Computerized detection and classification of lesions on breast ultrasound

Karen Drukker, Maryellen L. Giger*, Carl J. Vyborny, Robert A. Schmidt, Ellen B. Mendelson, Michael Stern

*Corresponding author for this work

Research output: Contribution to journalConference article

5 Scopus citations

Abstract

We are developing a computerized method that detects suspicious areas on ultrasound images, and then distinguishes between malignant and benign-type lesions. The computerized scheme identifies potential lesions based on expected lesion shape and margin characteristics. All potential lesions are subsequently classified by a Bayesian neural net based on computer-extracted lesion features. The scheme was trained on a database of 400 cases (757 images) - consisting of complex cysts, benign and malignant lesions - and tested on a comparable database of 458 cases (1740 images) including 578 normal images. We investigated the performances of lesion detection and subsequent classification by a Bayesian neural net for two tasks. The first task was the distinction between actual lesions and false-positive (FP) detections, and the second task the distinction between actual malignant lesions and all detected lesion candidates. In training, the detection and classification method obtained an Az value of 0.94 in the distinction of false-positive detections from actual lesions, and an Az of 0.91 was obtained on the testing database. The task of distinguishing malignant lesions from all other detections (false-positives plus all benign type lesions) showed to be more challenging and Az values of 0.87 and 0.81 were obtained during training and testing, respectively. For the testing database, the combined detection and classification scheme correctly identified lesions in 82% (0.45 FP per image) of all the patients, and in 100% (0.43 FP malignancies per image) of the cancer patients.

Original languageEnglish (US)
Pages (from-to)106-110
Number of pages5
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5032 I
DOIs
StatePublished - Sep 15 2003
EventMedical Imaging 2003: Image Processing - San Diego, CA, United States
Duration: Feb 17 2003Feb 20 2003

Keywords

  • Breast sonography
  • Computer-aided diagnosis
  • Lesion detection

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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