Illustration of image elements in 2D and 3D space. Each image element is uniquely specified by its intensity value and its coordinates for pixels and for voxels, where is the image row number, is the image column number, and is the slice number in a volumetric stack see Figure 2. Every image consists of a finite set of image elements called pixels in 2D space or voxels in 3D space. The values (or amplitudes) of the functions and are intensity values and are typically represented by a gray value in MRI of the brain see Figure 1.
and 3D ImagesĪn image can be defined as a function in 2D space or in 3D space, where, , and denote spatial coordinates. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. This includes defining 2D and 3D images, describing an image segmentation problem and image features, and introducing MRI intensity distributions of the brain tissue. To introduce the reader to the complexity of the brain MRI segmentation problem and address its challenges, we first introduce the basic concepts of image segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. In this paper we review the most popular methods commonly used for brain MRI segmentation. This diversity of image processing applications has led to development of various segmentation techniques of different accuracy and degree of complexity. For example, MRI segmentation is commonly used for measuring and visualizing different brain structures, for delineating lesions, for analysing brain development, and for image-guided interventions and surgical planning. This is because different processing steps rely on accurate segmentation of anatomical regions. Nowadays, computerized methods for MR image segmentation, registration, and visualization have been extensively used to assist doctors in qualitative diagnosis.īrain MRI segmentation is an essential task in many clinical applications because it influences the outcome of the entire analysis.
These difficulties in brain MRI data analysis required inventions in computerized methods to improve disease diagnosis and testing.
This manual analysis is often time-consuming and prone to errors due to various inter- or intraoperator variability studies. The analysis of these large and complex MRI datasets has become a tedious and complex task for clinicians, who have to manually extract important information. The advances in brain MR imaging have also provided large amount of data with an increasingly high level of quality. Enormous progress in accessing brain injury and exploring brain anatomy has been made using magnetic resonance imaging (MRI). Over the last few decades, the rapid development of noninvasive brain imaging technologies has opened new horizons in analysing and studying the brain anatomy and function.
To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation.
In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications.