Paper Info
Title
Automatic segmentation and classification of neonatal hips according to Graf's sonographic method: A computer-aided diagnosis system.
Abstract
Graf’s technique for hip ultrasonography (US) assessment is a well-known and universally accepted method for the evaluation of neonatal hips. However, the training process is long and requires supervision until the evaluator achieves expertise. Computer-based segmentation results may be helpful to less experienced evaluators in the discrimination of anatomical structures and the classification of hip US images. The aim of this research was to construct a fully automatic computer-aided diagnosis (CAD) system for the classification of newborn hip US images to be used as a guide in the training and evaluation processes. The proposal included a cascaded framework that utilizes particle swarm optimization (PSO) and statistical level set (SLS) method of segmentation. The location of the initial contour and the region of interest (ROI) were determined using the PSO method. The SLS method was applied in the ROI in order to segment the critical anatomical structures with great success. The proposed tool utilized the knowledge of these anatomical structures to draw lines and define the alpha and beta angles. The specificity rate of the proposed system in the classification of 164 randomly selected hip ultrasound images was 98.57%. The proposed CAD system is a very promising tool for the segmentation and classification of neonatal hip US images according to Graf’s basic types: normal (Type I), mild dysplasia (Type IIa, IIb) and severe dysplasia (Type IIc, D).
Year
DOI
Venue
2019
10.1016/j.asoc.2019.105516
Applied Soft Computing
Keywords
Field
DocType
Graf’s hip ultrasound,Particle swarm optimization,Active contours,Automatic segmentation of ultrasound
CAD,Particle swarm optimization,Pattern recognition,Segmentation,Computer-aided diagnosis,Level set,Artificial intelligence,Cad system,Anatomical structures,Region of interest,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
82
1568-4946
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Hasan Basri Sezer100.34
Aysun Sezer253.51