A calculation method of plant similarity giving consideration to different plant features.

A method to compute the similarity between different plants is proposed, using features of a plant׳s topological structure and peripheral contour, as well as its geometry. The topological structures are described using tree graphs, and their similarity can be calculated based on the edit distance of these graphs. The peripheral contour of a plant is abstracted by its three-dimensional convex hull, which is projected in several directions. The similarity of the different projections is calculated by an algorithm to compute the similarity of two-dimensional shapes. The similarity of the geometrical detail is computed by considering the geometrical properties of different level branches. Finally the overall similarity between different plants is calculated by combining these different similarity measures. The validity of proposed method is evaluated by detailed experiments.

Skeletal bone age assessments for young children based on regression convolutional neural networks.

Pediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for in-vestigations pertaining to genetic disorders, hormonal complications and abnormalities in the skeletal system maturity of children. Conventional methods dating back to 1950 were often tedious and suscep-tible to inter-observer variability, and preceding attempts to improve these traditional techniques have inadequately addressed the human expert inter-observer variability so as to significantly refine bone age evaluations. In this paper, an automated and efficient approach with regression convolutional neu-ral network is proposed. This approach automatically exploits the carpal bones as the region of interest (ROI) and performs boundary extraction of carpal bones, then based on the regression convolutional neural network it evaluates the skeletal age from the left hand wrist radiograph of young children. Experiments show that the proposed method achieves an average discrepancy of 2.75 months between clinical and automatic bone age evaluations, and achieves 90.15% accuracy within 6 months from the ground truth for male. Further experimental results with test radiographs assigned an accuracy within 1 year achieved 99.43% accuracy.