Comput Math Methods Med - Automated detection of healthy and diseased aortae from images obtained by contrast-enhanced CT scan.

Tópicos

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Resumo

RPOSE: We developed the next stage of our computer assisted diagnosis (CAD) system to aid radiologists in evaluating CT images for aortic disease by removing innocuous images and highlighting signs of aortic disease.MATERIALS AND METHODS: Segmented data of patient's contrast-enhanced CT scan was analyzed for aortic dissection and penetrating aortic ulcer (PAU). Aortic dissection was detected by checking for an abnormal shape of the aorta using edge oriented methods. PAU was recognized through abnormally high intensities with interest point operators.RESULTS: The aortic dissection detection process had a sensitivity of 0.8218 and a specificity of 0.9907. The PAU detection process scored a sensitivity of 0.7587 and a specificity of 0.9700.CONCLUSION: The aortic dissection detection process and the PAU detection process were successful in removing innocuous images, but additional methods are necessary for improving recognition of images with aortic disease.

Resumo Limpo

rpose develop next stage comput assist diagnosi cad system aid radiologist evalu ct imag aortic diseas remov innocu imag highlight sign aortic diseasemateri method segment data patient contrastenhanc ct scan analyz aortic dissect penetr aortic ulcer pau aortic dissect detect check abnorm shape aorta use edg orient method pau recogn abnorm high intens interest point operatorsresult aortic dissect detect process sensit specif pau detect process score sensit specif conclus aortic dissect detect process pau detect process success remov innocu imag addit method necessari improv recognit imag aortic diseas

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