Ultrasound image segmentation based on a multi-parameter Gabor filter and multiscale local level set method
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摘要: 针对超声图像边缘较弱且不连续、图像灰度分布不均的特点，提出一种基于多方向、多频率的Gabor滤波融合多尺度水平集的边缘提取算法。将超声图像成像的不连续性看作随机方向的纹理，利用Gabor滤波的方向性进行不同角度的滤波，通过最大值融合多图像，得到待分割区域和背景之间的差异且最大程度地保留原图像信息的中间图像。同时，使用多中心频率的Gabor滤波核以满足超声图像复杂的频率分布特性，并通过均值融合的方式减弱噪声的影响。再针对融合图像边缘较弱且灰度变化不均的缺陷，改进传统的局部聚类水平集方法，采用不同方差大小的高斯卷积核来适应图像不同部分的灰度变化情况，通过均值融合构造多尺度能量函数。通过在增强图像上迭代改进后的多尺度水平集函数，获取最终边缘。为验证算法的有效性，对胃部超声图像进行测试，分割结果的相关性系数和敏感性系数分别达到了0.856和0.910，相比传统局部强度聚类水平集方法分别提升了20.7%和5%。实验结果表明，该算法可以显著提高超声图像边缘提取的连续性和准确性，有效降低因超声图像灰度不均和边缘较弱造成的过分割现象。Abstract: To address the weakness and discontinuity of the edges and the uneven distribution of gray in ultrasonic images, an improved edge extraction algorithm based on a multi-parameter Gabor filter and multiscale local level set method is proposed. With the grayscale inhomogeneity of ultrasound images being regarded as texture in different directions, the directionalities of the Gabor wavelet are adopted to filter at different angles. An intermediate image is obtained to isolate the difference between each region and the background, which will allow the retention of the original image by maximizing it with a fusion method. The Gabor filter kernel with multi-center frequency meets the complex frequency distribution characteristics of ultrasound images, and the mean fusion method is used to maximize the information in the image while reducing noise influence. For the edge of the ultrasound image is weak and the grayscale is uneven, the local intensity clustering level set method is improved. A Gaussian convolution kernel template is applied with different variance sizes to fit the grayscale changes in different parts of the image. Testing the ultrasound images of a stomach show that correlation coefficient and sensitivity coefficient reaches 0.856 and 0.910, respectively, which is a 20.7% and 5% improvement over the traditional LIC algorithm, respectively. This method can satisfy the system requirements where non-contact, online, real-time, higher precision and rapid speed strong anti-jamming and stabilization are needed.
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Key words:
- ultrasound image segmentation /
- edge extraction /
- Gabor filter /
- level set method
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图 2 超声图像分割结果图。 (a)~(c)超声原图像； (d)~(f)经Gabor滤波组处理增强后的图像； (g)~(i) 本文算法分割结果
Figure 2. Ultrasound image segmentation results. (a)~(c) Original ultrasound images; (d)~(f) images enhanced by Gabor filter groups; (g)~(i) segmentation results of the proposed algorithm.
图 3 几种不同方法的实验结果对比。 (a)本文所提算法 (b) LIC算法 (c) C-V算法 (d) Canny算子(e) 标准分割结果
Figure 3. Comparison of experimental results by different algorithms. (a) The proposed algorithm; (b) LIC; (c) C-V; (d) canny operator; (e) standard segmentation
表 1 本文算法与LIC算法和C-V算法效果比较
Table 1. Performance comparison when applying proposed algorithm, LIC algorithm and C-V algorithm
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表 2 本文算法与LIC算法和C-V算法运行时间比较
Table 2. Running time comparison when applying proposed algorithm, LIC algorithm and C-V algorithm
算法 迭代次数 运行时间/s 本文算法 50 13.12 LIC 50 8.29 C-V 50 1.63
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