Interval change analysis to improve computer aided detection in mammography

• Published in: Medical image analysis Vol. 10; no. 1; pp. 82 - 95
• Main Authors: Timp, Sheila, Karssemeijer, Nico
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2006
• Subjects: Aged; Breast Neoplasms - diagnostic imaging; Computer aided diagnosis (CAD); Data Interpretation, Statistical; Diagnosis, Computer-Assisted - methods; Female; Humans; Interval change analysis; Mammography; Middle Aged; Radiographic Image Interpretation, Computer-Assisted - methods; Registration; Temporal; Computer aided diagnosis (CAD); Interval change analysis; Mammography; Temporal; Registration
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2005.03.007

We are developing computer aided diagnosis (CAD) techniques to study interval changes between two consecutive mammographic screening rounds. We have previously developed methods for the detection of malignant masses based on features extracted from single mammographic views. The goal of the present work was to improve our detection method by including temporal information in the CAD program. Toward this goal, we have developed a regional registration technique. This technique links a suspicious location on the current mammogram with a corresponding location on the prior mammogram. The novelty of our method is that the search for correspondence is done in feature space. This has the advantage that very small lesions and architectural distortions may be found as well. Following the linking process several features are calculated for the current and prior region. Temporal features are obtained by combining the feature values from both regions. We evaluated the detection performance with and without the use of temporal features on a data set containing 2873 temporal film pairs from 938 patients. There were 589 cases in which the current mammogram contained exactly one malignant mass. Cross validation was used to partition the data set into a train set and a test set. The train set was used for feature selection and classifier training, the test set for classifier evaluation. FROC (free response operating characteristic) analysis showed an improvement in detection performance with the use of temporal features.