Quantitative comparison of clustered microcalcifications in for‐presentation and for‐processing mammograms in full‐field digital mammography. Issue 7 (9th June 2017)

• Record Type: Journal Article
• Authors: Wang, Juan; Nishikawa, Robert M.; Yang, Yongyi
• Is Part Of: Medical physics. Volume 44:Issue 7(2017)
• Page Start: 3726
• Page End: 3738
• Publication Date: 2017-06-09
• Subjects: clustered microcalcifications (MCs) -- computer‐aided diagnosis (CAD) -- for‐presentation format -- full‐field digital mammography; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.12316
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Mammograms acquired with full‐field digital mammography (FFDM) systems are provided in both "for‐processing'' and "for‐presentation'' image formats. For‐presentation images are traditionally intended for visual assessment by the radiologists. In this study, we investigate the feasibility of using for‐presentation images in computerized analysis and diagnosis of microcalcification (MC) lesions. Methods: We make use of a set of 188 matched mammogram image pairs of MC lesions from 95 cases (biopsy proven), in which both for‐presentation and for‐processing images are provided for each lesion. We then analyze and characterize the MC lesions from for‐presentation images and compare them with their counterparts in for‐processing images. Specifically, we consider three important aspects in computer‐aided diagnosis (CAD) of MC lesions. First, we quantify each MC lesion with a set of 10 image features of clustered MCs and 12 textural features of the lesion area. Second, we assess the detectability of individual MCs in each lesion from the for‐presentation images by a commonly used difference‐of‐Gaussians (DoG) detector. Finally, we study the diagnostic accuracy in discriminating between benign and malignant MC lesions from the for‐presentation images by a pretrained support vector machine (SVM) classifier. To accommodate the underlying background suppression and image enhancement in for‐presentation images, a normalization procedure is applied. Results: The quantitative image features of MC lesions from for‐presentation images are highly consistent with that from for‐processing images. The values of Pearson's correlation coefficient between features from the two formats range from 0.824 to 0.961 for the 10 MC image features, and from 0.871 to 0.963 for the 12 textural features. In detection of individual MCs, the FROC curve from for‐presentation is similar to that from for‐processing. In particular, at sensitivity level of 80%, the average number of false‐positives (FPs) per image region is 9.55 for both for‐presentation and for‐processing images. Finally, for classifying MC lesions as malignant or benign, the area under the ROC curve is 0.769 in for‐presentation, compared to 0.761 in for‐processing ( P = 0.436). Conclusion: The quantitative results demonstrate that MC lesions in for‐presentation images are highly consistent with that in for‐processing images in terms of image features, detectability of individual MCs, and classification accuracy between malignant and benign lesions. These results indicate that for‐presentation images can be compatible with for‐processing images for use in CAD algorithms for MC lesions.