Image Processing Diagnostics: Emphysema Alex McKenzie Metropolitan State College of Denver

Introduction Emphysema is a disease of the lungs which causes damage to the air sacs at the end of air passages (alveoli ). – As the disease progresses, the alveoli begin to break down and the disease destroys the walls of the lung which causes large holes to form. Currently several different methods are being used to aid in the diagnosis of emphysema, but it is difficult to quantify the degree of the disease with these tests.

Traditional Diagnosis Methods Arterial blood-gas analysis: – By testing arterial blood, it can be determined how well lungs process oxygen and carbon dioxide. Pulse oximetry: – The oxygenation of the blood can be determined by using an oximeter, which is also used to determine lung function. Chest X-ray: – X-rays can be used to verify that a patient has emphysema, but cannot accurately determine the severity.

Traditional Diagnosis Methods CT Scan: – CT scans can be used to detect emphysema, but other tests are required to measure more accurately the amount of affected tissue, which looks simply like dark spots on the lung. In order to make these measurements, radiodensities in air passages of the lung are analyzed (measured in Hounsfield units).

Traditional Diagnosis Methods CT Scan: – Hounsfield units (HU) are measurements of volumetric pixels (voxels) which are based on the radiodensity of distilled water at STP, which is defined as 0 HU. The Hounsfield scale goes from to 3172 with air being ≤ HU and dense objects like bone being ≥ 400 HU. The Hounsfield scale in images is represented by 4096 shades of gray, with pure black being HU and 3172 being pure white.

Goals Currently CT scans do not quantify the degree of emphysema in a patient well enough on their own, and other tests are necessary to determine the severity of an individual case. Our goal is to create a Java plug-in which will interface with an open source imaging processing program called ImageJ. – This plug-in will automate the process of collecting data and performing statistical calculations on several CT scans, which can then be used to create a scale that will determine severity.

Data We received CT scans and diagnostic data for five patients with varying degrees of severity. – For each patient, the data we received included Hounsfield unit analysis of: Mean Standard Deviation Tissue ≤ -950 HU (%) Tissue ≤ -910 HU (%) For consistency, we sampled five sets of ten images for each patient and applied our method.

Method Our method involves examining a selection taken from ten random CT images spaced throughout the lung. – From this selection, we looked at the pixel value of which represent color of pixel; 0 being black, 255 being white. – Once we made our selection we set a cutoff point to remove the lighter air passages (values ≥ 30). – We then looked at a histogram to determine the deviation from a normal Gaussian distribution to determine the skewness.

Skewness Skewness is a measure of asymmetry for a normal Gaussian distribution of a variable. – For a sample of n values the sample skewness is defined as: A normal Gaussian distribution has a skewness of zero, which means it is perfectly symmetric.

Skewness Below is shown a histogram of pixel value versus count - the darker pixel values indicate severity of emphysema. The portion to the right of the red line shows the light pixel values we ignored, as they distorted our true skewness value.

Consistency Challenges Our CT scans are sets of 500 to 700 images that are 0.75 mm slices of the chests of several different patients. Due to unique body structures these images can vary drastically from patient to patient, and even in the same patient. Some of these challenges include: – Body size – Lung size – Selected area – View obstruction by other organs

CT scan image of a lung. Light spots are air passageways. Red circles indicate some areas affected by emphysema. Unusable CT scan image nearing bottom of the lung, other organs are beginning to get in the way.

Method Verification To verify our method, we repeated our calculations several times with different images. – By taking multiple measurements, we were able to determine our method’s relative standard deviation. We determined the relative standard deviations of the data using the traditional Hounsfield unit analysis and our system for comparison purposes. – This allowed us to measure the sensitivities of our the different methods.

Results Our data shows a standard deviation of 10.6% between patients, with a 1.45% error between the left and right lung.

Results Sample analyses of one patient showing the five image sets with the different analysis techniques, as well as the relative standard deviations.

Results Our results show that our method of analyzing skewness appears to be more sensitive than looking at HUs. – Patient 1 sample data: Skewness Method: 1-2.5% standard deviation HU Method: 22+% standard deviation These results appear to be consistent and reliable, with similar numbers occurring for each patient we examined.

Next Steps Our results appear promising, and we are currently working to get more CT scan data to work with so we have a larger sample size. – For this next series of data, we plan to calculate the severity blind – without using or comparing our results to the HU method. – After we analyze our results, we will present our findings to our collaborators and see if our data matches theirs. Once we have a larger sample size we hope to create a scale which can be used to quantify the degree of the disease.

References Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, "Measures of Skewness and Kurtosis." NIST ITL home page. 23 Apr Uppaluri, Renuka, Theophano Mitsa, Milan Sonka, Eric A. Hoffman, and Geoffrey McLennan. "Quantification of Pulmonary Emphysema from Lung Computed Tomography Images." Am. J. Respir. Crit. Care Med (1997): “Emphysema: Tests and diagnosis.” Mayo Clinic. 29 April sts-and-diagnosis

Acknowledgements Dr. Alberto Sadun, University of Colorado, Denver Dr. John Newell, MD, National Jewish Health

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