Department of Electronics and Communication, Central Institute of Technology Kokrajhar, Assam 783370, India; City Clinic and Research Centre, Kokrajhar, Assam, India.
Department of EEE, Indian Institute of Technology Guwahati, Assam, India.
Ultrasonics. 2021 Mar;111:106304. doi: 10.1016/j.ultras.2020.106304. Epub 2020 Nov 21.
Ultrasound image guided brain surgery (UGBS) requires an automatic and fast image segmentation method. The level-set and active contour based algorithms have been found to be useful for obtaining topology-independent boundaries between different image regions. But slow convergence limits their use in online US image segmentation. The performance of these algorithms deteriorates on US images because of the intensity inhomogeneity. This paper proposes an effective region-driven method for the segmentation of hyper-echoic (HE) regions suppressing the hypo-echoic and anechoic regions in brain US images. An automatic threshold estimation scheme is developed with a modified Niblack's approach. The separation of the hyper-echoic and non-hyper-echoic (NHE) regions is performed by successively applying patch based intensity thresholding and boundary smoothing. First, a patch based segmentation is performed, which separates roughly the two regions. The patch based approach in this process reduces the effect of intensity heterogeneity within an HE region. An iterative boundary correction step with reducing patch size improves further the regional topology and refines the boundary regions. For avoiding the slope and curvature discontinuities and obtaining distinct boundaries between HE and NHE regions, a cubic B-spline model of curve smoothing is applied. The proposed method is 50-100 times faster than the other level-set based image segmentation algorithms. The segmentation performance and the convergence speed of the proposed method are compared with four other competing level-set based algorithms. The computational results show that the proposed segmentation approach outperforms other level-set based techniques both subjectively and objectively.
超声图像引导脑外科手术（UGBS）需要一种自动且快速的图像分割方法。基于水平集和活动轮廓的算法已被证明在获取不同图像区域之间的拓扑独立边界方面非常有用。但是，由于强度不均匀性，其收敛速度较慢，限制了它们在在线 US 图像分割中的应用。由于 US 图像中的强度不均匀性，这些算法的性能会下降。本文提出了一种有效的区域驱动方法，用于分割脑 US 图像中的高亮（HE）区域，同时抑制低亮和无回声区域。提出了一种自动阈值估计方案，采用改进的 Niblack 方法。通过连续应用基于补丁的强度阈值和边界平滑来实现 HE 和非 HE（NHE）区域的分离。首先，执行基于补丁的分割，大致分离两个区域。该过程中的基于补丁的方法减少了 HE 区域内强度不均匀性的影响。通过迭代边界校正步骤（减小补丁大小）进一步改进区域拓扑并细化边界区域。为避免斜率和曲率不连续性并在 HE 和 NHE 区域之间获得明显的边界，应用了曲线平滑的三次 B 样条模型。与其他基于水平集的图像分割算法相比，该方法的速度快 50-100 倍。与其他四种基于水平集的算法相比，比较了所提出方法的分割性能和收敛速度。计算结果表明，所提出的分割方法在主观和客观上均优于其他基于水平集的技术。
