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Fully automatic ROI extraction and edge-based segmentation of radius and ulna bones from hand radiographs

Simu S., Lal S., Nagarsekar P., Naik A.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 4

718--732

Bone age is a reliable measure of person's growth and maturation of skeleton. The difference between chronological age and bone age indicates presence of endocrinological problems. The automated bone age assessment system (ABAA) based on Tanner and Whitehouse method (TW3) requires monitoring the growth of radius, ulna and short bones (phalanges) of left hand. In this paper, a detailed analysis of two bones in the bone age assessment system namely, radius and ulna is presented. We propose an automatic extraction method for the region of interest (ROI) of radius and ulna bones from a left hand radiograph (RUROI). We also propose an improved edge-based segmentation technique for those bones. Quantitative and qualitative results of the proposed segmentation technique are evaluated and compared with other state-of-the-art segmentation techniques. Medical experts have also validated the qualitative results of proposed segmentation technique. Experimental results reveal that these proposed techniques provide better segmentation accuracy as compared to the other state-of-the-art segmentation techniques.

Bone segmentation applying rigid bone position and triple shadow check method based on RF data

Doctor A., Vondenbusch B., Kozak J.

Acta of Bioengineering and Biomechanics

2011

Vol. 13, no. 2

3-11

Noninvasive 3D reconstruction of a bone requires very accurate 2D navigated scans of bone. The use of brightness-mode ultrasound seems to be promising, if some 2D scans of bone are obtained in a fully automatic manner. This paper presents a rapid and fully automatic method for segmenting bone in a standard 2D ultrasound image (B-mode image). The algorithm focuses on segmenting bone in the B-mode image using RF data of the image. The article introduces the signal-processing scheme designed based on RF data to automatically segment bone in the B-mode image. The segmentation accuracy was assessed by performing various tests for this algorithm for various locations of the limbs of the human body. The algorithm was tested for 120 images taken at different locations of limbs of the human body. The sensitivity of these tests was calculated to be 0.99 and specificity was found to be 1. The suggested segmentation approach provides a reliable means of detecting bone in B-mode image.