1,234 research outputs found

    A Gaussian-mixed Fuzzy Clustering Model on Valence-Arousal-related fMRI Data-Set

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    Previous medical experiments illustrated that Valence and Arousal were high corresponded to brain response by amygdala and orbital frontal cortex through observation by functional magnetic resonance imaging (fMRI). In this paper, Valence-Arousal related fMRI data-set were acquired from the picture stimuli experiments, and finally the relative Valence -Arousal feature values for a given word that corresponding to a given picture stimuli were calculated. Gaussian bilateral filter and independent components analysis (ICA) based Gaussian component method were applied for image denosing and segmenting; to construct the timing signals of Valence and Arousal from fMRI data-set separately, expectation maximal of Gaussian mixed model was addressed to calculate the histogram, and furthermore, Otsu curve fitting algorithm was introduced to scale the computational complexity; time series based Valence -Arousal related curve were finally generated. In Valence-Arousal space, a fuzzy c-mean method was applied to get typical point that represented the word relative to the picture. Analyzed results showed the effectiveness of the proposed methods by comparing with other algorithms for feature extracting operations on fMRI data-set including power spectrum density (PSD), spline, shape-preserving and cubic fitting methods

    Advanced Brain Tumour Segmentation from MRI Images

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    Magnetic resonance imaging (MRI) is widely used medical technology for diagnosis of various tissue abnormalities, detection of tumors. The active development in the computerized medical image segmentation has played a vital role in scientific research. This helps the doctors to take necessary treatment in an easy manner with fast decision making. Brain tumor segmentation is a hot point in the research field of Information technology with biomedical engineering. The brain tumor segmentation is motivated by assessing tumor growth, treatment responses, computer-based surgery, treatment of radiation therapy, and developing tumor growth models. Therefore, computer-aided diagnostic system is meaningful in medical treatments to reducing the workload of doctors and giving the accurate results. This chapter explains the causes, awareness of brain tumor segmentation and its classification, MRI scanning process and its operation, brain tumor classifications, and different segmentation methodologies

    Inter-comparison of medical image segmentation algorithms

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    Segmentation of images is a vital part of medical image processing, and MRI (Magnetic Resonance Imaging) is already recognized as a very important tool for clinical diagnosis. In this thesis, comparisons between different segmentation algorithms are carried out, specifically on brain MRI images. Initial parts of the thesis provide the background to the project, and an introduction to the basic principles of MRI, respectively, followed by parameter definitions and MRI image artifacts. The next part briefly covers various image pre-processing techniques which are required, and this is followed with a review of the major segmentation techniques which are available, including thresholding, region growing, clustering, and K-Means clustering. The concept of fuzzy logic is also introduced here, and the chapter concludes with a discussion of fuzzy logic based segmentation algorithms such as Fuzzy C-Means (FCM) and Improved Fuzzy C-Means (IFCM) clustering algorithms. The following part provides details concerning the source, type and parameters of the data (images) used for this thesis. Evaluation and inter-comparisons between a number of different segmentation algorithms are given in near concluding part, finally, conclusions and suggestions for future research are provided in last part. Qualitative comparisons on real images and quantitative comparisons on simulated images were performed. Both qualitative and quantitative comparisons demonstrated that fuzzy logic based segmentation algorithms are superior in comparison with classical segmentation algorithms. Edge-based segmentation algorithms demonstrated the poorest performance of all; K-means and IFCM clustering algorithms performed better, and FCM demonstrated the best performance of all. However, it should be noted that IFCM was not properly evaluated due to time restrictions in code generation, testing and evaluation.Segmentation of images is a vital part of medical image processing, and MRI (Magnetic Resonance Imaging) is already recognized as a very important tool for clinical diagnosis. In this thesis, comparisons between different segmentation algorithms are carried out, specifically on brain MRI images. Initial parts of the thesis provide the background to the project, and an introduction to the basic principles of MRI, respectively, followed by parameter definitions and MRI image artifacts. The next part briefly covers various image pre-processing techniques which are required, and this is followed with a review of the major segmentation techniques which are available, including thresholding, region growing, clustering, and K-Means clustering. The concept of fuzzy logic is also introduced here, and the chapter concludes with a discussion of fuzzy logic based segmentation algorithms such as Fuzzy C-Means (FCM) and Improved Fuzzy C-Means (IFCM) clustering algorithms. The following part provides details concerning the source, type and parameters of the data (images) used for this thesis. Evaluation and inter-comparisons between a number of different segmentation algorithms are given in near concluding part, finally, conclusions and suggestions for future research are provided in last part. Qualitative comparisons on real images and quantitative comparisons on simulated images were performed. Both qualitative and quantitative comparisons demonstrated that fuzzy logic based segmentation algorithms are superior in comparison with classical segmentation algorithms. Edge-based segmentation algorithms demonstrated the poorest performance of all; K-means and IFCM clustering algorithms performed better, and FCM demonstrated the best performance of all. However, it should be noted that IFCM was not properly evaluated due to time restrictions in code generation, testing and evaluation

    Segmentation of Brain MRI

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    Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution

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    Magnetic Resonance Imaging (MRI) is extensively used in the study of brain. Segmentation of MR brain images is necessary for a number of clinical investigations of various complexity, change detection, cortical labeling, and visualization in surgical planning. The volume of enhancing lesions, following the administration of paramagnetic contrast agent is an important indicator of pathology in multiple sclerosis (MS). Manual estimation of enhancing lesion volumes introduces significant errors, and operator bias, besides being time consuming and subjective. Therefore, there is a need for automatic identification and estimation of volumes of the present MS lesions specially by dealing with a large number of images that are typically acquired in multi-center clinical trials. In the developed techniques, 150 T1- and T2-weighted spin echo images were taken from the routine scans of Kuala Lumpur General Hospital.Multiple sclerosis lesions visualized by morphological MRI are classified through a feature map technique on T1 weighted MRI tissue. Gray level morphology methods are used to make tissue types in the images more homogenous and minimize difficulties with connections to outside tissue. A method for hzzy connectedness and combinations of the different segmentation techniques were experimented. A gain-based correction method; probability density function model are used to cluster white and gray matters, cerebrospinal fluid, and meninges. Results of segmentation have been validated by a group of neuro-radiologists. 3D visualization has been implemented for the segmented regions as well as brain lesion. The visualization of the segmented structures uses a combination of volume rendering and surface rendering. The mutual information algorithms used in this work has been developed and experimented in the system and has proven to yield more accurate and stable results than other algorithms. Currently testing the validation of the proposed segmentation in a validation study that compares resulting MS lesion as well as gray and white matter tissue structures with Neural Network expert segmentation system. The proposed method versus Neural Network rater validation showed an average validation score of overlap ratio of >85% for gray and white matters tissue segmentation and for MS lesion the rater validation showed an average overlap ratio of > 87%

    Intercomparison of medical image segmentation algorithms

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    Magnetic Resonance Imaging (MRI) is one of the most widely-used high quality imaging techniques, especially for brain imaging, compared to other techniques such as computed tomography and x-rays, mainly because it possesses better soft tissue contrast resolution. There are several stages involved in analyzing an MRI image, segmentation being one of the most important. Image segmentation is essentially the process of identifying and classifying the constituent parts of an image, and is usually very complex. Unfortunately, it suffers from artefacts including noise, partial volume effects and intensity inhomogeneities. Brain, being a very complicated structure, its precise segmentation is particularly necessary to delineate the borders of anatomically distinct regions and possible tumors. Many algorithms have been proposed for image segmentation, the most important being thresholding, region growing, and clustering methods such as k-means and fuzzy c-means algorithms. The main objective of this project was to investigate a representative number of different algorithms and compare their performance. Image segmentation algorithms, including thresholding, region growing, morphological operations and fuzzy c-means were applied to a selection of simulated and real brain MRI images, and the results compared. The project was realized by developing algorithms using the popular Matlab® software package. Qualitative comparisons were performed on real and simulated brain images, while quantitative comparisons were performed on simulated brain images, using a variety of different parameters, and results tabulated. It was found that the fuzzy c-means algorithm performed better than all the other algorithms, both qualitatively and quantitatively. After comparing the performance of all algorithms, it was concluded that, by combining one or two basic algorithms, a more effective algorithm could be developed for image segmentation that is more robust to noise, considers both intensity and spatial characteristics of an image, and which is computationally efficient.Magnetic Resonance Imaging (MRI) is one of the most widely-used high quality imaging techniques, especially for brain imaging, compared to other techniques such as computed tomography and x-rays, mainly because it possesses better soft tissue contrast resolution. There are several stages involved in analyzing an MRI image, segmentation being one of the most important. Image segmentation is essentially the process of identifying and classifying the constituent parts of an image, and is usually very complex. Unfortunately, it suffers from artefacts including noise, partial volume effects and intensity inhomogeneities. Brain, being a very complicated structure, its precise segmentation is particularly necessary to delineate the borders of anatomically distinct regions and possible tumors. Many algorithms have been proposed for image segmentation, the most important being thresholding, region growing, and clustering methods such as k-means and fuzzy c-means algorithms. The main objective of this project was to investigate a representative number of different algorithms and compare their performance. Image segmentation algorithms, including thresholding, region growing, morphological operations and fuzzy c-means were applied to a selection of simulated and real brain MRI images, and the results compared. The project was realized by developing algorithms using the popular Matlab® software package. Qualitative comparisons were performed on real and simulated brain images, while quantitative comparisons were performed on simulated brain images, using a variety of different parameters, and results tabulated. It was found that the fuzzy c-means algorithm performed better than all the other algorithms, both qualitatively and quantitatively. After comparing the performance of all algorithms, it was concluded that, by combining one or two basic algorithms, a more effective algorithm could be developed for image segmentation that is more robust to noise, considers both intensity and spatial characteristics of an image, and which is computationally efficient
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