Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification  

作　　者：Dalel Ben Ismail Hela Elmannai Souham Meshoul Mohamed Saber Naceur 

机构地区：[1]LTSIRS LR20ES06,Institut National des Sciences Appliquées et de Technologie INSAT,Universitéde Carthage,Tunisia [2]Department of Information Technology,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh 11671,Saudi Arabia

出　　处：《Intelligent Automation & Soft Computing》2023年第2期2219-2236,共18页智能自动化与软计算（英文）

基　　金：supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R196);Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：Imagery assessment is an efficient method for detecting craniofacial anomalies.A cephalometric landmark matching approach may help in orthodontic diagnosis,craniofacial growth assessment and treatment planning.Automatic landmark matching and anomalies detection helps face the manual labelling lim-itations and optimize preoperative planning of maxillofacial surgery.The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classification.First,the Active Appearance Model(AAM)was used for the matching process.This pro-cess was achieved by the Ant Colony Optimization(ACO)algorithm enriched with proximity information.Then,the maxillofacial anomalies were classified using the Support Vector Machine(SVM).The experiments were conducted on X-ray cephalograms of 400 patients where the ground truth was produced by two experts.The frameworks achieved a landmark matching error(LE)of 0.50±1.04 and a successful landmark matching of 89.47%in the 2 mm and 3 mm range and of 100%in the 4 mm range.The classification of anomalies achieved an accuracy of 98.75%.Compared to previous work,the proposed approach is simpler and has a comparable range of acceptable matching cost and anomaly classification.Results have also shown that it outperformed the K-nearest neigh-bors(KNN)classifier.

关 键 词：Maxillofacial anomalies cephalometric landmarks similarity chi-square distance quadratic assignment problem ant colony optimization SVM 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]