Pectus carinatum (PC) is one of the most common chest wall anomalies, which is characterized by the protrusion of the anterior chest wall including the sternum and adjacent costal cartilages. Mildly patients suffer from mental problems such as self-abasement, while severely suffering patients are disturbed by significant cardiopulmonary symptoms. The traditional Haller index, which is widely used clinically to evaluate the severity of PC, is deficient in diagnosis efficiency and classification. This paper presents an improved Haller index algorithm for PC: first, the contour of the patient chest in the axial computed tomography (CT) slice where the most convex thorax presents is extracted; and then a cubic B-spline curve is employed to fit the extracted contour followed by an eclipse fitting procedure; finally, the improved Haller index and the classification index are automatically calculated based on the analytic curves. The results of CT data analysis using 22 preoperative and postoperative patient CT datasets show that the proposed diagnostic index for PC can diagnose and classify PC patients correctly, which confirms the feasibility of the evaluation index. Furthermore, digital measurement techniques can be employed to improve the diagnostic efficiency of PC, achieving one small step towards the computer-aided intelligent diagnosis and treatment for pediatric chest wall malformations.
鸡胸是最常见的儿童胸壁畸形之一,表现为部分胸骨及与之相连的肋软骨向前突起,轻者心理健康受损,重者肺部等身体机能受损。临床上广泛使用的鸡胸评价指标——Haller 指数,其在鸡胸诊断效率以及分型上存在一定不足,因此本文提出一种改进型 Haller 指数及其自动化测量算法。首先提取患者电子计算机断层扫描(CT)图像中胸骨最凸横断面的外胸廓像素点集合;然后将其分别拟合为三次 B 样条曲线和椭圆曲线;最后基于解析曲线自动计算得出改进型 Haller 指数及分型指数。通过对 22 组患者术前术后 CT 数据的实验分析结果表明,本文提出的改进型 Haller 指数不仅可以正确诊断鸡胸,而且可用于对不同程度的鸡胸进行分型,证实了该评价指标的可行性,同时由于采用计算机自动测量技术,提高了鸡胸诊断效率,为计算机辅助个性化儿童胸壁畸形诊疗技术奠定了基础。.
Keywords: computed tomography image; computer-aided diagnosis; cubic B-spline curve; elliptic curve; pectus carinatum.