Open Access

ARTICLE

Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification

Dalel Ben Ismail¹, Hela Elmannai^2,*, Souham Meshoul², Mohamed Saber Naceur¹

1 LTSIRS LR20ES06, Institut National des Sciences Appliquées et de Technologie INSAT, Université de 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

* Corresponding Author: Hela Elmannai. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 2219-2236. https://doi.org/10.32604/iasc.2023.028944

Received 22 February 2022; Accepted 21 April 2022; Issue published 19 July 2022

Abstract

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 limitations 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 process 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 neighbors (KNN) classifier.

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Keywords

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