Authors:
Peng Guo
1
;
Ronald J. Stanley
1
;
Justin G. Cole
2
;
Jason Hagerty
3
and
William V. Stoecker
2
Affiliations:
1
Missouri University of Science & Technology, United States
;
2
Stoecker & Associates, United States
;
3
Missouri University of Science & Technology and Stoecker & Associates, United States
Keyword(s):
Pillbox Image, Color Recognition, Support Vector Machine, Image Processing.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Medical Image Applications
;
Segmentation and Grouping
Abstract:
Patients, their families and caregivers routinely examine pills for medication identification. Key pill information includes color, shape, size and pill imprint. The pill can then be identified using an online pill database. This process is time-consuming and error prone, leading researchers to develop techniques for automatic pill identification. Pill color may be the pill feature that contributes most to automatic pill identification. In this research, we investigate features from two color planes: red, green and blue (RGB), and hue saturation and value (HSV), as well as chromaticity and brightness features. Color-based classification is explored using MatLab over 2140 National Library of Medicine (NLM) Pillbox reference images using 20 feature descriptors. The pill region is extracted using image processing techniques including erosion, dilation and thresholding. Using a leave-one-image-out approach for classifier training/testing, a support vector machine (SVM) classifier yield
ed an average accuracy over 12 categories as high as 97.90%.
(More)