A Mammographic Registration Method Based on Optical Flow and Mmultiresolution Computing
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Breast cancer is second only to lung cancer as the most prevalent form of cancer to afflict women remaining the leading cause of cancer death in women between the ages of 40 and 55. Mammography is a potent weapon in the fight against this lethal disease, due in large part to its widespread availability and low cost. Despite the fact that mammography can detect small lesions as early as two years before they become palpable on physical exam, between 10 and 30 percent of cancerous lesions go undetected during evaluation by the radiologist. One approach to improving detection rates involves compa...
Breast cancer is second only to lung cancer as the most prevalent form of cancer to afflict women remaining the leading cause of cancer death in women between the ages of 40 and 55. Mammography is a potent weapon in the fight against this lethal disease, due in large part to its widespread availability and low cost. Despite the fact that mammography can detect small lesions as early as two years before they become palpable on physical exam, between 10 and 30 percent of cancerous lesions go undetected during evaluation by the radiologist. One approach to improving detection rates involves comparing mammograms of the same breast from successive years. Since most forms of breast cancer develop slowly, multiple-view techniques might be able to detect subtle changes indicative of cancerous growth. This thesis proposes a computer-aided system designed to bring two images into correspondence, or alignment, so that they can be compared and evaluated for possible abnormalities. The system estimates a mapping between two images by calculating the optical flow, or apparent intensity change, between a source and target mammogram. The efficiency of the proposed registration system is enhanced by utilizing a multire solution approach whereby images are compared at more than one scale. In contrast to other registration attempts which match sets of morphological features, this system does not require the identification of any control points at all. This advantage permits the system to perform well even when the two images differ significantly from one another. Preliminary results suggest the potential usefulness of this system as part of a clinical computer-aided detection (CADx) system.
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