The segmentation of brain tumor using Magnetic Resonance Image (MRI) plays an important role in the medical image process. This paper presents a comprehensive survey on brain tumor methods and technology using MRI images. Generally, brain tumor segmentation methods can divided into two main categories, spatial continuous and spatial discrete methods. Several methods, techniques, related advantage and weakness will be described and discussed. The evaluation measures are mentioned and the qualities of different method focus on the methods that were applied on the standard data sets. The efficient and stably brain tumor segmentation is still a challenging task for the unpredictable appearance and shape of the brain tumor.
Biological images with significant intensity inhomogeneity are considerably difficult for the tissue segmentation. To overcome the difficulties caused by the intensity inhomogeneity, this study presents a variational level set method to simultaneous bias field estimation and tissue segmentation for images in the presence of intensity inhomogeneity. An energy function is defined in terms of two data fitting terms which incorporate the local clustering properties into the global region information. First, depended on the observed image mode, the local cluster property based on the observed signal is simplified to a criterion function which is similar to the Mumford‐Shah model. The local criterion energy is then integrated with a global region measure, which is based on intensity difference of the true signal. The energy is minimized in a variational level set formulation with a regularity term, thus avoiding the expensive computation of the level set reinitialization and keeping the curve close to the signal distance function. Experiment results on biological images show desirable performance and demonstrate the effectiveness of the proposed algorithm.
This paper, using the distributed parameter line model, presents an accurate fault location method based on fundamental frequency positive sequence fault components for EHV transmission line. The method is based on positive sequence fault components Extra-High Voltage (EHV) electric transmission line. The method based on the positive sequence fault component is robust to the operating state of the prefault system and fault path resistance. The technique proposed in the paper does not require the fault type, fault phase, and the zero-sequence parameter to be obtained in advance. In addition, due to the use of fault component protection theory, the algorithm itself is not affected by the previous operating state of the system. The method uses a distributed parameter model, which is more accurate in positioning and smaller in error than a lumped parameter model by a large number of simulations. Accurate fault location is important for shortening the fault time and reducing the loss of the fault, so the positioning method proposed can improve the power supply quality and safety. This paper describes the characteristics of the proposed technique and assesses its performance by using Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC).
To restore power feeding as soon as possible and reduce repair costs and labor, a precise and robust fault location method for transmission lines is proposed. This method is based on the current and voltage synchronously collected by the phasor measurement units (PMUs) at two terminals of the line and does not require line parameters to calculate the fault distance. The line parameter is not approximately constant, but is affected by power load, temperature, and humidity, which affects the accuracy of most fault location algorithms that rely on line parameters. Therefore, the method proposed in this paper is robust and accurate. The method is based on the sequence fault component network and synchronous measurement technology, which is not affected by the system's pre-fault state, fault type, fault inception angle, and fault phase. Then, the method is verified in PSCAD/EMTDC by choosing different path resistances, fault types, fault inception angles, load currents, and line transpositions. A large number of simulation results show that the proposed method has high accuracy and robustness.
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