Segmentation and Recognition of Multi-Food Meal Images for Carbohydrate Counting

Anthimopoulos, Marios; Dehais, Joachim Blaise; Diem, Peter; Mougiakakou, Stavroula (November 2013). Segmentation and Recognition of Multi-Food Meal Images for Carbohydrate Counting. In: 13th IEEE International Conference on BioInformatics and BioEngineering system (IEEE BIBE2013). Chania, Greece. 10.-13.11.2013.

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In this paper, we propose novel methodologies for the automatic segmentation and recognition of multi-food images. The proposed methods implement the first modules of a carbohydrate counting and insulin advisory system for type 1 diabetic patients. Initially the plate is segmented using pyramidal mean-shift filtering and a region growing algorithm. Then each of the resulted segments is described by both color and texture features and classified by a support vector machine into one of six different major food classes. Finally, a modified version of the Huang and Dom evaluation index was proposed, addressing the particular needs of the food segmentation problem. The experimental results prove the effectiveness of the proposed method achieving a segmentation accuracy of 88.5% and recognition rate equal to 87%

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Health and Nutrition 04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition
Graduate School:	Graduate School for Cellular and Biomedical Sciences (GCB)
UniBE Contributor:	Anthimopoulos, Marios, Dehais, Joachim Blaise, Diem, Peter, Mougiakakou, Stavroula
Subjects:	600 Technology > 610 Medicine & health 600 Technology > 620 Engineering
Language:	English
Submitter:	Stavroula Mougiakakou
Date Deposited:	16 Jun 2014 17:17
Last Modified:	05 Dec 2022 14:34
URI:	https://boris.unibe.ch/id/eprint/52909