مجله ماشین بینایی و پردازش تصویر

مجله ماشین بینایی و پردازش تصویر

ارائه‌ی الگوریتم جدید مبتنی بر خوشه‌بند فازی جهت بخش‌بندی بافت‌های مغزی توام با تصحیح بایاس از روی تصاویر MRI

نوع مقاله : مقاله پژوهشی

نویسندگان
1 استادیار، گروه علوم مهندسی، دانشکده فناوری‌های نوین، دانشگاه محقق اردبیلی، نمین، ایران
2 گروه بیوالکتریک، دانشکده‌ی مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران
چکیده
بخش‌بندی تصاویر MR گامی ضروری در برنامه‌ریزی جراحی و درمان است. غیریکنواختی شدت، شناخته شده تحت عنوان بایاس، یک آرتیفکت رایج در تصاویر MR بوده که باعث ایجاد چالش‌های در فرآیند بخش‌بندی بافت‌های مغزی می‌شود. این آرتیفکت به‌دلیل عواملی نظیر غیریکنواختی میدان مغناطیسی، حساسیت کویل فرکانس رادیویی و آرتیفکت‌های اسکنر رخ داده که باعث ایجاد غیریکنواختی شدت داخل نواحی بافت همگن تصاویر مغزی می‌شود. جهت حل چالش مطرح شده، رویکرد جدید خوشه‌بند فازی مبتنی بر الگوریتم گستافسون-کسل ارائه می‌شود که با تخمین بایاس و کمینه کردن اثر نویز براساس تئوری اطلاعات نسبت به رویکردهای مبتنی بر الگوریتم فازی C-میانگین مقاوم به نویز و غیریکنواختی شدت است. در ابتدا مولفه‌ای تحت عنوان بایاس به تابع هزینه الگوریتم گستافسون-کسل اضافه می‌شود. در گام بعدی، جهت مقاوم بودن الگوریتم پیشنهادی به نویز، اطلاعات متقابل بین توزیع داده هر خوشه و داده‌ی خارج از خوشه بیشینه می‌شود. سرانجام، با به‌روزرسانی ماتریس کواریانس، ماتریس عضویت، ضریب yi جهت حذف نمونه‌های نویزی، مولفه بایاس و مراکز خوشه فرآیند بخش‌بندی بافت‌های مغزی انجام می‌شود. نتایج آزمایش نشان می‌دهد که الگوریتم پیشنهادی در مقایسه با سایر روش‌های بخش‌بندی تصاویر MR تحت تغییرات غیریکنواختی شدت و الگوریتم‌های مبتنی بر فازی C-میانگین عملکرد بهتری در بخش‌بندی بافت‌های مختلف مغزی دارد.
کلیدواژه‌ها

عنوان مقاله English

A Novel Fuzzy-based Clustering Algorithm for Segmentation of Brain Tissues with Bias Correction based on MRI images

نویسندگان English

Ali Fahmi Jafargholkhanloo 1
Mousa Shamsi 2
Mahdi Bashiri Bawil 2
1 Assistant Professor, Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran
2 Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
چکیده English

Magnetic Resonance (MR) image segmentation is a crucial step in surgical planning and treatment strategies. However, the presence of intensity non-uniformity—commonly referred to as bias—poses significant challenges in accurately segmenting brain tissues. This artifact arises from factors such as magnetic field inhomogeneities, radiofrequency coil sensitivity variations, and scanner-induced distortions, leading to inconsistent intensities within homogeneous tissue regions. To address these challenges, a novel fuzzy clustering method based on the Gustafson-Kessel algorithm, is propose which demonstrates robustness to both noise and intensity non-uniformity. Unlike traditional fuzzy C-means-based approaches, the proposed method integrates bias field estimation directly into the cost function. Furthermore, to enhance noise resilience, the mutual information between the distribution of data within each cluster and data outside the cluster is maximized. The segmentation process iteratively updates the covariance matrix, membership matrix, a noise-suppressing yi coefficient, bias field, and cluster centers. Experimental results demonstrate that the proposed algorithm significantly outperforms existing methods, particularly under conditions of intensity non-uniformity, yielding more accurate and reliable brain tissue segmentation.

کلیدواژه‌ها English

Bias Correction
Segmentation of Brain Tissues
Magnetic Resonance Imaging
Information Theory
Gustafson-Kessel Clustering
Intensity Non-Uniformity
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دوره 12، شماره 2
تابستان 1404
صفحه 1-13

  • تاریخ دریافت 21 فروردین 1404
  • تاریخ بازنگری 02 شهریور 1404
  • تاریخ پذیرش 03 شهریور 1404