Development and implementation of a user friendly and automated environment for the creation of databases of digital mammograms with simulated microcalcifications

Zanca, Federica; Jacobs, Jurgen; Pöyry, P; Ongeval, Chantal Van; Carton, Ann-Catherine; Deprez, Tom; Marchal, Guy; Bosmans, Hilde

doi:10.1117/12.653135

Cited by 3 publications

(4 citation statements)

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“…Twenty-six different simulated clusters of microcalcifications were created by extending a previously described method. 21,22 It used digital images of large core ͑ten gauge͒ needle biopsy specimens containing pathology-proven microcalcification clusters. The specimens were superposed on 40 mm polymethylmethacrylate, simulating a breast with a compressed thickness of 45 mm and 50% glandularity.…”

Section: Iib1 Simulation Of Clusters Of Microcalcificationsmentioning

confidence: 99%

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Evaluation of clinical image processing algorithms used in digital mammography

et al. 2009

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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.

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Section: Iib1 Simulation Of Clusters Of Microcalcificationsmentioning

confidence: 99%

“…An in-house developed JAVA application simulating artifacts for radiological applications 21,25 ͑SARA͒ was used to create simulated ͑hybrid͒ abnormal images. The software reads DICOM raw images and one can conveniently insert simulated lesions at specified locations.…”

Section: Iib2 Insertion Of Clusters Of Microcalcification Into CLImentioning

confidence: 99%

Evaluation of clinical image processing algorithms used in digital mammography

et al. 2009

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“…To correct for kVp and beam quality differences the pixel values were converted to Al-equivalent thicknesses as follows. 17,18 Aluminium was chosen as the reference element since it is close in atomic number to the composition of microcalcifications 19 and is therefore expected to respond similarly to changes in kVp and beam quality. We exposed 0.1, 0.2, 0.3, and 0.4 mm Al of 99.9% purity ͑Nuclear Associates, NY͒ superposed on 40 mm PMMA at 27 kVp and 180 mA s, Mo/ Mo filter, and calculated the average x-ray transmissions of the different sheets.…”

Section: Iic Simulation Of Microcalcifications For Other Chosen Sysmentioning

confidence: 99%

“…From the 97 templates, 166 artificial microcalcifications with different x-ray transmission were generated. The sizes of the simulated microcalcifications varied from 200 to 1238 m, contrast varied from 1.3% to 25%, and Alequivalent thicknesses varied from 20 and 325 m. 17 Out of the 166 artificial microcalcifications, 59 were selected for the validation experiment, in order to obtain a representative sample of the morphology.…”

Section: Iic Simulation Of Microcalcifications For Other Chosen Sysmentioning

confidence: 99%

An improved method for simulating microcalcifications in digital mammograms

et al. 2008

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The assessment of the performance of a digital mammography system requires an observer study with a relatively large number of cases with known truth which is often difficult to assemble. Several investigators have developed methods for generating hybrid abnormal images containing simulated microcalcifications. This article addresses some of the limitations of earlier methods. The new method is based on digital images of needle biopsy specimens. Since the specimens are imaged separately from the breast, the microcalcification attenuation profile scan is deduced without the effects of over and underlying tissues. The resulting templates are normalized for image acquisition specific parameters and reprocessed to simulate microcalcifications appropriate to other imaging systems, with different x-ray, detector and image processing parameters than the original acquisition system. This capability is not shared by previous simulation methods that have relied on extracting microcalcifications from breast images. The method was validated by five experienced mammographers who compared 59 pairs of simulated and real microcalcifications in a two-alternative forced choice task designed to test if they could distinguish the real from the simulated lesions. They also classified the shapes of the microcalcifications according to a standardized clinical lexicon. The observed probability of correct choice was 0.415, 95% confidence interval (0.284, 0.546), showing that the radiologists were unable to distinguish the lesions. The shape classification revealed substantial agreement with the truth (mean kappa = 0.70), showing that we were able to accurately simulate the lesion morphology. While currently limited to single microcalcifications, the method is extensible to more complex clusters of microcalcifications and to three-dimensional images. It can be used to objectively assess an imaging technology, especially with respect to its ability to adequately visualize the morphology of the lesions, which is a critical factor in the benign versus malignant classification of a lesion detected in screening mammography.

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