Abstract
The caudate nucleus of the brain is highly correlated to the emotional decision-making of pessimism, which is an important process for improving the understanding and treatment of depression; and the segmentation of the caudate nucleus is the most basic step in the process of analysis and research concerning this region. In this paper, Level Set Method (LSM) is applied for caudate nucleus segmentation. Firstly, Distance Regularized Level Set Evolution (DRLSE), Region-Scalable Fitting (RSF) and Local Image Fitting (LIF) models are proposed for segmentation of the caudate nucleus of Magnetic Resonance Imaging (MRI) images of the brain, and the segmentation results are compared by using selected evaluation indices. The average Dice Similarity Coefficient (DSC) values of the proposed three methods all exceed 85%, and the average Jaccard Similarity (JS) values are over 77%, respectively. The results indicate that all these three models can have good segmentation results for medical images with intensity inhomogeneity and meet the general segmentation requirements, while the proposed DRLSE model performs better in segmentation.
Highlights
With the development of science and technology, advanced brain imaging technologies and new types of equipment are constantly appearing
Distance Regularized Level Set Evolution (DRLSE), Region-Scalable Fitting (RSF) and Local Image Fitting (LIF) models are proposed for segmentation of the caudate nucleus of Magnetic Resonance Imaging (MRI) images of the brain, and the segmentation results are compared by using selected evaluation indices
The RSF, LIF, and DRLSE models were selected for segmentation and the results indicated that the proposed DRLSE model has the best segmentation ability by comparing with ground truth image dataset
Summary
With the development of science and technology, advanced brain imaging technologies and new types of equipment are constantly appearing. The existing brain imaging technologies include Magnetic Resonance Imaging (MRI), Positron Emission Computed Tomography (PET), Electroencephalography (EEG), Magnetoencephalography (MEG), Computed Tomography (CT), Single-Photon Emission Computed Tomography (SPECT), Diffusion Tensor Imaging (DTI), etc These brain imaging techniques have become an indispensable means to carry out disease diagnosis, surgical planning, and prognosis assessment. The value of medical image segmentation is mainly reflected in the following two aspects: a) The human tissues and organs or lesion tissues can be extracted by medical image segmentation to assist in the diagnosis, treatment planning, and clinical research. It saves time and effectively reduces the diagnostic errors. The original image is compressed after being segmented, which reduces the computational complexity and makes the image satisfy the real-time and precision in 3D reconstruction
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