title: Evaluation of clinical image processing algorithms used in digital mammography.
abstract: Screening is the only proven approach to reduce the mortality of breast cancer, but significant numbers of breast cancers remain undetected even when all quality assurance guidelines are implemented. With the increasing adoption of digital mammography systems, image processing may be a key factor in the imaging chain. Although to our knowledge statistically significant effects of manufacturer-recommended image processings have not been previously demonstrated, the subjective experience of our radiologists, that the apparent image quality can vary considerably between different algorithms, motivated this study. This article addresses the impact of five such algorithms on the detection of clusters of microcalcifications. A database of unprocessed (raw) images of 200 normal digital mammograms, acquired with the Siemens Novation DR, was collected retrospectively. Realistic simulated microcalcification clusters were inserted in half of the unprocessed images. All unprocessed images were subsequently processed with five manufacturer-recommended image processing algorithms (Agfa Musica 1, IMS Raffaello Mammo 1.2, Sectra Mamea AB Sigmoid, Siemens OPVIEW v2, and Siemens OPVIEW v1). Four breast imaging radiologists were asked to locate and score the clusters in each image on a five point rating scale. The free-response data were analyzed by the jackknife free-response receiver operating characteristic (JAFROC) method and, for comparison, also with the receiver operating characteristic (ROC) method. JAFROC analysis revealed highly significant differences between the image processings (F = 8.51, p < 0.0001), suggesting that image processing strongly impacts the detectability of clusters. Siemens OPVIEW2 and Siemens OPVIEW1 yielded the highest and lowest performances, respectively. ROC analysis of the data also revealed significant differences between the processing but at lower significance (F = 3.47, p = 0.0305) than JAFROC. Both statistical analysis methods revealed that the same six pairs of modalities were significantly different, but the JAFROC confidence intervals were about 32% smaller than ROC confidence intervals. This study shows that image processing has a significant impact on the detection of microcalcifications in digital mammograms. Objective measurements, such as described here, should be used by the manufacturers to select the optimal image processing algorithm.
authors.affiliation: Department of Radiology and Leuven University Center of Medical Physics in Radiology, University Hospitals Leuven, 3000 Leuven, Belgium. federica.zanca@uz.kuleuven.ac.be
authors.forename: Federica
authors.initials: F
authors.lastname: Zanca
authors.forename: Jurgen
authors.initials: J
authors.lastname: Jacobs
authors.forename: Chantal
authors.initials: C
authors.lastname: Van Ongeval
authors.forename: Filip
authors.initials: F
authors.lastname: Claus
authors.forename: Valerie
authors.initials: V
authors.lastname: Celis
authors.forename: Catherine
authors.initials: C
authors.lastname: Geniets
authors.forename: Veerle
authors.initials: V
authors.lastname: Provost
authors.forename: Herman
authors.initials: H
authors.lastname: Pauwels
authors.forename: Guy
authors.initials: G
authors.lastname: Marchal
authors.forename: Hilde
authors.initials: H
authors.lastname: Bosmans
mesh_terms: D000465:Algorithms; D055592:Biophysical Phenomena; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D002114:Calcinosis / Q000000981:diagnostic imaging; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data*; D019047:Phantoms, Imaging; D012372:ROC Curve; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted; D012984:Software