Abstract
In Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Brain image segmentation is of fundamental importance since it helps clinicians and researchers to concentrate on specific regions of the brain in order to analyze them. However, segmentation of brain images is a difficult task due to high similarities and correlations of intensity among different regions of the brain image. Among various methods proposed in the literature, clustering algorithms prove to be successful tools for image segmentation. In this paper, we present a framework for image segmentation that is devoted to support the expert in identifying different brain regions for further analysis. The framework includes different clustering methods to perform segmentation of MR images. Furthermore, it enables easy comparison of different segmentation results by providing a quantitative evaluation using an entropy-based measure as well as other measures commonly used to evaluate segmentation results. To show the potential of the framework, the implemented clustering methods are compared on simulated T1-weighted MR brain images from the Internet Brain Segmentation Repository (IBSR database) provided with ground truth segmentation.
Highlights
Medical image analysis plays a crucial role in modern diagnosis
We present some comparative results of Magnetic Resonance (MR) image segmentation using the clustering methods implemented in the proposed framework
We used T1-weighted MR brain images from the Internet Brain Segmentation Repository (IBSR), which was made available by the Center for Morphometric Analysis, Massachusetts General
Summary
Medical image analysis plays a crucial role in modern diagnosis. computer-based image analysis is becoming an important field, with an increasing reliance on it by the biomedical community.Diagnostic imaging is an invaluable tool in medicine today. Medical image analysis plays a crucial role in modern diagnosis. Computer-based image analysis is becoming an important field, with an increasing reliance on it by the biomedical community. Diagnostic imaging is an invaluable tool in medicine today. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), digital mammography and other imaging modalities provide effective means for non-invasive analysis of the anatomy of a subject. Computer algorithms designed for the delineation of anatomical structures and other regions of interest are key components in assisting or even automating specific medical tasks. Image segmentation is an essential and crucial process for facilitating the delineation, characterization, and visualization of a Region Of Interest (ROI) in any medical image, because its output affects all subsequent processes of image analysis
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