Comput Methods Programs Biomed - Computer-supported diagnosis for endotension cases in endovascular aortic aneurysm repair evolution.

Tópicos

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Resumo

An abdominal aortic aneurysm (AAA) is a localized abnormal enlargement of the abdominal aorta with fatal consequences if not treated on time. The endovascular aneurysm repair (EVAR) is a minimal invasive therapy that reduces recovery times and improves survival rates in AAA cases. Nevertheless, post-operation difficulties can appear influencing the evolution of treatment. The objective of this work is to develop a pilot computer-supported diagnosis system for an automated characterization of EVAR progression from CTA images. The system is based on the extraction of texture features from post-EVAR thrombus aneurysm samples and on posterior classification. Three conventional texture-analysis methods, namely the gray level co-occurrence matrix (GLCM), the gray level run length matrix (GLRLM), the gray level difference method (GLDM), and a new method proposed by the authors, the run length matrix of local co-occurrence matrices (RLMLCM), were applied to each sample. Several classification schemes were experimentally evaluated. The ensembles of a k-nearest neighbor (k-NN), a multilayer perceptron neural network (MLP-NN), and a support vector machine (SVM) classifier fed with a reduced version of texture features resulted in a better performance (Az=94.35?0.30), as compared to the classification performance of the other alternatives.

Resumo Limpo

abdomin aortic aneurysm aaa local abnorm enlarg abdomin aorta fatal consequ treat time endovascular aneurysm repair evar minim invas therapi reduc recoveri time improv surviv rate aaa case nevertheless postoper difficulti can appear influenc evolut treatment object work develop pilot computersupport diagnosi system autom character evar progress cta imag system base extract textur featur postevar thrombus aneurysm sampl posterior classif three convent textureanalysi method name gray level cooccurr matrix glcm gray level run length matrix glrlm gray level differ method gldm new method propos author run length matrix local cooccurr matric rlmlcm appli sampl sever classif scheme experiment evalu ensembl knearest neighbor knn multilay perceptron neural network mlpnn support vector machin svm classifi fed reduc version textur featur result better perform az compar classif perform altern

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