این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Artificial Intelligence and Data Mining، جلد ۱۰، شماره ۳، صفحات ۳۶۱-۳۷۲
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی A Novel Classification and Diagnosis of Multiple Sclerosis Method using Artificial Neural Networks and Improved Multi-Level Adaptive Conditional Random Fields
چکیده انگلیسی مقاله Due to the small size, low contrast, variable position, shape, and texture of multiple sclerosis lesions, one of the challenges of medical image processing is the automatic diagnosis and segmentation of multiple sclerosis lesions in Magnetic resonance images. Early diagnosis of these lesions in the first stages of the disease can effectively diagnose and evaluate treatment. Also, automated segmentation is a powerful tool to assist professionals in improving the accuracy of disease diagnosis. This study uses modified adaptive multi-level conditional random fields and the artificial neural network to segment and diagnose multiple sclerosis lesions. Instead of assuming model coefficients as constant, they are considered variables in multi-level statistical models. This study aimed to evaluate the probability of lesions based on the severity, texture, and adjacent areas. The proposed method is applied to 130 MR images of multiple sclerosis patients in two test stages and resulted in 98% precision. Also, the proposed method has reduced the error detection rate by correcting the lesion boundaries using the average intensity of neighborhoods, rotation invariant, and texture for very small voxels with a size of 3-5 voxels, and it has shown very few false-positive lesions. The proposed model resulted in a high sensitivity of 91% with a false positive average of 0.5.
کلیدواژه‌های انگلیسی مقاله Image segmentation, Automatic Detection, Multiple Sclerosis, Adaptive Multi-Level Conditional Random Fields (AMCRF), Artificial Neural Network
نویسندگان مقاله Seyedeh R. Mahmudi Nezhad Dezfouli |
Department of Computer Engineering, Islamic Azad University, Dezful Branch, Dezful, Iran

Y. Kyani |
Department of Computer Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran

Seyed A. Mahmoudinejad Dezfouli |
edical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran.

نشانی اینترنتی https://jad.shahroodut.ac.ir/article_2345_977c6c30a219a50871ea560763e4ad60.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات