A colour-based image segmentation method for the measurement of masticatory performance in older adults

Lorenzo Scalise, Rachele Napolitano, Lorenzo Verdenelli, Susanna Spinsante, Giorgio Rappelli


Masticatory efficiency in older adults is an important parameter for the assessment of their oral health and quality of life. This study presents a measurement method based on the automatic segmentation of two-coloured chewing gum based on a K-means clustering algorithm. The solution proposed aims to quantify the mixed areas of colour in order to evaluate masticatory performance in different dental conditions. The samples were provided by ‘two-colour mixing’ tests, currently the most used technique for the evaluation of masticatory efficacy, because of its simplicity, low acquisition times and reduced cost. The image analysis results demonstrated a high discriminative power, providing results in an automatic manner and reducing errors caused by manual segmentation. This approach thus provides a feasible and robust solution for the segmentation of chewed samples. Validation was carried out by means of a reference software, demonstrating a good correlation (R2 = 0.64) and the higher sensitivity of the proposed method (+75 %). Tests on patients with different oral conditions demonstrated that the K-means segmentation method enabled the automatic classification of patients with different masticatory conditions, providing results in a shorter time period (20 chewing cycles instead of 50).

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DOI: http://dx.doi.org/10.21014/acta_imeko.v10i2.645