title: Modeling skeletal bone development with hidden Markov models.
abstract: This paper presents a tool for automatic assessment of skeletal bone age according to a modified version of the Tanner and Whitehouse (TW2) clinical method. The tool is able to provide an accurate bone age assessment in the range 0-6 years by processing epiphysial/metaphysial ROIs with image-processing techniques, and assigning TW2 stage to each ROI by means of hidden Markov models. The system was evaluated on a set of 360 X-rays (180 for males and 180 for females) achieving a high success rate in bone age evaluation (mean error rate of 0.41±0.33 years comparable to human error) as well as outperforming other effective methods. The paper also describes the graphical user interface of the tool, which is also released, thus to support and speed up clinicians' practices when dealing with bone age assessment.
authors.affiliation: The Department of Electrical, Electronics and Computer Engineering, University of Catania, 95125 Catania, Italy. Electronic address: dgiordan@dieei.unict.it.
authors.forename: Daniela
authors.initials: D
authors.lastname: Giordano
authors.affiliation: The Department of Electrical, Electronics and Computer Engineering, University of Catania, 95125 Catania, Italy. Electronic address: kavasidis@dieei.unict.it.
authors.forename: Isaak
authors.initials: I
authors.lastname: Kavasidis
authors.affiliation: The Department of Electrical, Electronics and Computer Engineering, University of Catania, 95125 Catania, Italy. Electronic address: cspampin@dieei.unict.it.
authors.forename: Concetto
authors.initials: C
authors.lastname: Spampinato
mesh_terms: D000365:Age Determination by Skeleton / Q000379:methods*; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D005260:Female; D050276:Hand Bones / Q000000981:diagnostic imaging* / Q000254:growth & development*; D006801:Humans; D007231:Infant, Newborn; D000069550:Machine Learning; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods
keywords: Image processing; Machine learning; Medical imaging