Chuang Bo-I, Kuo Li-Chieh, Yang Tai-Hua, Su Fong-Chin, Jou I-Ming, Lin Wei-Jr, Sun Yung-Nien
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.
Department of Occupational Therapy, National Cheng Kung University, Tainan, Taiwan.
PLoS One. 2017 Oct 27;12(10):e0187042. doi: 10.1371/journal.pone.0187042. eCollection 2017.
Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area. In the experiments, the segmentation results have fewer errors, with respect to the ground truth, than contours drawn by regular users. The mean values of the absolute segmentation difference of the tendon and synovial sheath are 3.14 and 4.54 pixels, respectively. The average accuracy of pathological determination is 87.14%. The segmentation results are all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases are also a good reference for diagnosis according to the expert clinicians' opinions.
扳机指已成为一种普遍的疾病，严重影响职业活动和日常生活。超声成像常用于扳机指严重程度的临床诊断。由于图像特性的变化，传统方法无法有效地分割手指关节的肌腱结构。在本研究中，采用了一种基于自适应纹理的主动形状模型方法来分割肌腱和滑膜鞘。在分割过程中应用自适应权重，根据不同位置的图像特征调整能量项的贡献。然后根据分割后的肌腱区域的小波和共生纹理特征确定病理情况。在实验中，与普通用户绘制的轮廓相比，分割结果与真实情况的误差更少。肌腱和滑膜鞘的绝对分割差异的平均值分别为3.14像素和4.54像素。病理判定的平均准确率为87.14%。在74幅图像中，清晰边界和模糊边界情况的数据分割结果均可以接受。并且根据专家临床医生的意见，42例病例的症状分类对诊断也有很好的参考价值。
