Quantification of food intake using food image analysis

Abstract

Measuring free-living peoples' food intake represents methodological and technical challenges. The Remote Food Photography Method (RFPM) involves participants capturing pictures of their food selection and plate waste and sending these pictures to the research center via a wireless network, where they are analyzed by Registered Dietitians to estimate food intake. Initial tests indicate that the RFPM is reliable and valid, though the efficiency of the method is limited due to the reliance on human raters to estimate food intake. Herein, we describe the development of a semi-automated computer imaging application to estimate food intake based on pictures captured by participants.

Figures

Fig. 1

Reference card detection. Top: Input image; Middle: Result of adaptive thresholding; Bottom: Response of the pattern detector.

Fig. 2

Corner detection and perspective correction. In raster scan order: Binary image after adaptive thresholding; Detected reference card; Corners of the card are marked on the image; Perspective correction of the card.

Fig. 3

Manual selection of the food region during the training stage. Top: The region of interest is outlined by the blue points that are clicked by the user. Bottom: Distribution of the color samples in RGB space.

Fig. 4

Automatic region segmentation and classification. Top: Mahalanobis distance of pixels to the food class shown in Fig. 3. Middle: Mahalanobis distance of the pixels to another food class. Bottom: Final segmented and classified region.

Fig. 5

Area-Volume relation. (a) Volume is linearly proportional to the area; that is, V2= A2 (V1/A1). (b) The volume depends on the shape (namely, top and bottom areas and the height) of the bowl, which is modeled as a cut cone.