基于自动种子区域生长的超声B图像缺陷分割方法

提出一种基于自动种子区域生长的超声图像缺陷分割方法。首先使用最大类间方差（Otsu）分割法对超声B图像进行一次预分割;其次寻到绝对背景区,并且在此区域内自动设置种子起始点;然后利用区域生长法将缺陷从背景中分割;最后通过数字形态学降噪法来进一步提高缺陷的识别度。实验结果表明:该方法不仅能准确地分割出缺陷,且具有较好的缺陷边界信息,提高了对超声B图像的处理效率,有效地抑制了大部分图像噪声。

Ultrasonic B Image Defect Segmentation Method Based on Automatic Seeded Region Growing

An ultrasound image defect segmentation algorithm based on automatic seeded region growing was proposed. First, the pre-segmentation of the ultrasound B image was performed by the Otsu method. Next, the seed starting points were set automatically by seeking the absolute background area. Then, the defects were segmented from the background area by regional growth algorithms. Finally, the defect recognition was further improved by digital morphological noise reduction method. Experimental results shown that the defects were segmented effectively by the proposed algorithm and good defect boundary information has also been provided. The efficiency of processing ultrasonic B images is improved and noise of B-scan image is mostly suppressed effectively.