A Deep Convolutional Architectural Framework for Radiograph Image Processing at Bit Plane Level for Gender & Age Assessment

摘要

Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills. Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images. The permutation and combination of these features realized satisfactory accuracies for a set of limited groups. In this paper, assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images. A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process. Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image. A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female. The experimentations are conducted on the datasets of Radiological Society of North America (RSNA) of about 12442 images. Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%. Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%.