Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 6 Articles
Processing magnetic resonance images are very complex and constantly studied by\nthe researchers to give doctors better ability to diagnose the patients. In order to\ndetect automatically suspicious regions or tumors, we present a new approach inspired\nby threshold segmentation and based on morphological operations in this paper.\nThe advantages of our approach come from the complementarities between\nthese two approaches. The morphological operations extract roughly the tumor region\nand eventually can affect healthy while the threshold segmentation method\ngives a clear picture of the structure of the different brain and therefore these two\napproaches improve significantly the threshold segmentation and detection and extraction\nof the tumor zone based on morphological operations....
In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of\nthe most active research fields in the medical imaging domain. Because of the fuzzy nature of the\nMRI images, many researchers have adopted the fuzzy clustering approach to segment them. In\nthis work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed.\nThis system gets its robustness from a robust c-means algorithm (RFCM) and obtains its\nfastness from the beneficial properties of agents, such as autonomy, social ability and reactivity.\nTo show the efficiency of the proposed method, we test it on a normal brain brought from the\nBrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to\nnoise and running times standpoints....
We used the finite element method (FEM) to investigate the stress profiles of vertebrae\nin patients who underwent balloon kyphoplasty (BKP) for vertebral fracture.\nBKP is often performed for persistent pain after vertebral fractures. However, fractures\nare frequently reported in the adjacent vertebrae after BKP. The purpose was to\nclarify the mechanism of fractures that occur in the adjacent vertebrae after BKP.\nThe subjects were two patients (first case: 74-year-old woman; second case: 88-yearold\nwoman) who had BKP for osteoporotic vertebral fractures (L1). A bone analysis\nsoftware program, Mechanical Finder, was used to construct three-dimensional finite\nelement models (T11-L3) from computed tomographic (CT) digital imaging and\ncommunications in medicine (DICOM) data. Moment loadings were examined to\nevaluate stress concentrations on the vertebrae. Young�s moduli were lower in the\nsecond case than in the first case at all vertebral levels. Maximum Drucker-Prager\nstresses after BKP were larger in the second case than in the first case for compression,\nflexion, extension, and axial rotation. Strain energy density decreased in L1 and\nincreased in the adjacent vertebrae. Our results suggest that post-BKP fractures of\nthe adjacent vertebrae not only are due to bone fragility, but also can be caused by\nincreased rigidity in the vertebrae filled with bone cement, which increases stress\nconcentration on the adjacent vertebrae and raises the likelihood of fracture....
It is challenging to save acquisition time and reconstruct a medical magnetic resonance (MR) image with important details and\nfeatures fromits compressive measurements. In this paper, a novel method is proposed for longitudinal compressive sensing (LCS)\nMR imaging (MRI), where the similarity between reference and acquired image is combined with joint sparsifying transform.\nFurthermore, the joint sparsifying transform with the wavelet and the Contourlet can efficiently represent both isotropic and\nanisotropic features and the objective function is solved by extended smooth-based monotone version of the fast iterative shrinkage\nthresholding algorithm (SFISTA). The experiment results demonstrate that the existing regularization model obtains better\nperformance with less acquisition time and recovers both edges and fine details of MR images, much better than the existing\nregularization model based on the similarity and the wavelet transform for LCS-MRI....
Background: This study focuses on osteoarthritis (OA), which affects millions of adults\nand occurs in knee cartilage. Diagnosis of OA requires accurate segmentation of cartilage\nstructures. Existing approaches to cartilage segmentation of knee imaging suffer\nfrom either lack of fully automatic algorithm, sub-par segmentation accuracy, or failure\nto consider all three cartilage tissues.\nMethods: We propose a novel segmentation algorithm for knee cartilages with level\nset-based segmentation method and novel template data. We used 20 normal subjects\nfrom osteoarthritis initiative database to construct new template data. We adopt spatial\nfuzzy C-mean clustering for automatic initialization of contours. Force function of our\nalgorithm is modified to improve segmentation performance.\nResults: The proposed algorithm resulted in dice similarity coefficients (DSCs) of\n87.1, 84.8 and 81.7 % for the femoral, patellar, and tibial cartilage, respectively from 10\nsubjects. The DSC results showed improvements of 8.8, 4.3 and 3.5 % for the femoral,\npatellar, and tibial cartilage respectively compared to existing approaches. Our algorithm\ncould be applied to all three cartilage structures unlike existing approaches that\nconsidered only two cartilage tissues.\nConclusions: Our study proposes a novel fully automated segmentation algorithm\nadapted for three types of knee cartilage tissues. We leverage state-of-the-art level set\napproach with newly constructed knee template. The experimental results show that\nthe proposed method improves the performance by an average of 5 % over existing\nmethods....
Retinal fundus image plays an important role in the diagnosis of retinal related diseases. The detailed information of the retinal\nfundus image such as small vessels, microaneurysms, and exudates may be in low contrast, and retinal image enhancement\nusually gives help to analyze diseases related to retinal fundus image. Current image enhancement methods may lead to artificial\nboundaries, abrupt changes in color levels, and the loss of image detail. In order to avoid these side effects, a new retinal fundus\nimage enhancement method is proposed. First, the original retinal fundus image was processed by the normalized convolution\nalgorithm with a domain transform to obtain an image with the basic information of the background. Then, the image with the\nbasic information of the background was fused with the original retinal fundus image to obtain an enhanced fundus image. Lastly,\nthe fused image was denoised by a two-stage denoising method including the fourth order PDEs and the relaxed median filter.The\nretinal image databases, including the DRIVE database, the STARE database, and the DIARETDB1 database, were used to evaluate\nimage enhancement effects. The results show that the method can enhance the retinal fundus image prominently. And, different\nfrom some other fundus image enhancement methods, the proposed method can directly enhance color images....
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