ارائه یک سیستم خودکار برای تشخیص افراد سالم و افراد دارای بیماری رتینوپاتی دیابتی

نوع مقاله: مقاله پژوهشی

نویسندگان

دانشکده مهندسی برق- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

چکیده

دیابت یکی از شایع­ترین بیماری­ها در جهان است که آثار مخربی بر روی قسمت­های مختلف بدن برجای می­گذارد. از ابتدایی­ترین قسمت­هایی که دچار عارضه می­شود چشم است. تحلیل صدمات وارد شده بر روی شبکیه چشم از بهترین راه­های تشخیص دیابت است. به همین علت ابتدا یک روش پرکاربرد و موثر برای حذف نویز تصاویر با ترکیب فیلتر وینر و تبدیل موجک گسسته اعمال می­شود. در مرحله بعد از الگوریتم خوشه­بندی k-means برای حذف قسمت­های نامطلوب تصویر شامل نواحی خیلی روشن و خیلی تیره تصویر، استفاده می­شود. سپس ویژگی­های رنگ و شکل تصاویر استخراج می­شود. برای استخراج ویژگی­های رنگ تصویر، تصاویر را به فضای lab که برای چشم انسان بهتر قابل درک­ است برده می­شود و برای استخراج ویژگی­های شکل ابتدا تصاویر را به تصاویر خاکستری تبدیل کرده و سپس اقدام به استخراج ویژگی­های شکل می­گردد. پس از استخراج ویژگی­ها به کمک الگوریتم تجزیه و تحلیل­مؤلفه­های­اصلی تعداد ویژگی­ها را کاهش داده و بهترین و مؤثرترین ویژگی­ها انتخاب می­شود. در پایان برای طبقه­بندی ویژگی­ها و تصاویر به دو گروه سالم و بیمار، از طبقه­­بند ماشین بردار پشتیبان با کرنل­های متفاوت استفاده می­شود. این الگوریتم صحت بالای 90% برای تصاویر آزمایشی حاصل می­کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Proposing an Automated System for Differentiating between Healthy Individuals and Patients with Diabetic Retinopathy

نویسندگان [English]

  • Mina Ghayoor
  • Hossein Pourghassem
Najafabad Branch, Islamic Azad University, Najafabad, Iran
چکیده [English]

Diabetes is one of the most common diseases in the world, adversely affects different body organs. One of the most common causes of eye problems is diabetes. Analyzing retinal damage is one of the best ways to diagnose diabetes so one of the best ways to diagnose diabetes is to look at the damage to the retina. Hence, first, a highly applicable and effective method, which is a combination of the Wiener filter and the discrete wavelet transform (DWT), is used for the removal of noise from images. Afterward, the k-means clustering algorithm is used to remove the bad image sections including very light and very dark areas of the image. Next, the image color and shape features are extracted. We transfer the images to the lab space, which fits the eye more, to extract the image color features. To extract the image shape features, first the images are converted into grey images and then the shape features are extracted. After extracting the features, the number of features is reduced using the Principal Component Analysis (PCA) algorithm. Besides, the best and most effective features are also selected. Finally, the support vector machine classifier with different kernel is used to classify the features and images into two categories, namely the healthy participants and patients. The accuracy resulting from this algorithm using the test images is over 90%.

کلیدواژه‌ها [English]

  • : Diabetic retinopathy
  • shape and color properties of images
  • principal component analysis
  • Support vector machine
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