Reproducibility of image analysis for breast ultrasound computer-aided diagnosis

Acoustical Imaging 03/2008; 29(1):397-402. DOI: 10.1007/978-1-4020-8823-0_55

ABSTRACT We employ a Case-Based Reasoning approach to analyze breast masses in ultrasound and to classify them for level of suspicion for cancer following the ACR BI-RADS® protocol. Our computer-aided imaging system (Breast Companion®, BC) measures numeric features of the mass, determines Relative Similarity (RS) between the mass of interest and images in a database of masses with known findings and outcomes, then retrieves and displays the images of the most similar known masses instantaneously for the radiologist to review during interpretation. This study tested BC for reproducibility of performance in comparison to that of three radiologists under a variety of operating conditions. The long-term goal is to standardize diagnosis, reduce radiologist variability and reduce false positives.

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    ABSTRACT: We demonstrate that the coda of station-to-station Green's functions extracted from the ambient seismic field in southern California reach stability in the microseism band (5-10 s) after correlating six months of noise data. The coda stability makes it possible to retrieve Green's functions between stations that operate asynchronously through scattered waves as recorded by a network of fiducial stations. The Green's functions extracted from asynchronous and synchronous data have comparable quality as long as stable virtual coda are used, and both show good convergence to the Green's functions extracted from 1 year of seismic noise with ˜50 fiducial stations. This approach suggests that Green's functions can be extracted across seismic stations regardless of whether or not they are occupied simultaneously, which raises the prospect of a new mode for seismic experiments that seek to constrain Earth structure.
    Geophysical Research Letters 03/2012; 39(6):6301-. · 3.98 Impact Factor
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    ABSTRACT: Our computer-aided diagnostic (CADx) tool uses advanced image processing and artificial intelligence to analyze findings on breast sonography images. The goal is to standardize reporting of such findings using well-defined descriptors and to improve accuracy and reproducibility of interpretation of breast ultrasound by radiologists. This study examined several factors that may impact accuracy and reproducibility of the CADx software, which proved to be highly accurate and stabile over several operating conditions. Keywords Breast cancer - Sonography - Computer-aided diagnosis - Image processing - Relative similarity - ROC analysis - Segmentation - Case-based reasoning
    Acoustical Imaging, 30 edited by Michael P Andre, Joie P Jones, Hua Lee, 01/2011: pages 3-10; Springer., ISBN: 978-90-481-3254-6

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May 22, 2014

Michael P Andre