1 August 2011 Fast and fully automatic phalanx segmentation using a grayscale-histogram morphology algorithm
Chi-Wen Hsieh, Din-Yuen Chan, Tzu-Chiang Liu, Chui-Mei Tiu, Tai-Lang Jong, Chih-Yen Chen
Author Affiliations +
Abstract
Bone age assessment is a common radiological examination used in pediatrics to diagnose the discrepancy between the skeletal and chronological age of a child; therefore, it is beneficial to develop a computer-based bone age assessment to help junior pediatricians estimate bone age easily. Unfortunately, the phalanx on radiograms is not easily separated from the background and soft tissue. Therefore, we proposed a new method, called the grayscale-histogram morphology algorithm, to segment the phalanges fast and precisely. The algorithm includes three parts: a tri-stage sieve algorithm used to eliminate the background of hand radiograms, a centroid-edge dual scanning algorithm to frame the phalanx region, and finally a segmentation algorithm based on disk traverse-subtraction filter to segment the phalanx. Moreover, two more segmentation methods: adaptive two-mean and adaptive two-mean clustering were performed, and their results were compared with the segmentation algorithm based on disk traverse-subtraction filter using five indices comprising misclassification error, relative foreground area error, modified Hausdorff distances, edge mismatch, and region nonuniformity. In addition, the CPU time of the three segmentation methods was discussed. The result showed that our method had a better performance than the other two methods. Furthermore, satisfactory segmentation results were obtained with a low standard error.
©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)
Chi-Wen Hsieh, Din-Yuen Chan, Tzu-Chiang Liu, Chui-Mei Tiu, Tai-Lang Jong, and Chih-Yen Chen "Fast and fully automatic phalanx segmentation using a grayscale-histogram morphology algorithm," Optical Engineering 50(8), 087007 (1 August 2011). https://doi.org/10.1117/1.3613940
Published: 1 August 2011
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Image segmentation

Bone

Optical engineering

Digital filtering

Tissues

Radiology

Image processing

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