Computer Assisted Detection:
Computer Assisted Detection:
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Breast cancer is one of the most devastating and deadly diseases for women - Computer Assisted Detection: introduction. Though there are many techniques used in detecting cancer, mammography is the screening test used in the battle against breast cancer. It has been found that early detection of cancer decreases the mortality rate. However, it has also been reported that up to 30% of breast lesions go undetected in screening mammograms. The clinical significance of early diagnosis and the difficulty of the diagnostic task have generated a tremendous interest in developing computer-assisted detection CAD schemes for mammographic interpretation. Several studies have demonstrated that CAD technology has a positive impact on early breast cancer detection. The CAD schemes are increasing the accuracy of interpretation of digital mammograms and breast ultrasound images. These programs provide further penetrative vision for doctors looking at mammograms and other breast scans and are showing great promise in the detection of breast cancer. Moreover, they also help in distinguishing breast cancer from benign problems without a biopsy and in tracking changes in a woman’s breast over time (Gavin, 2003). According to the report published by the University of Michigan Health System, a CAD system improved the ability of highly experienced radiologists to tell cancerous tumors from benign growths on ultrasound breast scans. Such scans are often performed after a suspicious finding on a screening mammogram, to help determine if a biopsy is needed. Based on clinical studies of the CAD technology, researchers estimate that for every 100,000 breast cancers currently detected with screening mammograms, the CAD technology could result in the detection of an additional 20,500 breast cancers.
How CAD works:
While using CAD technology, mammogram films are first loaded into a special processing unit that digitizes the mammogram images. The CAD unit then highlights any detected breast abnormalities on the digitized mammograms using special pattern recognition computer software. The digitized mammogram files are then transmitted to monitors on a motorized film viewer so the radiologist can compare the original film to the digitized mammogram image on the small monitor. In the meantime, the radiologist reviews the patient’s original mammogram films and makes his or her interpretation as to whether any breast abnormalities are present. After the radiologist finishes analyzing the mammogram films, he or she can view the digitized mammograms on the small monitor to determine whether the computer marked any abnormalities on the films. Based on the results of the CAD marker information, the radiologist may choose to re-examine the original mammogram films and modify his or her interpretation when appropriate (Gavin, 2003). Using sophisticated pattern recognition computer software, the CAD technology is designed to detect the following abnormalities on mammogram films:
· Patterns of bright spots that suggest microcalcifications (tiny calcium deposits that may indicate cancer)
· Dense regions with or without radiating lines that suggest breast masses or distortions
The CAD technology marks breast abnormalities on digitized mammography films using a special coding system. For example, the R2 Image checker marks clusters of calcifications with a small triangle and breast masses with an asterisk. CAD marks are only made on the digitized mammograms; the original films are not altered.
Different Types of Cad Schemes:
Many different types of CAD schemes are being developed for detection and/or characterization of various lesions in medical imaging, including conventional projection radiography, computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound. Organs currently being subjected to research for CAD includes the breast, chest, colon, brain, liver, kidney, and the vascular and skeletal systems.
Computed tomography is a medical imaging method where digital geometry processing is used to generate a three dimensional image of the internals of an object from a large series of two dimensional X-ray images taken around a single axis of rotation. Since its introduction in the 1970s, CT has become an important tool in medical imaging to supplement X-rays and medical ultrasonography. Although it is still quite expensive, it is the gold standard in the diagnosis of a large number of different disease entities such as cerebrovascular accidents and intracranial hemorrhage, lung parenchyma, cardiac diseases, renal/urinary stones, appendicitis, pancreatitis, etc.
MRI or magnetic resonance imaging is a method used to visualize the inside of living organisms. It is primarily used to demonstrate pathological or other physiological alterations of living tissues and is a commonly used form of medical imaging. MRI has also found many novel applications outside of the medical and biological fields such as rock permeability to hydrocarbons and certain non-destructive testing methods such as produce and timber quality characterization (Wikipedia, 2006).
New Applications of CAD:
Computer assisted detection (CAD) is a rapidly growing field with applications in a growing number of diseases, modalities, and anatomies. Academic and industrial research groups worldwide are proposing and publishing new approaches, techniques, and paradigms at an ever-increasing rate. According to a study titled Computer-Aided Detection of Polyps in a Colon Phantom: Effect of Scan Orientation, Polyp Size, Collimation, and Dose by Ling et al (2002), it has been concluded that computer assisted detection can help in detecting clinically significant 10-mm polyps with 100% sensitivity in all orientations, doses, collimations and modes.
Doi’s report titled “Current status and future potential of computer-aided diagnosis in medical imaging” (2005) says that many CAD schemes can be used in the field of medical imaging. These schemes include: detection and classification of lung nodules on digital chest radiographs; detection of nodules in low dose CT; distinction between benign and malignant nodules on high resolution CT; usefulness of similar images for distinction between benign and malignant lesions; quantitative analysis of diffuse lung diseases on high resolution CT; and detection of intracranial aneurysms in magnetic resonance angiography. The paper further concludes that CAD will have a major impact on medical imaging and diagnostic radiology in the 21st century because CAD can be applied to all imaging modalities, all body parts and all kinds of examinations (Doi, 2005).
· The Texas study, which was presented at the annual Radiological Society of North America (RSNA) meeting in Chicago on November 28, found that using CAD technology can increase the detection of breast cancer by approximately 20%. (Imaginis, 2000).
· According to Timothy W. Freer, MD, director of Women’s Diagnostic and Breast Health Center in Plano, Texas, detecting breast cancer is difficult and often involves identifying subtle abnormalities on mammogram films. However, in the study, the eight breast cancers detected with the CAD technology were in very early stages and were easily treatable (Freer, 2005).
· According to R2 Technology, Inc, maker of a computer-aided detection (CAD) system called the R2 Imagechecker, for every 100,000 cancers currently detected by screening mammography, the use of their CAD system could result in an additional 20,500 breast cancers being detected each year (Imaginis, 2006).
Thus we find that computer aided detection has revolutionized the process of medical imaging. The CAD technology is helping doctors and radiologists to detect, diagnose and understand various diseases in a better manner. The scope and range of application of CAD is being widely explored in the realm of medical imaging. The time is not very far off when computer aided detection technology may be used even in the case of simple ailments.
Freer, W. Timothy (2005). “Computer-Aided Detection (CAD) in Screening Mammography: A Prospective Study of 12,860 Patients in a Community Breast Center”. Presented at the Women’s Diagnostic and Breast Health Center in Plano, Texas.
Imaginis (2006). New Technologies to Help Improve Mammography. http://www.imaginis.com/breasthealth/cad.asp
Imaginis (2000). Computer-Aided Detection Technology May Help Improve Accuracy of Mammograms. November 30, 2000. http://www.imaginis.com/breasthealth/news/news11.30.00.asp
Gavin, Kara (2003). Computer-assisted breast imaging systems help find and characterize cancers. February 12, 2003. http://www.innovations-report.de/html/berichte/informationstechnologie/bericht-23829.html
Doi, K. (2005). Current status and future potential of computer-aided diagnosis in medical imaging. British Journal of Radiology (2005) 78, S3-s19. http://bjr.birjournals.org/cgi/content/full/78/suppl_1/S3#BDY
Ling et al (2002). Computer-Aided Detection of Polyps in a Colon Phantom: Effect of Scan Orientation, Polyp Size, Collimation, and Dose. Journal of Computer Assisted Tomography. 26(6):1013-1018, November/December 2002. http://www.jcomputertomography.com/pt/re/jcat/abstract.00004728-20021100000027.htm;jsessionid=FhWZ5LgDry79n6xd7JJ4ythVbLT3QhzjBP1w3YryxZ3VdHnJRPv1!-1640309041!-949856145!8091!-1
Wikipedia (2006). Magnetic Resonance Imaging. http://en.wikipedia.org/wiki/Magnetic_resonance_imaging