Design Of Infant Hip Development Auxiliary Detection System With Graf Criteria Applied

Computer-aided diagnosis and auxiliary decision-making system together with medical image processing and image recognition are heated research topics in recen-t years.Developmental disorder of hip is one of the common newborn bony diseases,and an auxiliary diagnosing system which aims at realising the automatic diagnosing of the disease has been designed and finally been completed with digital image processing techniques applied.The system,which consists of five parts including pre-processing of raw image,detecting of dislocated hip,segmenting for non-dislocated hip,feature extracting and final diagnosing,can automatically classify the developmental stage of a hip into maturity,dysplasia,severe dysplasia and dislocation by an infant hip ultrasound image.Firstly,procedures in raw image pre-processing extract the subimage containing the key acetabulum part from raw image.Secondly,the acetabulum subimage is input into a back propagation neural network that can pick out dislocated hips from all hips to be diagnosed due to significant differences between dislocated hips and non-dislocated hips.For hips that are consider as non-dislocated hips by neural network,segmenta-tion proceeds to segment them.Consider the great significance of segmentation in the system and the featured hip image,we propose to segment the image for twice with two different segmentation algorithms on basis of existing algorithms applied to get optimal segmented results in this paper.In the first segmentation,acetabulum image is segmented by algorithm specializing in dealing with images with intensity inhomo-geneity.Thereafter,problem of intensity inhomogeneity is overcame by linearly map-ping the evolved level set function in first segmentation into a new gray level image.In the segmentation,we propose to introduce shape prior energy term and regularity term into original CV model to guide the evolution of level set function in CV model and thus achieve optimal segmentation result.Finally,features are extracted by incor-porating the position information of shape prior level set function and then are used in final diagnosing step.Combined with clinical Graf criteria,final diagnosing quantifies the extracted features and receives parameters needed to make diagnosis.Accordingly,hips are classified into corresponding types and diagnostic result as well as suggestions are given by the system.The system designed in this paper has been validated on infant hip ultrasound images.While dislocated hips can be perfectly picked out,the diagnosis accuracy for non-dislocated hips still remains pretty high.Besides,the system can be featured as proficient?effective and high sensibility to dislocated hips,also,the system can effectively lighten the labor intensity of physicians in relating fields and thus contributes to the promotion of detection of infant hip disorders.