Infant Head Circumference Measurement Using Deep Learning Techniques

Infant's head circumference measurement and and its growth monitoring plays a crucial role in diagnosis the diseases which cause a deformation in the infant's head. Due to the fact that the contact measurement, which is performed using a tape measure and a caliper, has problems such as transmitting disease, infecting, not comfortable and disruption relaxing the baby, going to non-contact measurements is unavoidable. The purpose of this study is to provide a non-contact image based method for measuring the infant's head circumference. In this study, an algorithm was developed that calculates the infant's head circumference using an image taken above the infant's head and the scale index next to the head. The first step in calculating the head circumference is detecting and segmenting the baby's head in the image. In this regard, two the state of the art deep learning algorithms, MaskR-CNN and CRF-RNN, were compared in this study for accurately segmenting the infant's head. Subsequently, the head circumference pixels were detected by a fusion of the Canny edge detection and morphology algorithms. In the next step, the ground sample distance at suitable level was calculated using the scale tag in the image. Finally, the head circumference was calculated using the ground sample distance value and the number of pixels forming the head circumference. The evaluations show that the MaskR_CNN method with a total accuracy of 98.8% is a more appropriate method than the CRF-RNN method for detection and segmentation of the head in the image. Also by comparing the results of the proposed algorithm with the actual values obtained by strip meter on 10 images, it was found that the error of the proposed method is about 1 to 3%.