Automatic diagnosis of pectus carinatum for children based on the improved Haller index.
10.7507/1001-5515.201712024
- Author:
Honglei JI
1
;
Junchen WANG
1
;
Chenghao CHEN
2
;
Qi ZENG
3
Author Information
1. School of Mechanical Engineering & Automation, Beihang University, Beijing 100191, P.R.China；.
2. Department of Thoracic Surgery of Beijing Children's Hospital, Capital Medical University, Beijing 100045, P.R.China.
3. Department of Thoracic Surgery of Beijing Children's Hospital, Capital Medical University, Beijing 100045, P.R.China.zengqi-1@163.com.
- Publication Type:Journal Article
- Keywords:
computed tomography image;
computer-aided diagnosis;
cubic B-spline curve;
elliptic curve;
pectus carinatum
- From:
Journal of Biomedical Engineering
2018;35(4):571-577
- CountryChina
- Language:Chinese
-
Abstract:
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.
