Int J Comput Assist Radiol Surg - Ultrasound texture-based CAD system for detecting neuromuscular diseases.

Tópicos

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Resumo

RPOSE: Diagnosis of neuromuscular diseases in ultrasonography is a challenging task since experts are often unable to discriminate between healthy and pathological cases. A computer-aided diagnosis (CAD) system for skeletal muscle ultrasonography was developed and tested for myositis detection in ultrasound images of biceps brachii.METHODS: Several types of features were extracted from rectangular and polygonal image regions-of-interest (ROIs), including first-order statistics, wavelet-based features, and Haralick's features. Features were chosen that are sensitive to the change in contrast and structure for pathological ultrasound images of neuromuscular diseases. The number of features was reduced by applying different sequential feature selection strategies followed by a supervised principal component analysis. For classification, two linear approaches were investigated: Fisher's classifier and the linear support vector machine (SVM) as well as the nonlinear [Formula: see text]-nearest neighbor approach. The CAD system was benchmarked on datasets of 18 subjects, seven of which were healthy, while 11 were affected by myositis. Three expert radiologists provided pre-classification and testing interpretations.RESULTS: Leave-one-out cross-validation on the training data revealed that the linear SVM was best suited for discriminating healthy and pathological muscle tissue, achieving 85/87?% accuracy, 90?% sensitivity, and 83/85?% specificity, depending on the radiologist.CONCLUSION: A muscle ultrasonography CAD system was developed, allowing a classification of an ultrasound image by one-click positioning of rectangular ROIs with minimal user effort. The applicability of the system was demonstrated with the challenging example of myositis detection, showing highly accurate results that were robust to imprecise user input.

Resumo Limpo

rpose diagnosi neuromuscular diseas ultrasonographi challeng task sinc expert often unabl discrimin healthi patholog case computeraid diagnosi cad system skelet muscl ultrasonographi develop test myositi detect ultrasound imag bicep brachiimethod sever type featur extract rectangular polygon imag regionsofinterest roi includ firstord statist waveletbas featur haralick featur featur chosen sensit chang contrast structur patholog ultrasound imag neuromuscular diseas number featur reduc appli differ sequenti featur select strategi follow supervis princip compon analysi classif two linear approach investig fisher classifi linear support vector machin svm well nonlinear formula see textnearest neighbor approach cad system benchmark dataset subject seven healthi affect myositi three expert radiologist provid preclassif test interpretationsresult leaveoneout crossvalid train data reveal linear svm best suit discrimin healthi patholog muscl tissu achiev accuraci sensit specif depend radiologistconclus muscl ultrasonographi cad system develop allow classif ultrasound imag oneclick posit rectangular roi minim user effort applic system demonstr challeng exampl myositi detect show high accur result robust imprecis user input

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