تحلیل روش های قطعه بندی تصاویر ام‌آرآی (MRI) مغز

ABSTRACT Review of brain MRI image segmentation methods

The application of image processing techniques has rapidly increased in recent years. Nowadays, capturing and storing of medical images are done digitally (Chang and Teng 2007).

However, the interpretation of details of medical images is still time-consuming. This matter is especially observed in regions with abnormal color and shape which should be identified by radiologists for future studies (Chang and Teng 2007). Image segmentation is a key task in many image processes and computer vision applications. The purpose of image segmentation is to partition image to different regions based on given criteria for future process (Chang and Teng 2007).

Medical image segmentation is a key task in many medical applications such as surgical planning, post-surgical assessment, abnormality detection, and so on (Zhang et al. 2007). There are lots of methods for automatic and semi-automatic image segmentation, though, most of them fail because of unknown noise, poor image contrast, inhomogeneity and weak boundaries that are usual in medical images. Medical images mostly contain complicated structures and their precise segmentation is necessary for clinical diagnosis (Hall et al. 1992).

One of such is brain image segmentation which is quite complicated and challenging but its accurate segmentation is very important for detecting tumors, edema, and necrotic tissues. Accurate detection of these tissues is very important in diagnostic systems. Also, magnetic resonance imaging (MRI) is an important imaging technique for detecting abnormal changes in different parts of the brain in early stage. MRI imaging is a popular way to obtain an image of brain with high contrast. MRI acquisition parameters can be adjusted to give different grey levels for different tissues and various types of neuropathology (Tian and Fan 2007). MRI images have good contrast in comparison to computerized tomography (CT). Therefore, most of research in medical image segmentation uses MRI images.

The identification of brain structures in magnetic resonance imaging (MRI) is very important in neuroscience and has many applications such as: mapping of functional activation onto brain anatomy, the study of brain development, and the analysis of neuroanatomical variability in normal brains (Han and Fischl 2007). Brain image segmentation is also useful in clinical diagnosis of neurodegenerative and psychiatric disorders, treatment evaluation, and surgical planning (Han and Fischl 2007). There are lots of methods for automatic and semi-automatic image segmentation, though, most of them fail because of unknown noise, poor image contrast, and weak boundaries that are usual in medical images.