رویکردی جدید از ادغام تصاویر MRI و CT-Scan با استفاده از تقسیم‌بندی بافت و وزن‌دهی فازی برپایهی تبدیل موجک
محورهای موضوعی : مهندسی کامپیوتر و فناوری اطلاعات
خلیل مولانی
1
,
mehdi jafari
2
,
malihe hashemi
3
1 - گروه مهندسی کامپیوتر، واحد کرمان، دانشگاه آزاد اسلامی،کرمان، ایران
2 - گروه مهندسی برق، واحد کرمان، دانشگاه آزاد اسلامی، کرمان، ایران
3 - گروه مهندسی کامپیوتر، واحد کرمان، دانشگاه آزاد اسلامی، کرمان، ایران
کلید واژه: همجوشی تصاویر, پردازش تصاویر پزشکی, تبدیل موجک گسسته, تقسیم‌بندی بافت, وزن‌دهی فازی,
چکیده مقاله :
تصاویر CT اطلاعاتی در مورد ساختارهای استخوانی ارائه می‌دهند، اما نمی‌توانند اطلاعات بافتی را پشتیبانی کنند؛ در مقابل، تصاویر MRI جزئیاتی را در مورد بافت‌های نرم نشان می‌دهند. به دست آوردن حداکثر اطلاعات و ویژگی‌های کلیدی از تصاویر منبع، افزایش کیفیت بصری و کنتراست تصویر ترکیب شده همچنین کاهش وظایف محاسباتی برای بسیاری از الگوریتم‌های همجوشی تصاویر پزشکی، به صورت یک چالش بزرگ باقی مانده است. در این مقاله، ادغام تصاویر پزشکی بر اساس تبدیل موجک گسسته دو بعدی صورت گرفته است. ابتدا تصاویر اصلی توسط بسته موجک گسسته‌ی Db2 به دو مجموعه ضرایب تقریبی و ضرایب جزئی تجزیه می‌شوند. برای ماتریس ضرایب تقریبی تکنیک وزن‌دهی‌فازی، ماتریس ضرایب تقریبی تصاویر ورودی و برای ضرایب جزئی، از روش میانگین ماتریس ضرایب جزئیات استفاده میشود. وزن‌دهی از تکنیک ماسک حاصل از بخشبندی بافت تصاویر استفاده می‌کند. این تحقیق، به ترکیب تصاویر پزشکی رنگی گسترش یافته است که به طور موثری از اعوجاج رنگ جلوگیری می‌کند و کیفیت بصری را افزایش می‌دهد. نتایج به‌دست آمده نشان می‌دهد که الگوریتم پیشنهادی نه تنها در تشخیص لبه و کانتور و ویژگی‌های بصری برتر عمل می‌کند، بلکه در مقایسه با دیگر پژوهش‌ها، در مقادیر پارامترهای کمی نیز دارای بهبود است.
CT images provide information about bony structures but cannot support tissue information, whereas MRI images show details about soft tissues. Obtaining the maximum information and key features from the source images, increasing the visual quality and contrast of the fused image, and reducing the computational tasks remain a major challenge for many medical image fusion algorithms. In this article, the integration of medical images is based on two-dimensional discrete wavelet transform (DWT). First, the original images are decomposed by the Db2 discrete wavelet package into two sets of approximate coefficients and partial coefficients. For the matrix of approximate coefficients, the fuzzy weighting technique of the matrix of approximate coefficients of the input images is used, and for partial coefficients, the average method of the matrix of detail coefficients is used. Weighting uses the mask technique obtained by segmenting the texture of the images. This research has been extended to the composition of color medical images, which effectively prevents color distortion and enhances visual quality. The obtained results show that the proposed algorithm not only performs better in edge and contour detection and visual features, but also has improvements in in quantitative parameter values compared to other researches.
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