CADe systems in breast imaging are fully automated computer vision algorithms developed to improve radiologists’ performance in detecting breast lesions, by identifying suspicious regions in a breast image or series of images.
Published in Chapter:
Computer-Aided Diagnosis in Breast Imaging: Trends and Challenges
Lena Costaridou (University of Patras, Greece), Spyros Skiadopoulos (University of Patras, Greece), Anna Karahaliou (University of Patras, Greece), Nikolaos Arikidis (University of Patras, Greece), and George Panayiotakis (University of Patras, Greece)
Copyright: © 2009
|Pages: 18
DOI: 10.4018/978-1-60566-314-2.ch010
Abstract
Breast cancer is the most common cancer in women worldwide. Mammography is currently the most effective modality in detecting breast cancer, challenged by the presence of dense breast parenchyma with relatively low specificity in distinguishing malignant from benign lesions. Breast ultrasound and Magnetic Resonance Imaging (MRI) are significant adjuncts to mammography providing additional diagnostic information. Various Computer-Aided Diagnosis (CADx) schemes have been proposed across modalities, acting as clinical tools that provide a “second opinion” to assist radiologists in the diagnostic task of lesion characterization by means of quantitative image feature extraction and classification methods. The advent of multimodality imaging broadens the role of CADx, in terms of complementary tissue properties analyzed. In this chapter, major stages of CADx schemes in breast imaging are reviewed, while challenges and trends are discussed and highlighted by corresponding application examples of CADx methodologies for microcalcification clusters in mammography and masses in Dynamic Contrast-Enhanced MRI.