# A Novel Color-Based Segmentation Method for the Objective Measurement of Human
## 1 Introduction  
Human mastication is a complex biomechanical process that is aimed at properly preparing food for swallowing and digestion [1]. Quality of life, general health, and social relations could be influenced by impaired mastication. Furthermore, the assessment of mastication could also be able to provide information about the efficiency and status of many oDuring their daily office practice, clinicians may be required to evaluate patient mastication. Thus, it is necessary to have an objective method that is able to assess it. The latter not only should aim at evaluating oral function but also at providing information about patient disability and a real indication for prosthetic rehabilitation. Over the past few years, several methods have been proposed in order to assess masticatory performance (MP). MP is defined as the individual ability to grind a test food after a fixed number of chewing strokes [3]. Most of the studies on MP used communution tests in order to study the particles of both artificial and natural food by sieving the communuted food [4]. Even though these systems are considered to be the gold standard in assessing MP, patient discomfort and high costs hinder their use. Besides the need for expensive and special equipment, particles of the specimens have to be completely removed from the mouth after the communution procedure, which could be very difficult in the case of small fragments. Furthermore, dysphagia patients risk aspirating these particles [5]. Previous studies showed that the results of color-mixing ability tests highly correlate with those of communution ones in assessing MP and proposed that color-mixing tests should be used on patients with chewing deficiencies [6; 7]. In 1991 and 1995, [PERSON] and [PERSON] proposed and described for the first time a new method for the study of MP. This test consisted of the chewing of two-colored chewing gum for 10, 20, 40, 60, 80, and 100 strokes. The specimens were then scored visually into a 1 to 5 color mixture scale, in accordance with a reference scale established in pilot tests [8; 9]. In 1999, [PERSON] understood the need for simple objective tests for the assessment of oral function. Chewing gum containing two different colors was chewed. Once the bolus was removed from the oral cavity, it was flattened, and a digital image was taken. It was concluded that flattening the bolus before the assessment provided a more reliable evaluation than observing the bolus in its raw state. Nevertheless, the subjective evaluation had similar accuracy to image processing and the author concluded that subjective assessment was enough [10]. In 2007, [PERSON] et al. tested the reliability and reproducibility of quantitative data obtained from the two-color chewing gum test and found that the visual assessment of the specimens was less reliable than computerized analysis. The authors concluded that the digital evaluation of the color mixing degree of two-color chewing gum was a precise and reliable method to assess MP [11]. As the computerized image analysis was carried out using a commercially available software package, in 2013, [PERSON] et al. proposed novel software for the quantitative assessment of MP [12]. The two-color chewing gum test and the dedicated software were validated in 2015 [13]. Even though the method proposed by [PERSON] et al. is considered to be the gold standard among mixing ability tests, the digital analysis of boluses derived from its application is still controversial. Different software packages were proposed in order to analyze the specimens, highlighting the great variability of those methods and the need to find the best possible one to use both in clinical and research settings [12; 14; 15; 16; 17; 18]. Unfortunately, the software may introduce errors due to the manual resizing and segmentation of bolus images and the difficulty in distinguishing hue variations between the gum and the background, especially for well-mixed chewing gum.