Restoring Tumorous MRI Brain Images Yadavan Varatharajah Summer Ventures 2007 Appalachian State University
Abstract Brain tumors are caused by many environmental, lifestyle, and genetic factors. This project will cover what a brain tumor is, general information about brain tumors, the different tests to detect brain tumors, and how to improve the image, so that the tumor portion of the brain can be clearly visible. Another significant part of this project that will be covered is image restoration. We will be using multiple computer programs, such as MATLAB and ImageJ, to improve images and restore degraded images back to their original form. In order to show how image restoration works, we will corrupt images by using MATLAB and then multiply the image and use formulas to blur the image. Another way to corrupt an image is by using ImageJ, and using Gaussian Blur or another filter such as convolution and finding edges to help distort the image. In this report, the MRI images of the brain tumor will be labeled in most figures, so it can be clearly visible.
Introduction Brain tumors are intracranial tumors that are created by abnormal and uncontrolled cell division, normally either in the brain itself, cranial nerves, brain envelopes, skull, pituitary, and pineal gland. Though brain tumors can be identified in any area of the brain, most tumors are usually located in two different locations, depending on age. Brain tumors are usually identified in the posterior cranal fossa in children and in the anterior two-thirds of the cerebral hemispheres in adults [1]. Although brain tumors can occur at any age, studies have shown that they are most common in two age groups. The first age group is children three to twelve years old, and the other is adults from forty to seventy years old. Studies show that some types of brain tumors are more frequent among workers in certain industries, such as drug and rubber manufacturing, and oil refining. Also, other recent studies have shown that embalmers and chemists have a high incidence of brain tumors. [2] Each year more than sixteen thousand people find out that they have a brain tumor. Tumors are all either benign or malignant. Benign tumors do not contain cancer cells and can usually be removed, and are not likely to recur. Benign brain tumors have clear borders, and though they do not invade nearby tissues, they can press on sensitive areas of the brain and cause symptoms. Unlike benign tumors, malignant tumors contain cancer cells. They interfere with vital signs and are therefore life-threatening. However, malignant brain tumors rarely spread from a person s brain and are likely to grow rapidly and crowd or invade the tissue around them. [2] To come to a diagnosis, specialized tests are used such as CT, MRI, and PET scans. Computer Tomography (CT) scans use special X-ray equipment, which obtain
images from different angles, and a computer that assembles them to create a detailed cross-sectional picture of the body s tissues. These pictures are called slices, which provide more detailed information on brain tumors than conventional X-ray films. [3] But unlike conventional X-rays and CT scans, MRI does not use radiation. Instead, radio waves and a strong magnetic field are used to produce clear and detailed pictures of internal organs. The radio waves are directed at protons. In a setting of a strong magnetic field, the protons become excited and then relaxed, which emit radio signals, and then they are computer-processed to form an image. MRI images show differences in the water content in various body tissues due to protons being abundant in the hydrogen atoms of water. Different types of tissue within the same organ, such as the white and gray matter of the brain, can be distinguished. Small tumors, tumors next to the bone, brain stem tumors, and low grade tumors are often best imaged by MRI. [3] A PET scan provides a picture of brain activity, rather than structure, by measuring levels of injected glucose labeled with a radioactive tracer. After a low dose of radioactive glucose is injected, a scanner rotates around the patient s head and detects the amount of radioactivity in different parts of the brain. Different types of tissue and organ functions are displayed using the different degrees of brightness and color. A good example of this is cancerous tissue which consumes more glucose than healthy tissue, so it appears brighter when scanned. A PET scan is not regularly used for diagnosing tumors, but is used with other scans to determine the grade of the tumor and to distinguish between scar tissue and necrosis. [3] Image restoration is the ability to restore a corrupted or disturbed image back to its original form. The goal of image restoration is to take a corrupted image and to restore
the image back to better quality, or closer to the original. MATLAB and ImageJ are helpful tools for image restoration. Restoration can be helpful for all images, no matter how much it is corrupted and is used by medical imagers to better see anomalies in MRI or X-ray images. In ImageJ a few filters such as, Gaussian blur, convolution, and finding edge will be useful tools in corrupting the brain images. In order to better present image restoration all images will be manually corrupted, so that the picture will be restored to the closest clarity. Methods Images for the research were gathered from the internet. ImageJ and MATLAB, were the two programs used to alter and then restore the images. The first image represents a brain tumor on the right side of the brain: Figure 1- Uncorrupted image of brain tumor1 [4]
Figure 1 was blurred, and symmetry was used to fill in the border as well as being smoothed using the Gaussian Blur. The commands that were used in MATLAB are present in Figure 2. PSF=fspecial( gaussian,7,10; Blurred=imfilter(f,PSF, symmetric, conv ); figure;imshow(blurred);title( Blurred Image ); Figure 2 Figure 3- Blurred image of brain tumor1 [4] Figure 3 is a blurred image, but will be removed by using a blind filter. A blind filter is used when the exact Point Spread Function (PSF) is not known. When you use maximum likelihood estimation, you will find the PSF, and therefore this will estimate random corruption. Now we will remove the blur by using the commands seen in Figure 4.
INITPSF=ones(size(PSF)); [f P]=deconvblind(Blurred, INITPSF,30); figure;imshow(p,[], notruesize ); figure;imshow(f);title( Restored Image ); Figure 4 Figure 5- Restored image of brain tumor1 [4] Figure 5 is the new filtered image that was restored, after we sent the command seen in Figure4. The restored image came out as close as possible to the uncorrupted image, and there was a significant change from the corrupted image to the restored image, and we could easily distinguish the tumor. Now another image will be done similarly, but in color. This image is shown uncorrupted in Figure 6. The tumor is easily identified by the bright yellow circular portion, on the right side of the brain:
Figure 6- Uncorrupted image of brain tumor2 [5] As seen here in Figure 6 the image can easily be seen, and is colorful. The tumor portion is in the axial section of the human brain. As seen in the prior experiment, the procedure worked perfectly, and we restored the image effectively. The commands used in the grey0-scale image are also going to be the same ones used to corrupt and restore this color image. The commands that will be used to corrupt this image will be presented in figure 7. PSF=fspecial( gaussian,7,10); Blurred=imfilter(f,PSF, symmetric, conv ); figure;imshow(blurred);title( Blurred Image ); Figure 7
Figure 8- Blurred image of brain tumor2 [5] In Figure 8 the image is blurred, and is very bright, because it is in color. However, you can not see the minor details that were in the uncorrupted image. Just like the black and white image, we will be using the blind filter, to remove the blur. There might be some differences between grey-scale and color images, so that will be one of the most important aspects we will be looking at in this study. To restore the image, we will be using the same commands used in Figure 4. INITPSF=ones(size(PSF)); [f P]=deconvblind(Blurred, INITPSF,30); figure;imshow(p,[], notruesize ); figure;imshow(f);title( Restored Image ); Figure 9
Figure 10- Restored Image of brain tumor2 [5] Figure 10 is the new filtered image that was restored after we sent the command seen in Figure9. The restored image was just like the image in Figure6 which was uncorrupted. The blur is gone, so you can easily see the tumor again. You could identify the tumor even with the blur, because it was in color. Luckily, brain tumors are easy to distinguish in MRI images. Conclusions The results show that any corruption to the original image can be fully restored. This proves that image restoration can be a powerful tool used by law enforcement agencies worldwide as well as other fields, like medical technologies. Image restoration takes a long time to do, because it takes many trials to find the best PSF to restore the image to the fullest. In order to correctly restore an image it should go through many filters because the amount of corruption is unknown and the image is going through a blind filter.
References 1. Chamberlain MC, Kormanik PA. Practical guidelines for the treatment of malignant gliomas CA Cancer J Clin 2000;http://www.answers.com/topic/brain-tumor- 1?cat=health 2. Medicine Net, Brain Tumors 16, April 2002 http://www.medicinenet.com/brain_tumor/article.htm 3. Gerson, Sam. The Diagnosis of Brain Tumors Brain Tumor Society. 2004. http://www.tbts.org/itemdetail.asp?categoryid=366&itemid=16383 4. Sgambati, Maria. The Road to Tenure: Measures of Progress. National Cancer Institute. July 2003.http://dceg.cancer.gov/newsletter/jul03/brainMRI.jpg 5. Burt M. Alvin. Brain. Brain Forum. 2007. http://www.biologyreference.com/images/biol_01_img0057.jpg