Multichannel response analysis on 2D projection views for detection of clustered microcalcifications in digital breast tomosynthesis. Issue 4 (25th March 2014)

• Record Type: Journal Article
• Authors: Wei, Jun; Chan, Heang‐Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Lu, Yao; Zhou, Chuan; Samala, Ravi
• Is Part Of: Medical physics. Volume 41:Issue 4(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-03-25
• Subjects: Digital mammography -- Edge enhancement -- Probability theory, stochastic processes, and statistics; biological organs -- Gaussian processes -- image classification -- image enhancement -- image fusion -- mammography -- medical image processing -- pattern clustering -- sensitivity analysis; computer‐aided detection (CADe) -- digital breast tomosynthesis (DBT) -- multichannel response (MCR) analysis -- Hermite–Gaussian (HG) -- Laguerre–Gaussian (LG); Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; Monte Carlo methods -- Optical microcavities -- Medical imaging -- Computer aided diagnosis -- Cluster analysis -- Three dimensional sensing -- Image reconstruction -- Image detection systems -- Three dimensional image processing -- Mammography; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4868694
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : To investigate the feasibility of a new two‐dimensional (2D) multichannel response (MCR) analysis approach for the detection of clustered microcalcifications (MCs) in digital breast tomosynthesis (DBT). Methods: : With IRB approval and informed consent, a data set of two‐view DBTs from 42 breasts containing biopsy‐proven MC clusters was collected in this study. The authors developed a 2D approach for MC detection using projection view (PV) images rather than the reconstructed three‐dimensional (3D) DBT volume. Signal‐to‐noise ratio (SNR) enhancement processing was first applied to each PV to enhance the potential MCs. The locations of MC candidates were then identified with iterative thresholding. The individual MCs were decomposed with Hermite–Gaussian (HG) and Laguerre–Gaussian (LG) basis functions and the channelized Hotelling model was trained to produce the MCRs for each MC on the 2D images. The MCRs from the PVs were fused in 3D by a coincidence counting method that backprojects the MC candidates on the PVs and traces the coincidence of their ray paths in 3D. The 3D MCR was used to differentiate the true MCs from false positives (FPs). Finally a dynamic clustering method was used to identify the potential MC clusters in the DBT volume based on the fact that true MCs of clinical significance appear in clusters. Using two‐fold cross validation, the performance of the 3D MCR for classification of true and false MCs was estimated by the area under the receiver operating characteristic (ROC) curve and the overall performance of the MCR approach for detection of clustered MCs was assessed by free response receiver operating characteristic (FROC) analysis. Results: : When the HG basis function was used for MCR analysis, the detection of MC cluster achieved case‐based test sensitivities of 80% and 90% at the average FP rates of 0.65 and 1.55 FPs per DBT volume, respectively. With LG basis function, the average FP rates were 0.62 and 1.57 per DBT volume at the same sensitivity levels. The difference in the two sets of basis functions for detection of MCs did not show statistical significance. Conclusions: : The authorsˈ experimental results indicate that the MCR approach is promising for the detection of MCs on PV images. The HG or LG basis functions are both effective in characterizing the signal response of MCs using the channelized Hotelling model. The coincidence counting method for fusion of the 2D MCR in 3D is an important step for FP reduction. Further study is underway to improve the MCR approach for microcalcification detection in DBT.