Breast elastography diagnosis based on dynamic sequence features
Journal
Medical Physics
Journal Volume
40
Journal Issue
2
Pages
022905
Date Issued
2013-02
Author(s)
Abstract
Purpose: Each dynamic elastography contains multiple images; therefore, physicians need to determine the most representative image from the scanned sequence to make diagnosis. To eliminate interobserver variations on diagnoses and help doctors providing correct treatments, the authors developed an objective computer-aided diagnostic scheme without requiring manually selecting representative images for diagnosis. Methods: About 112 histological-proven lesions including 66 benign and 46 malignant tumors were involved as the material database. Suspicious lesions were automatically segmented on the first B-mode images on each captured dynamic elastography. Tissue strains inside lesions on elastograms were classified by utilizing the fuzzy c-means algorithm. In order to reduce the influence of image quality, important tumor characteristics were computed from every strain images in elastography and regressed to a probability of being malignant. Since tumor boundaries changed slightly between adjacent slices, a tumor boundary tracking scheme based on template matching was applied on slices excepting the first one. Results: The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are 85.71% (96/112), 86.96% (40/46), 84.85% (56/66), 80.00% (40/50), and 90.32% (56/62). In addition, the area under the receiver operating characteristic curve is 0.9016. Conclusions: The authors' proposed study provides a reliable computer-aided system which helps physicians to make diagnoses according to features computed from entire elastographic sequence. Experimental results illustrate that the diagnostic scheme is sufficient in distinguishing benign and malignant tumors. It is not necessary for physicians to spend a lot of time to determine the most suitable image for diagnosis. The tumor boundary tracking mechanism effectively eliminates the computation time since it slightly adjusts tumor boundaries between neighboring slices instead of segmenting the tumor contour on each image in the dynamic elastography. The system sufficiently reduces the variations of diagnosis caused by operator dependencies and image qualities and furthermore save physicians' workloads. ? 2013 American Association of Physicists in Medicine.
Subjects
computer-aided diagnosis; dynamic breast tumor elastography; fuzzy c-means clustering; lesion boundary tracking
SDGs
Other Subjects
Clustering algorithms; Copying; Fuzzy clustering; Fuzzy systems; Image quality; Medical imaging; Strain; Template matching; Tumors; Benign and malignant tumors; Boundary tracking; Breast tumor; Computer aided diagnostics; Fuzzy C means clustering; Negative predictive value; Positive predictive values; Receiver operating characteristic curves; Computer aided diagnosis; adult; article; B scan; breast cancer; diagnostic test accuracy study; dynamics; elastography; histology; human; image quality; major clinical study; malignant neoplastic disease; predictive value; priority journal; receiver operating characteristic; sensitivity and specificity; aged; algorithm; breast tumor; cluster analysis; echography; echomammography; female; fuzzy logic; image processing; methodology; middle aged; statistical model; Adult; Aged; Algorithms; Breast Neoplasms; Cluster Analysis; Elasticity Imaging Techniques; Female; Fuzzy Logic; Humans; Image Processing, Computer-Assisted; Logistic Models; Middle Aged; Ultrasonography, Mammary; Young Adult
Type
journal article