# A Novel Color-Based Segmentation Method for the Objective Measurement of Human
## 2 Materials and Methods  
The study was performed on a sample of 50 subjects that were enrolled as students at the Dental School of Marche Polytechnic University, Ancona, Italy, from March 2019 to May 2019. They were young adults (mean age: \(24.3\pm 2.7\) years), homogenous for sex (25 males and 25 females), fully dentate (28 teeth totally, third molars were not considered, 14 occluding pairs), in good general health condition, with a decay missed filled teeth (DMFT) less than 4, without temporomandibular joint (TMJ) disorders and in angle class I. They were taken as the reference population and were recruited for their similar oral conditions. All participants volunteered for this study. No sample size calculation was made, but a sample size that was 2.5-fold greater than those used in the studies of [PERSON] et al. and [PERSON] et al. was enrolled [11; 20]. The study was performed in full accordance with the World Medical Association Declaration of Helsinki and it was approved by the Local Review Board of the Dental School (ODO-ID062019). All the tests were performed with the informed and written consent of each subject and in accordance with the abovementioned principles. The masticatory test was performed using the two-colored chewing gum mixing test as described by [PERSON] et al., which involves the use of two-colored chewing gum (Hue-check Gum(r), Orophys GmbH, Muri b. Bern, Switzerland) [8; 10; 11; 13]. Each sample was chewed for 5, 10, 20, 30, and 50 chewing cycles. A rest interval of 1 min was set between each masticatory test in order to avoid muscle strain. Briefly, blue and pink gums were roughly stuck together using moderate strength. Specimens were placed on the tongue of the participants who were asked to chew in their usual way. Participants were allowed to change the chewing side during the test. An operator counted the masticatory strokes and at the defined chewing cycle, the operator asked the participants to stop. Boluses were retrieved from the mouth of the participants and gently dabbed with a paper towel in order to eliminate the possible excess of saliva. Specimens were collected by inserting them between two sheets of transparent plastic and flattened to a standard thickness of 1 mm. Figure 1 shows the used chewing gum and the boluses after 5, 10, 20, 30, and 50 masticatory cycles. Both sides of each bolus were photographed, and, one side at a time, all images were post-processed using the segmentation algorithm developed by the Department of Industrial Engineering and Mathematical Sciences of the Polytechnic University of Marche, Ancona, Italy. The algorithm automatically analyzes the images and gives as output as the percentage of the mixed portion of the sample. The proposed algorithm exploits the k-means clustering method and the analysis is carried out by measuring the areas of different colors in pixels. In the laboratory tests, accurate digitization of each sample was performed. The procedure was divided into two main phases:  
Figure 1: Chewing gum specimens: (**a**) the two colored chewing gums; (**b**) the two colored chewing gums stuck together; (**c**–**g**) chewing gum boluses after 5, 10, 20, 30, and 50 masticatory cycles, respectively; (**h**–**l**) flattened chewing gum boluses after 5, 10, 20, 30, and 50 masticatory cycles, respectively. An acquisition phase, in which the images of both sides of the boluses were captured using a micro-camera (FREDI HD MINI WIFI 1080P, Shenzhen Jinbaixun Technology Co., Ltd., Shenzhen, China) put at a standard distance of 10 cm. Each wafer was acquired on a dark background in order to make it easier to separate the different colors. 2. A processing phase, which was necessary to classify an image into different color groups. A PC (Intel(r) Core TM i7-6700 HQ, 2.6 GHz, 16.0 GB) with Windows 10 Home was used as the electronic equipment to process the acquired data and MATLAB(r) R2019b (MatLab, MathWorks, Natick, MA, USA) software to enable the color-based segmentation.