Automated regional registration and characterization of corresponding microcalcification clusters on temporal pairs of mammograms for interval change analysis.
A computerized regional registration and characterization system for analysis of microcalcification clusters on serial mammograms is being developed in our laboratory. The system consists of two stages. In the first stage, based on the location of a detected cluster on the current mammogram, a regional registration procedure identifies the local area on the prior that may contain the corresponding cluster. A search program is used to detect cluster candidates within the local area. The detected cluster on the current image is then paired with the cluster candidates on the prior image to form true (TP-TP) or false (TP-FP) pairs. Automatically extracted features were used in a newly designed correspondence classifier to reduce the number of false pairs. In the second stage, a temporal classifier, based on both current and prior information, is used if a cluster has been detected on the prior image, and a current classifier, based on current information alone, is used if no prior cluster has been detected. The data set used in this study consisted of 261 serial pairs containing biopsy-proven calcification clusters. An MQSA radiologist identified the corresponding clusters on the mammograms. On the priors, the radiologist rated the subtlety of 30 clusters (out of the 261 clusters) as 9 or 10 on a scale of 1 (very obvious) to 10 (very subtle). Leave-one-case-out resampling was used for feature selection and classification in both the correspondence and malignant/benign classification schemes. The search program detected 91.2% (238/261) of the clusters on the priors with an average of 0.42 FPs/image. The correspondence classifier identified 86.6% (226/261) of the TP-TP pairs with 20 false matches (0.08 FPs/image) relative to the entire set of 261 image pairs. In the malignant/benign classification stage the temporal classifier achieved a test A(z) of 0.81 for the 246 pairs which contained a detection on the prior. In addition, a classifier was designed by using the clusters on the current mammograms only. It achieved a test A(z) of 0.72 in classifying the clusters as malignant and benign. The difference between the performance of the temporal classifier and the current classifier was statistically significant (p=0.0014). Our interval change analysis system can detect the corresponding cluster on the prior mammogram with high sensitivity, and classify them with a satisfactory accuracy.
Filev
Peter
P
Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109-0904, USA.
Hadjiiski
Lubomir
L
Chan
Heang-Ping
HP
Sahiner
Berkman
B
Ge
Jun
J
Helvie
Mark A
MA
Roubidoux
Marilyn
M
Zhou
Chuan
C
D001331:Automation; D001706:Biopsy; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D002114:Calcinosis / Q000175:diagnosis / Q000473:pathology; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted; D055088:Early Detection of Cancer; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013997:Time Factors