A methodology for evaluation of boundary detection algorithms on breast ultrasound images.
A methodology for evaluation of boundary detection algorithms on breast ultrasound images.
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一种评估乳腺超声图像边界检测算法的方法。
DOI：
10.1080/03091900110067292
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发表时间：
2001
影响因子：
--
通讯作者：
S. Chen
中科院分区：
文献类型：
--
作者：
J. Hsu;C. Tseng;S. Chen
图像分割是将图像分割成一组组成整个图像的非重叠区域。图像被分解成有意义的部分，这些部分在某些特征方面是一致的，例如灰度级或纹理。本研究提出了一种新的评价超声图像分割算法的方法。声像图特征可鉴别不同大小的乳腺肿瘤。临床实验可以根据肿瘤的轮廓、形状、回声性质和回声纹理来确定肿瘤是否良性。进一步研究标准化的超声特征，特别是肿瘤的轮廓和形态，将提高乳腺肿瘤检测的阳性预测值和准确率。通过对乳腺超声图像的分割实验，验证了该方法的有效性。通过一定的分割，可以确定被识别的肿瘤的形状和轮廓。此外，该方法还可以提高超声对乳腺良恶性病变的鉴别能力。
Image segmentation is the partition of an image into a set of non-overlapping regions that comprise the entire image. The image is decomposed into meaningful parts, which are uniform with respect to certain characteristics, such as grey level or texture. This study presents a novel methodology to evaluate ultrasound image segmentation algorithms. The sonographic features can differentiate between various sized malignant and benign breast tumours. The clinical experiment can determine whether a tumour is benign or not, based on contour, shape, echogenicity and echo texture. Further study of the standardized sonographic features, especially the tumour contour and shape, will improve the positive predictive value and accuracy rate in breast tumour detection. The effectiveness of using this methodology is illustrated by evaluating image segmentation on breast ultrasound images. Via definite segmentation, the appreciated tumour shape and contour can be ascertained. Furthermore, this method can enhance the ability of ultrasound to distinguish between benign and malignant breast lesions.