With the growing concerns about young and adult people obesity and other diet-related health problems, various dietary assessment methods for prevention and intervention are being developed to inform the general populace of health risks of being overweight and encourage healthy eating patterns. These meth- ods include recording, cataloging and analyzing daily dietary details to monitor energy and nutrient intakes. With the widespread use of digital cameras and camera phones, one possible means of improving dietary assessment is through photographing foods and inputting these images into a system that can determine the nutrient content of foods in the images. However, one of the critical issues in such image-based dietary assessment tools is the low accuracy and consistency in estimating the sizes and weight of the food portion in the image. Food weight estimation from images remains a challenging problem, extremely important be- cause without precise weight estimation, food caloric content cannot be estimated accurately from an image. In this thesis, we investigate an accurate food weight estimation system based on image processing by using standardized cutlery, es- pecially chopsticks. Our system requires the user to take only a single image from the top with the chopsticks in the picture. Using several image processing techniques, and EXIF data of the image, the system automatically estimates the diameter and the height of the food container. Then, given the food type, the system combines the information about the container diameter, height and the food type to provide the weight of the food in the image. Our experiments show tenable results from the system which achieved an average relative error rate of 6.87% over the testing food images. Furthermore, our system constructs a per- sonal food journal that records the details of consumed meals. We developed an Android application through which users can upload their food images and view the food weight estimate, and their personal food journal.