BREAST CANCER DIAGNOSIS USING THREE-DIMENSIONAL ULTRASOUND AND PIXEL RELATION ANALYSIS
Resource
Ultrasound in Med. & Biol. 29(7),1027-1035
Journal
Ultrasound in Medicine & Biology
Pages
1027-1035
Date Issued
2003
Date
2003
Author(s)
DOI
20060927122917164483
Abstract
Because ultrasound (US) imaging offers benefits compared with other medical imaging techniques, it
is used routinely in nearly all hospitals and many clinics. However, the surface features and internal structure
of a tumor are not easily demonstrated simultaneously using the traditional 2-D US. The newly developed
three-dimensional (3-D) US can capture the morphology of a breast tumor and overcome the limitations of the
traditional 2-D US. This study deals with pixel relation analysis techniques for use with 3-D breast US images and
compares its performance to 2-D versions of the images. The 3-D US imaging was performed using a Voluson 530
scanner. The rectangular subimages of the volume-of-interest (VOI) were manually selected and the selected
VOIs were outlined to include the entire extent of the tumor margin. The databases in this study included 54
malignant and 161 benign tumors. All solid nodules at US belong over C3 (probably benign) according to ACR
BI-RADS category. All or some selected 2-D slices were used separately to calculate the diagnosis features for a
3-D US data set. We have proposed and compared several different methods to extract the characteristics of these
consecutive 2-D images. As shown in our experiments, the diagnostic results were better than those of the
conventional 2-D US. In the experiments, the area index Az under ROC curve of the proposed 3-D US method
can achieve 0.9700 0.0118, but Az of the 2-D US is only 0.8461 0.0315. The p value of these two Az differences
using z test is smaller than 0.01. Furthermore, we can find that the features from only several slices are enough
to provide good diagnostic results if the adopted features are modified from the 2-D features. (E-mail:dlchen88@ms13.hinet.net)
Subjects
3-D breast ultrasound
Pixel relation analysis
Breast tumor
Neural network
SDGs
Type
journal article
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