Over the past few years, challenges remain in producing an accurate brain structures segmentation due to the imaging nature of Magnetic Resonance images, that is known to exhibit similar intensity characteristics among subcortical structures such as the hippocampus, amygdala and caudate nucleus. Lack of a distinct image attributes that separate adjacent structures often hinders the accuracy of the segmentation. Therefore, researches have been directed to infer prior knowledge about the possible shape and spatial location to promote accurate segmentation. Realizing the importance of prior information, this focused review aims to introduce brain structures segmentation from the perspective of how the prior information has been utilized in the segmentation methods. A critical analysis on the methodology of the brain segmentation approaches, its’ advantages and issues pertaining to these methods has been discussed in detail. This review also provides an insight to the current happenings and future directions in brain structure segmentation.
Brain structures

Introduction: Metal artifacts can degrade the image quality of computed tomography (CT) images which lead to errors in diagnosis. This study aims to evaluate the performance of Laplace interpolation (LI) method for metal artifacts reduction (MAR) in CT images in comparison with cubic spline (CS) interpolation. Methods: In this study, the proposed MAR algorithm was developed using MATLAB platform. Firstly, the virtual sinogram was acquired from CT image using Radon transform function. Then, dual-adaptive thresholding detected and segmented the metal part within the CT sinogram. Performance of the two interpolation methods to replace the missing part of segmented sinogram were evaluated. The interpolated sinogram was reconstructed, prior to image fusion to obtain the final corrected image. The qualitative and quantitative evaluations were performed on the corrected CT images (both phantom and clinical images) to evaluate the effectiveness of the proposed MAR technique. Results: From the findings, LI method had successfully replaced the missing data on both simple and complex thresholded sinogram as compared to CS method (p-value = 0.17). The artifact index was significantly reduced by LI method (p-value = 0.02). For qualitative analysis, the mean scores by radiologists for LI-corrected images were higher than original image and CS-corrected images. Conclusion: In conclusion, LI method for MAR produced better results as compared to CS interpolation method, as it worked more effective by successfully interpolated all the missing data within sinogram in most of the CT images.