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Proceedings Paper

Ultrasonic multifeature analysis procedures for breast lesion classification
Author(s): Sheikh Kaisar Alam; Frederick L. Lizzi; Ernest Joseph Feleppa; Tian Liu; Andrew Kalisz
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Paper Abstract

We are developing quantitative descriptors of breast lesions in order to provide reliable, operator-independent means of non-invasive breast cancer identification. These quantitative descriptors include lesion internal features assessed using spectrum analysis of ultrasonic radio-frequency (RF) echo signals and morphometric features related to lesion shape. Internal features include quantitative measures of 'echogenicity,' 'heterogeneity,' and 'shadowing;' these were computed by generating spectral-parameter images of the lesion and surrounding tissue. Spectral-parameter values were generated at each pixel in the parameter image using a sliding-window Fourier analysis. Lesions were traced on B-mode images and traces were used in conjunction with spectral parameter values to compute echogenicity, heterogeneity, and shadowing. Initial results show that no single parameter may be sufficiently precise in identifying cancerous breast lesions; the results also show that the use of multiple features can substantially improve discrimination. This paper describes the background, research objective, and methodology. Clinical examples are included to illustrate the practical application of our methodology.

Paper Details

Date Published: 12 April 2000
PDF: 6 pages
Proc. SPIE 3982, Medical Imaging 2000: Ultrasonic Imaging and Signal Processing, (12 April 2000); doi: 10.1117/12.382226
Show Author Affiliations
Sheikh Kaisar Alam, Riverside Research Institute (United States)
Frederick L. Lizzi, Riverside Research Institute (United States)
Ernest Joseph Feleppa, Riverside Research Institute (United States)
Tian Liu, Riverside Research Institute (United States)
Andrew Kalisz, Riverside Research Institute (United States)


Published in SPIE Proceedings Vol. 3982:
Medical Imaging 2000: Ultrasonic Imaging and Signal Processing
K. Kirk Shung; Michael F. Insana, Editor(s)

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