Computerized analysis of images in the detection and diagnosis of breast cancer

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Improvements in mammographic acquisition techniques have resulted in making the early signs of breast cancer more apparent on mammograms. However, the accuracy of the overall mammographic examination depends on both the quality of the mammographic images and the ability of the radiologist to interpret those images. While mammography is the best screening method for the early detection of breast cancer, radiologists do miss lesions on mammograms. Use of output, however, from a computerized analysis of an image by a radiologist may help him/her in the detection or diagnostic tasks, and potentially improve the overall interpretation of breast images and the subsequent patient care. Computer-aided detection and diagnosis (CAD) involves the application of computer technology to the process of medical image interpretation. CAD can be defined as a diagnosis made by a radiologist, who uses the output from a computerized analysis of medical images as a “second opinion” in detecting and diagnosing lesions, with the final diagnosis being made by the radiologist. The computer output must be at a sufficient performance level, and in addition, the output must be displayed in a user-friendly format for effective and efficient use by the radiologist. This chapter reviews CAD in breast cancer detection and diagnosis, including examples of image analyses, multi-modality approaches (i.e., special-view diagnostic mammography, ultrasound, and MRI), and means of communicating the computer output to the human.

Section snippets

Image quality and CAD

Signal detection theory applies to all observers, whether they are human or computer. Therefore, one would not expect a CAD system to be able to detect a lesion having low signal levels or in high noise levels on poorly obtained mammograms.12 Conversely, one would anticipate that the performance of a computerized analysis of mammograms would improve if the acquired breast images were of high image quality. Improved signal to noise characteristics, as expected with full-field digital mammography

Computer vision and artificial intelligence

Computer vision involves the automatic extraction of features, which may or may not be visible to a human observer, from a digital image. The development of computer vision methods requires a priori knowledge of the physical imaging properties of the digital image acquisition system and the morphological information concerning the abnormality (e.g., mass lesion or cluster of microcalcifications) along with the normal parenchyma. A sufficient number and types of cases in a database is needed to

Computer-aided detection

Computerized detection of lesions involves having the computer locate suspicious regions, leaving the subsequent classification (e.g., probability of malignancy status) of the lesion and patient management decisions to the radiologist. In such situations, the computer is acting as a second reader, like a spell checker, in the breast cancer screening process. Most computerized detection schemes in mammography are being developed for the detection of mass lesions or clustered microcalcifications.

CAD in diagnostic breast imaging

Once a possible abnormality is detected, it characteristics must be evaluated by the radiologist in order to estimate a likelihood of malignancy and to yield a decision on patient management. Characteristics of the lesion may be evaluated further by multiple imaging techniques, including special view mammography, ultrasound, and magnetic resonance imaging (MRI), in order to improve the positive predictive value for biopsy recommendations.

The initial investigations into the use of computers in

Indices of similarity and human/computer interface

Radiologists interpret cases rather than individual images, thus the computerized analysis of breast images can be case-based as opposed to image-based. Computer analysis of multiple views or multiple modalities, however, requires effective and efficient displays in order to communicate the multiple images and output to the radiologist. Research has been performed to develop display interfaces, which would better present the computer output to the radiologist.74, 75, 76, 77

The output of a

Summary

Limitations in the human eye-brain visual system, the presence of overlapping structures in images, and the vast umber of normal cases in screening programs provide motivation for the use of computer techniques that have the potential to improve detection and diagnostic performance, and ultimately patient care. The success of computerized analyses of breast images depends on both the ability of the computer vision and artificial intelligence techniques that extract and characterize suspect

Acknowledgements

The author is grateful for the many fruitful discussions she has had with the faculty and research staff in the Department of Radiology, University of Chicago, especially to Carl J. Vyborny, MD, PhD, who passed away during the preparation of this manuscript. M.L. Giger is a shareholder in R2 Technology, Inc., Los Altos, CA. It is the University of Chicago conflict-of-interest policy that investigators disclose publicly actual or potential significant financial interests that may appear to be

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