Segmentation of cardiac fats based on Gabor filters and relationship of adipose volume with coronary artery disease using FP-Growth algorithm in CT scans

Segmentation of cardiac fats based on Gabor filters and relationship of adipose volume with coronary artery disease using FP-Growth algorithm in CT scans


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دانشگاه علوم پزشکی تبریز
دانشگاه علوم پزشکی تبریز

نویسندگان: احمد کشتکار , ناصر اصلان آبادی , بهروز خداداد , مهداد اسمعیلی , علی کاظمی

کلمات کلیدی: Cardiac Fat; Computed Tomography; Coronary Artery Disease; Textural Feature; GLCM; Segmentation; Gabor filters

نشریه: 19508 , 5 , 6 , 2020

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نویسنده ثبت کننده مقاله احمد کشتکار
مرحله جاری مقاله تایید نهایی
دانشکده/مرکز مربوطه دانشکده علوم نوین پزشکی
کد مقاله 72864
عنوان فارسی مقاله Segmentation of cardiac fats based on Gabor filters and relationship of adipose volume with coronary artery disease using FP-Growth algorithm in CT scans
عنوان لاتین مقاله Segmentation of cardiac fats based on Gabor filters and relationship of adipose volume with coronary artery disease using FP-Growth algorithm in CT scans
ناشر 6
آیا مقاله از طرح تحقیقاتی و یا منتورشیپ استخراج شده است؟ بلی
عنوان نشریه (خارج از لیست فوق) Biomedical Physics & Engineering Express
نوع مقاله Original Article
نحوه ایندکس شدن مقاله ایندکس شده سطح یک – ISI - Web of Science
آدرس لینک مقاله/ همایش در شبکه اینترنت

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Heart mediastinal and epicardial fat tissues are related to several adverse metabolic effects and cardiovascular risk factors, especially coronary artery disease (CAD). The manual segmentation of those fats is that the high dependence on user intervention and time-consuming analyzes. As a result, the automated measurement of cardiac fats could be considered as one of the most important biomarkers for cardiovascular risks in imaging and medical visualization by physicians. In this paper, we validate an automatic approach for the cardiac fat segmentation in non-contrast CT images then investigate the correlation between cardiac fat volume and CAD using the association rule mining algorithm. The pre-processing step includes threshold and contrast enhancement, the feature extraction step includes Gabor filter bank based on GLCM, the cardiac fat segmentation step is predicated on pattern recognition classification algorithms, and eventually, the step of investigating the relationship between cardiac fat volume and CAD is using FP-Growth algorithm. Experimental validation using CT images of two databases points to a good performance in cardiac fat segmentation. Experiments showed that the accuracy of the designed algorithm using the ensemble classifier with the best performance over other classifiers for the cardiac fat segmentation was 99.2%, with a sensitivity of 96.3% and a specificity of 99.8%. The results of using the FP-Growth algorithm showed that the low volume of epicardial (Confidence=0.6818, Lift=1.0626) and mediastinal (Confidence=0.6696, Lift =1.0436) fat are associated with healthy individuals and the high volume of epicardial (Confidence=0.8, Lift=2.2326) and mediastinal (Confidence=0.75, Lift =2.093) fat are related to individuals of CAD. As a result, cardiac fats can be used as a reliable biomarker tool in predicting the extent of CAD stenosis.

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نویسنده نفر چندم مقاله
احمد کشتکاردوم
ناصر اصلان آبادیچهارم
بهروز خدادادپنجم
مهداد اسمعیلیششم
علی کاظمیاول

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BPEX_6_5_055009.pdf1399/04/303499924دانلود