The effect of image processing on the detection of cancers in digital mammography.

OBJECTIVE. The objective of our study was to investigate the effect of image processing on the detection of cancers in digital mammography images. MATERIALS AND METHODS. Two hundred seventy pairs of breast images (both breasts, one view) were collected from eight systems using Hologic amorphous selenium detectors: 80 image pairs showed breasts containing subtle malignant masses; 30 image pairs, biopsy-proven benign lesions; 80 image pairs, simulated calcification clusters; and 80 image pairs, no cancer (normal). The 270 image pairs were processed with three types of image processing: standard (full enhancement), low contrast (intermediate enhancement), and pseudo-film-screen (no enhancement). Seven experienced observers inspected the images, locating and rating regions they suspected to be cancer for likelihood of malignancy. The results were analyzed using a jackknife-alternative free-response receiver operating characteristic (JAFROC) analysis. RESULTS. The detection of calcification clusters was significantly affected by the type of image processing: The JAFROC figure of merit (FOM) decreased from 0.65 with standard image processing to 0.63 with low-contrast image processing (p = 0.04) and from 0.65 with standard image processing to 0.61 with film-screen image processing (p = 0.0005). The detection of noncalcification cancers was not significantly different among the image-processing types investigated (p > 0.40). CONCLUSION. These results suggest that image processing has a significant impact on the detection of calcification clusters in digital mammography. For the three image-processing versions and the system investigated, standard image processing was optimal for the detection of calcification clusters. The effect on cancer detection should be considered when selecting the type of image processing in the future.

[1]  S. Moss,et al.  Interval cancers in the NHS breast cancer screening programme in England, Wales and Northern Ireland , 2011, British Journal of Cancer.

[2]  Hilde Bosmans,et al.  Effect of image quality on calcification detection in digital mammography. , 2012, Medical physics.

[3]  Stephen L Hillis,et al.  Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis. , 2008, Academic radiology.

[4]  D R Dance,et al.  Validation of simulation of calcifications for observer studies in digital mammography , 2013, Physics in medicine and biology.

[5]  Dev P Chakraborty,et al.  Observer studies involving detection and localization: modeling, analysis, and validation. , 2004, Medical physics.

[6]  Robert M. Nishikawa,et al.  Radiologists’ Preferences for Digital Mammographic Display , 2000 .

[7]  K. Berbaum,et al.  Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method. , 1992, Investigative radiology.

[8]  E. Halpern,et al.  Assessing radiologist performance using combined digital mammography and breast tomosynthesis compared with digital mammography alone: results of a multicenter, multireader trial. , 2013, Radiology.

[9]  Jing Xu,et al.  Comparison of tissue equalization, and premium view post-processing methods in full field digital mammography. , 2010, European journal of radiology.

[10]  N. Obuchowski,et al.  Comparing the performance of mammographic enhancement algorithms: a preference study. , 2000, AJR. American journal of roentgenology.

[11]  Dev P Chakraborty,et al.  A brief history of free-response receiver operating characteristic paradigm data analysis. , 2013, Academic radiology.

[12]  S. Ashley,et al.  The effect of Premium View post-processing software on digital mammographic reporting. , 2010, The British journal of radiology.

[13]  Ehsan Samei,et al.  Assessment of display performance for medical imaging systems: executive summary of AAPM TG18 report. , 2005, Medical physics.

[14]  E. Samei,et al.  Dose dependence of mass and microcalcification detection in digital mammography: free response human observer studies. , 2007, Medical physics.

[15]  H. Honda,et al.  Detection of breast cancer by soft-copy reading of digital mammograms: Comparison between a routine image-processing parameter and high-contrast parameters , 2010, Acta radiologica.

[16]  Nico Karssemeijer,et al.  Increase in perceived case suspiciousness due to local contrast optimisation in digital screening mammography , 2011, European Radiology.

[17]  Hilde Bosmans,et al.  Evaluation of clinical image processing algorithms used in digital mammography. , 2009, Medical physics.

[18]  D R Dance,et al.  Comparison of the x-ray attenuation properties of breast calcifications, aluminium, hydroxyapatite and calcium oxalate , 2013, Physics in medicine and biology.

[19]  Andrew D. A. Maidment,et al.  Diagnostic accuracy of digital mammography in patients with dense breasts who underwent problem-solving mammography: effects of image processing and lesion type. , 2003, Radiology.

[20]  Hilde Bosmans,et al.  Software Framework for Simulating Clusters of Microcalcifications in Digital Mammography , 2010, Digital Mammography / IWDM.