Paper Info
Title
Automatic selection of region of interest for radiographic texture analysis
Abstract
We have been developing radiographic texture analysis (RTA) for assessing osteoporosis and the related risk of fracture. Currently, analyses are performed on heel images obtained from a digital imaging device, the GE/Lunar PIXI, that yields both the bone mineral density (BMD) and digital images (0.2-mm pixels; 12-bit quantization). RTA is performed on the image data in a region-of-interest (ROI) placed just below the talus in order to include the trabecular structure in the analysis. We have found that variations occur from manually selecting this ROI for RTA. To reduce the variations, we present an automatic method involving an optimized Canny edge detection technique and parameterized bone segmentation, to define bone edges for the placement of an ROI within the predominantly calcaneus portion of the radiographic heel image. The technique was developed using 1158 heel images and then tested on an independent set of 176 heel images. Results from a subjective analysis noted that 87.5% of ROI placements were rated as "good". In addition, an objective overlap measure showed that 98.3% of images had successful ROI placements as compared to placement by an experienced observer at an overlap threshold of 0.4. In conclusion, our proposed method for automatic ROI selection on radiographic heel images yields promising results and the method has the potential to reduce intra- and inter-observer variations in selecting ROls for radiographic texture analysis.
Year
DOI
Venue
2007
10.1117/12.711531
Proceedings of SPIE
Keywords
Field
DocType
canny edge detection,parameterized bone segmentation,region of interest (ROI),peripheral instantaneous,x-ray imager (PIXI),radiographic texture analysis (RTA)
Calcaneus,Canny edge detector,Computer vision,Computer science,Edge detection,Digital image,Heel,Pixel,Digital imaging,Artificial intelligence,Region of interest
Conference
Volume
ISSN
Citations 
6514
0277-786X
1
PageRank 
References 
Authors
0.44
0
9
Name
Order
Citations
PageRank
Li Lan16918.36
Maryellen L. Giger239385.89
joel r wilkie311.11
Tamara J. Vokes421.55
Weijie Chen54918.02
Hui Li64515.48
tracy lyons710.44
Michael R Chinander8315.99
ann pham910.44