courtesy of Winston Wiggins, Daisy Ridings and Alicia Roh of Carolinas Medical Center, Charlotte,
NC.