Comparison of image thresholding and clustering segmentation methods for understanding nutritional content of food images

Abstract

In a hospital, nutritionists and dietitians have to pay attention to how much food consumed by patients since it can affect the nutritional intake they get if patients leftover their meals. Usually, measuring leftover food is done by using the optical measurement, and it may have different prediction values as well when different observers evaluate leftover food. We propose an automatic estimation of the nutritional content of food by focusing on image segmentation from food images. Two algorithms are proposed: image thresholding and K-means++ clustering using HSV color transformation. The result evaluation function shows that image segmentation providing with two-steps thresholding can achieve better rather than using K-means++, with the number of accuracies is 95% and 53.44%, respectively. It concludes that by utilizing the improved image thresholding method, it is robust to identify the food area images that represent as nutritional content.