A Mammographic Registration Method Based on Optical Flow and Mmultiresolution Computing

Kevin A Lee

ISBN 10: 1025083415 / ISBN 13: 9781025083414

Verlag: Creative Media Partners, LLC Mai 2025, 2025

Beschreibung

Neuware.

Bestandsnummer des Verkäufers 9781025083414

Bibliografische Details

• Titel: A Mammographic Registration Method Based on Optical Flow and Mmultiresolution Computing
• Verlag: Creative Media Partners, LLC Mai 2025
• Erscheinungsjahr: 2025
• Sprache: Englisch
• ISBN-10: 1025083415
• ISBN-13: 9781025083414
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 234B x 156 H x 6 T Millimeter Breite: 234 mm, Höhe: 156 mm, Tiefe: 6 mm
• Gewicht: 159 Gramm

Über diesen Titel

Inhaltsangabe

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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