رئوس مطالب

  • چکیده
  • کلیدواژه ها
  • 1.مقدمه
  • 1.1 روش‌های تصویربرداری
  • 1.2. تصویربرداری MR (ام آر آی)
  • 2. بررسی متون
  • 2.1.روش‌های نویززدایی موجود
  • 2.1.1 انتشار غیرخطی ناهمسانگرد
  • 2.1.2 روش میدان تصادفی مارکوف (MRF)
  • 2.1.3 روش‌های مبتنی بر موجک
  • 2.1.4 روش اصلاح تحلیلی
  • 2.1.5 روش غیرمحلی (NL)
  • 2.2 اصلاح ناهمگونی
  • 2.2.1 روش‌های آینده‌نگر
  • 2.2.2 روش‌های گذشته‌نگر
  • 2.3 روش‌های قطعه‌بندی تصویر
  • 2.3.1 FCM (C میانگین فازی)
  • 2.3.2 بردار مخلوط گاوس
  • 2.3.3 LVQ
  • 2.3.4 SOM
  • 2.3.5 آبریز
  • 2.3.6 رشد ناحیه
  • 2.3.7 مدل کنترل فعال
  • 2.3.8 کنترل فعال دو ناحیه‌ای
  • 2.3.9 کنترل فعال چند ناحیه‌ای
  • 2.3.10 قطعه‌بندی مبتنی بر اطلس
  • 2.3.11 مدل‌ میدان تصادفی مارکوف
  • 2.3.12 قطعه‌بندی مغز دچار انحرافات تشریحی (کالبد شناختی)
  • 3.نتیجه‌گیری

Abstract

Brain image segmentation is one of the most important parts of clinical diagnostic tools. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate segmentation of brain images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. We presented a review of the methods used in brain segmentation. The review covers imaging modalities, magnetic resonance imaging and methods for noise reduction, inhomogeneity correction and segmentation. We conclude with a discussion on the trend of future research in brain segmentation.

Keywords: - -

Conclusions

Generally, image segmentation has been an active research field for the last several decades. Moreover, it is a most challenging and most active research field in the image processing. Image segmentation is the preliminary stage of almost all image analysing tools. There exist a variety of state-of art methods and good prior knowledge for brain MRI segmentation. But still, brain MRI segmentation is a challenging task and there is a need for future research to improve the accuracy, precision and speed of segmentation methods. Using improved atlas based methods, parallelization and combining different methods can be the way for making improvement in brain segmentation methods. With increasing knowledge about the relationship between different disorders with anatomical deviation, brain segmentation is used as first stage in tools for detection and analyzing them. For example Alzheimer and Multiple sclerosis (MS) are disorders which can be studied based on deviation in structures of the brain.

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