A fuzzy-based growth model with principle component analysis selection for carpal bone-age assessment.
There are two well-known methods to assess bone age, the Greulich-Pyle method and the Tanner-Whitehouse method, which both utilize the hand radiogram to make bone-age assessment to assist medical doctors to identify the growth status of children. Basically, the morphology of bones could be evaluated to quantitatively describe the maturity. The study extracted the morphology of carpal bones and applied the fuzzy theory with principle component analysis to estimate the maturity of skeleton. Five geometric features of the carpals were extracted including the bone area, the area ratio, and the bone contour of the carpals. In order to analyze these features, the principle component analysis and the statistical correlation combined with three different types of procedure were used to construct a growth model of carpals. Eventually, the results of the three types of procedure with fuzzy rules can construct a bone-age assessment system to identify the maturity of children. The study shows that the proposed model based on fuzzy rule has an accuracy rate above 89% in Type-I and II, and above 87% in Type-III within a tolerance of 1.5 years.
Hsieh
Chi-Wen
CW
Department of Electrical Engineering, National Chiayi University, No. 300 Syuefu Road, Chiayi City, 60004, Taiwan, ROC. chiwen@mail.ncyu.edu.tw
Liu
Tzu-Chiang
TC
Jong
Tai-Lang
TL
Tiu
Chui-Mei
CM
D000365:Age Determination by Skeleton / Q000379:methods*; D002348:Carpal Bones / Q000000981:diagnostic imaging / Q000254:growth & development*; D002648:Child; D002675:Child, Preschool; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007223:Infant; D008297:Male; D025341:Principal Component Analysis; D012727:Sex Characteristics
Comput Med Imaging Graph. 1995 May-Jun;19(3):251-9
7641169
Pediatr Int. 2009 Aug;51(4):453-9
19674357
IEEE Trans Med Imaging. 2001 Aug;20(8):715-29
11513023
Zhonghua Yi Xue Za Zhi (Taipei). 2001 Apr;64(4):203-8
11458757
Arch Dis Child. 1983 Oct;58(10):767-76
6639123
Comput Med Imaging Graph. 1995 Nov-Dec;19(6):465-72
8796967
IEEE Trans Med Imaging. 1993;12(1):44-9
18218390
J Digit Imaging. 2004 Sep;17(3):175-88
15175931
IEEE Trans Med Imaging. 1991;10(4):616-20
18222868
Comput Med Imaging Graph. 2007 Jun-Jul;31(4-5):299-310
17369018
Comput Med Imaging Graph. 2000 Sep-Oct;24(5):297-307
10940607
Med Biol Eng Comput. 2007 Mar;45(3):283-95
17242901
IEEE Trans Biomed Eng. 2008 May;55(5):1463-76
18440892
IEEE Trans Med Imaging. 1989;8(1):64-9
18230501
IEEE Trans Med Imaging. 2009 Jan;28(1):52-66
19116188
Comput Med Imaging Graph. 2008 Dec;32(8):678-84
18835130
Clin Radiol. 1986 Mar;37(2):119-21
3698492
Chin Med J (Engl). 2007 May 5;120(9):767-70
17531116