Course Description
Breast cancer is the second-most diagnosed cancer in women (after skin cancer) and can often be found through self-examinations before any symptoms appear. According to the America Cancer Society, about 1 in 8 women will be diagnosed with breast cancer in their lifetime. Early detection is critical to improve outcomes, survival rate, and successful treatment of each patient. Computer-aided diagnosis (CAD) technology is used to improve the quality of mammograms, which can assist the radiologist with interpreting the images correctly and identify possible abnormalities that are undetectable by the human eye. While not all breast cancers can be detected through a self-examination or a mammogram, regular screenings can greatly increase the chances of early detection, successful treatment, and even a cure. The use of CAD in mammography can increase sensitivity of mammograms and plays a supporting and final interpretive role in diagnosis. This continuing education course will outline the process of analyzing mammograms utilizing a CAD system and present an updated system that introduces advanced techniques used to overcome the difficulty of identifying subtle signs of breast cancer and its stage. Segmentation, or the separation of the abnormal part (mass) from normal breast tissue on an image, will be presented with current approaches and practices as well as modifications for mammograms. In addition, feature extraction techniques and classification methods, the 2 most important steps in designing CAD systems, will be examined and compared.
Learning Objectives
After reading the content, the participant should be able to:
Categories: Mammography, Digital Radiography, Technology
In order to receive CE credit, you must first complete the activity content. When completed, go to the "Take CE Test!" link to access the post-test.
Submit the completed answers to determine if you have passed the post-test assessment. You must answer 17 out of 22 questions correctly to receive the CE credit. You will have no more than 3 attempts to successfully complete the post-test.
Participants successfully completing the activity content and passing the post-test will receive 1.5 ARRT Category A credits.
This program is approved by AHRA, a Recognized Continuing Education Evaluation Mechanism (RCEEM), approved by the ARRT to grant Category A CE credit.
Approved by the state of Florida for ARRT Category A credit.
Texas direct credit.
This activity may be available in multiple formats or from different sponsors. ARRT does not allow CE activities such as Internet courses, home study programs, or directed readings to be repeated for CE credit in the same biennium.
Category | Content Area | Credits |
---|---|---|
Mammography | Image Production | 1.5 |
Category | Subcategory | Credits |
---|---|---|
Mammography | Image Acquisition and Quality Assurance | 1.5 |
Category | Credits |
---|---|
Digital | 0.25 |
Fluoroscopy | 0 |
Mammography | 0.75 |
Bhagirathi Halalli and Aziz Makandar
ABSTRACT
Breast cancer is the second-most diagnosed cancer in women (after skin cancer) and can often be found through self-examinations before any symptoms appear. According to the America Cancer Society, about 1 in 8 women will be diagnosed with breast cancer in their lifetime. Early detection is critical to improve outcomes, survival rate, and successful treatment of each patient. Computer-aided diagnosis (CAD) technology is used to improve the quality of mammograms, which can assist the radiologist with interpreting the images correctly and identify possible abnormalities that are undetectable by the human eye. While not all breast cancers can be detected through a self-examination or a mammogram, regular screenings can greatly increase the chances of early detection, successful treatment, and even a cure. The use of CAD in mammography can increase sensitivity of mammograms and plays a supporting and final interpretive role in diagnosis. This continuing education course will outline the process of analyzing mammograms utilizing a CAD system and present an updated system that introduces advanced techniques used to overcome the difficulty of identifying subtle signs of breast cancer and its stage. Segmentation, or the separation of the abnormal part (mass) from normal breast tissue on an image, will be presented with current approaches and practices as well as modifications for mammograms. In addition, feature extraction techniques and classification methods, the 2 most important steps in designing CAD systems, will be examined and compared.
* This sample course is for reference purposes only. It is not currently available for earning CE credits. To earn ARRT CE credits please subscribe to eRADIMAGING where you will see a complete listing of all active and eligible CE courses.
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