تحلیل ریخت شناسی بطن های مغزی از روی تصاویر MRI با خوشه بند فازی بهینه شده توسط الگوریتم شاهین هریس (HHO) و ویژگی‌های کانال تجمیعی (ACF)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

2 گروه بیوالکتریک، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

چکیده

ریخت‌شناسی بطن‌ها در مطالعات هیدروسفالی، اسکیزوفرنی، تومورها، تروما، بیماری آلزایمر، بیماری پارکینسون، پیری و آتروفی جهت تشخیص بیماری‌های عصبی مانند سکته مغزی، زوال عقل و بیماری هانتینگتون مورد بررسی قرار گرفته می‌شود. در این پژوهش، روشی تمام خودکار جهت بررسی مورفومتریک بطن‌های مغزی ارائه می‌شود. بخش بندی بطن های مغزی گامی مهم جهت آشکارسازی لندمارک های بطن های مغزی است. همچنین، تخمین اولیه ناحیه بطنی مغزی می تواند در بخش بندی دقیق مرز بطن های مغزی مؤثر باشد. برای این منظور، خوشه بند فازی (FCM) بهینه شده با الگوریتم شاهین هریس (HHO) و ویژگی‌های کانال تجمیعی (ACF) به کار گرفته می‌شوند. جهت اندازه گیری شاخص‌های خطی بطن های مغزی شامل ایوانز، دودمی، دودمی-قدامی، دودمی-گیجگاهی و شماره هاکمن نیازمند مکان یابی تعدادی لندمارک بر روی تصاویر MRI هستیم. این فرآیند براساس ویژگی های هندسی بطن های مغزی و به کارگیری تبدیل هاف انجام می شود. نتایج پیاده‌سازی نشان می دهند که الگوریتم پیشنهادی با دقت 90%، حساسیت 82% و ویژگی 99% بهترین عمل کرد را در بخش بندی بطن های مغزی نسبت به سایر روش های مقایسه شده دارد. همچنین، نتایج نشان می دهند که دقت اندازه گیری الگوریتم پیشنهادی در شاخص‌های ذکر شده به ترتیب 98%، 77%، 78%، 78% و 94% است.

کلیدواژه‌ها

عنوان مقاله [English]

Morphological Analysis of Cerebral Ventricles using MRI Images with Fuzzy Clustering Optimized by Harris-Hawk Optimization (HHO) and Aggregate Channel Features (ACF)

نویسندگان [English]

  • SeyedHamed Esfahani 1
  • Mousa Shamsi 2
  • Ali Fahmi Jafargholkhanloo 2
  • Akbar Alipour Sifar 1
1 Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
2 Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
چکیده [English]

The morphology of the ventricles is used in studies of hydrocephalus, schizophrenia, tumors, trauma, Alzheimer's disease, Parkinson's disease, aging and atrophy to diagnose neurological diseases such as stroke, dementia and Huntington's disease. In this study, an automatic method for morphometric analysis of cerebral ventricles is proposed. Segmentation of cerebral ventricles is an important step to detect the landmarks of cerebral ventricles. Furthermore, the initial estimation of the cerebro-ventricular area can be effective in the proper segmentation of the ventricles. In this regard, Fuzzy C-Means Clustering (FCM) optimized with Harris-Hawk Optimization (HHO) and Aggregate Channel Features (ACF) are used. In order to measure the linear indices of cerebral ventricles, including Evans Index, Bicaudate Ratio, Bicaudate-Frontal Index, Bicaudate-Temporal Index, and Huckman Number, locating a number of landmarks on MRI images is required. This process is based on the geometric features of cerebral ventricles and the use of Hough transformation. The implementation results demonstrate that the proposed algorithm has the best performance with precision 90%, sensitivity 82%, specificity 99%, peak signal-to-noise ratio 77/11, dice similarity coefficient 86%, Jaccard index 75% and contour matching score 92% in the segmentation of cerebral ventricles among other compared methods. Additionally, the results show that the measurement accuracy of the proposed algorithm in the mentioned morphometric linear indices is 97%, 74%, 77%, 76% and 92% respectively.

کلیدواژه‌ها [English]

  • Aggregate Channel Features
  • Cerebrospinal Fluid Abnormalities
  • Harris-Hawks Optimizer
  • Hough Transformation
  • Indices of Cerebral Ventricles

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پژوهش های نظری و کاربردی هوش ماشینی
دوره 1، شماره 2
آذر 1402
صفحه 131-144

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