A two-dimensional extension of minimum cross entropy thresholding for the segmentation of ultrasound images
✍ Scribed by Y. Zimmer; R. Tepper; S. Akselrod
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 869 KB
- Volume
- 22
- Category
- Article
- ISSN
- 0301-5629
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✦ Synopsis
Segmentation is often an important step in medical image analysis. The local entropy is a possible variable for segmenting ultrasound images containing fluid surrounded by a soft tissue. A commonly used tool for image segmentation is thresholding. Recently, a new thresholding technique, known as "minimum cross entropy thresholding" (MCE), has been proposed. We present a multivariate extension of MCE in which the segmented variable (gray ievel) is replaced by a weighted combination of several image parameters. We propose to use a bivariate extension of MCE, which uses a linear combination of the gray level and the local entropy. The results obtained are demonstrated for ultrasound images of ovarian cysts.