OBJECTIVES: This paper proposes a measurement method to quantify the abnormal characteristics of the broken parts of ribs using local texture and shape features in chest radiographs. METHODS: OUR MEASUREMENT METHOD COMPRISES TWO STEPS: a measurement area assignment and sampling step using a spline curve and sampling lines orthogonal to the spline curve, and a fracture-ness measurement step with three measures, asymmetry and gray-level co-occurrence matrix based measures (contrast and homogeneity). They were designed to quantify the regional shape and texture features of ribs along the centerline. The discriminating ability of our method was evaluated through region of interest (ROI) analysis and rib fracture classification test using support vector machine. RESULTS: The statistically significant difference was found between the measured values from fracture and normal ROIs; asymmetry (p < 0.0001), contrast (p < 0.001), and homogeneity (p = 0.022). The rib fracture classifier, trained with the measured values in ROI analysis, detected every rib fracture from chest radiographs used for ROI analysis, but it also classified some unbroken parts of ribs as abnormal parts (8 to 17 line sets; length of each line set, 2.998 ± 2.652 mm; length of centerlines, 131.067 ± 29.460 mm). CONCLUSIONS: Our measurement method, which includes a flexible measurement technique for the curved shape of ribs and the proposed shape and texture measures, could discriminate the suspicious regions of ribs for possible rib fractures in chest radiographs.
OBJECTIVES: This paper proposes a measurement method to quantify the abnormal characteristics of the broken parts of ribs using local texture and shape features in chest radiographs. METHODS: OUR MEASUREMENT METHOD COMPRISES TWO STEPS: a measurement area assignment and sampling step using a spline curve and sampling lines orthogonal to the spline curve, and a fracture-ness measurement step with three measures, asymmetry and gray-level co-occurrence matrix based measures (contrast and homogeneity). They were designed to quantify the regional shape and texture features of ribs along the centerline. The discriminating ability of our method was evaluated through region of interest (ROI) analysis and rib fracture classification test using support vector machine. RESULTS: The statistically significant difference was found between the measured values from fracture and normal ROIs; asymmetry (p < 0.0001), contrast (p < 0.001), and homogeneity (p = 0.022). The rib fracture classifier, trained with the measured values in ROI analysis, detected every rib fracture from chest radiographs used for ROI analysis, but it also classified some unbroken parts of ribs as abnormal parts (8 to 17 line sets; length of each line set, 2.998 ± 2.652 mm; length of centerlines, 131.067 ± 29.460 mm). CONCLUSIONS: Our measurement method, which includes a flexible measurement technique for the curved shape of ribs and the proposed shape and texture measures, could discriminate the suspicious regions of ribs for possible rib fractures in chest radiographs.