Gray level co-occurrence and random forest algorithm-based gender determination with maxillary tooth plaster images. (1st June 2016)

• Record Type: Journal Article
• Authors: Akkoç, Betül; Arslan, Ahmet; Kök, Hatice
• Is Part Of: Computers in biology and medicine. Volume 73(2016)
• Page Start: 102
• Page End: 107
• Publication Date: 2016-06-01
• Subjects: Gender determination -- Feature extraction -- Image processing -- Random forest algorithm; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.04.003
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Gender is one of the intrinsic properties of identity, with performance enhancement reducing the cluster when a search is performed. Teeth have durable and resistant structure, and as such are important sources of identification in disasters (accident, fire, etc.). In this study, gender determination is accomplished by maxillary tooth plaster models of 40 people (20 males and 20 females). The images of tooth plaster models are taken with a lighting mechanism set-up. A gray level co-occurrence matrix of the image with segmentation is formed and classified via a Random Forest (RF) algorithm by extracting pertinent features of the matrix. Automatic gender determination has a 90% success rate, with an applicable system to determine gender from maxillary tooth plaster images. Graphical abstract: Highlights: In this study, GLCM and RF based gender determination has been performed. Maxillary tooth plaster model images have been used to determine gender. Automatic segmentation was carried out by using image processing methods. The features were extracted automatically without requiring any manual measurement. This study is a multi-disciplinary study.