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The lack of labeled medical data is a severe challenge of applying CNNs in medical image segmentation. The common method to solve this problem is employing patches extracted from every pixel of the entire image as train samples. But classifying every pixel in the image is time-consuming, which is not appropriate in practical medical application. This paper proposed a fast segmentation algorithm based...
Brain tumors are created by abnormal and uncontrolled cell division inside the brain. The segmentation of brain tumors which is carried out manually from MRI is a crucial and time consuming task. The accuracy of detecting brain tumor location and size takes the most important role in the successful diagnosis and treatment of tumors. So the detection of brain tumor needs to be fast and accurate. Brain...
Knee osteoarthritis is a chronic joint inflammation disease that affects the aged population nowadays. The disease leads to gradual degradation of cartilage and thus deteriorates the function of the knee joint. Magnetic Resonance Imaging (MRI) provides promising results for the early detection of knee osteoarthritis. Conventionally, the MR image segmentation for knee osteoarthritis is manually done...
Detection and analysis of the brain structural abnormalities from MR images are critical for early diagnosis of type 2 diabetes mellitus (T2DM). However, to date, T2DM biomarkers from brain MR images are still not completely clear. In this study, we investigated T2DM biomarkers using BrainLab, which is our recently developed toolbox for automated analysis of brain MR images. Specifically, our subjects...
The cerebral cortex is the main target of analysis in many functional magnetic resonance imaging (fMRI) studies; statistical analysis can be restricted to the subset of the voxels obtained after cortex segmentation. We used a event-related design and contrasted the cognitive processing of Chinese character and figure in left Brodmann areas 44 and 45, which constitute Broca's region. in Chinese-speaking...
This study focuses on segmentation and validation of brain MR images. Artificial neural network (ANN) has been applied to obtain the targeted segments from these images. In preprocessing step for avoiding the chances of misclassification during training of ANN, the unwanted skull tissues were removed by employing active contour modeling (ACM). The removal of these tissues leaves an image containing...
The amount and the bodily distribution of different adipose tissue (AT) compartments are important indicators for the risk of obesity-related diseases and play an important role in the investigation of their pathogenesis. Magnetic resonance imaging can be used to acquire images of the whole body, showing these compartments and their distribution. In this article, an automated segmentation algorithm...
As magnetic resonance imaging (MRI) is an important technology of radiological evaluation and computer- aided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing. The paper concerns medical image segmentation based on t-mixture model because of merits of the model. By analyzing the features of MR images, the main procedure of white matter segmentation...
It is difficult to segment MR images accurately due to factors such as noise contamination, intensive inhomogeneity and partial volume effect (PVE). In this paper, a fuzzy MRF model based on its conventional version is developed for the segmentation of MR images with intensive inhomogeneity. By solving the mathematic model, we derive a formula to compute the membership values for each voxel with respect...
Fuzzy c-means (FCM) clustering algorithm is a popular model widely used in segmentation of magnetic imaging (MRI) data. The conventional FCM does not take into account the spatial information of image and get the unexpected results of segmentation when dealing with some MRI contaminated by noise. Considering the intensities of ideal MRI are piecewise constant, we present an improved model to fuzzy...
The finite element (FE) modeling of a realistic head is a key issue for the finite element analysis of brain electromagnetic field. In the present study, we have developed a new method to generate subject specific FE head models based on their magnetic resonance (MR) and computer tomography (CT) imaging data. The present approach consists of three parts: segmentation of MR and CT images, co-registration...
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