• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.501-506
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• 2008

The classical snake algorithm has a problem in detecting the boundary of an object with deep concavities. While the GVF method can successfully detect boundary concavities, it consumes a lot of time computing the energy map. In this paper, we propose an algorithm to reduce the computation time and improve performance in detecting the boundary of an object with high concavity. We define the degree of complexity of object boundary as the local curvature. If the value of the local curvature is greater than a threshold value, new snake points are added. Simulation results on several different test images show that our method performs well in detecting object boundary and requires less computation time.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2798-2800
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• 2003

Active contour models(called Snakes) are methods for the image segmentation. Many researchers have developed snake algorithms and then published such as GVF, GGF snake. In this paper, we present a pre-process for GGF snake algorithm. This process removes noise so that snakes can flow smoothly. In experiment, we compared a image removed noise with a image corrupted by noise. In result, the pre-process produced a good image for GGF Snake and is necessary.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.670-675
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• 2015

Medical imaging is one of the most powerful tools for gaining information about internal organs and tissues. It is a challenging task to develop sophisticated image analysis methods in order to improve the accuracy of diagnosis. The objective of this paper is to develop a Computer Aided Diagnostics (CAD) scheme for Brain Tumour detection from Magnetic Resonance Image (MRI) using active contour models and to investigate with several approaches for improving CAD performances. The problem in clinical medicine is the automatic detection of brain Tumours with maximum accuracy and in less time. This work involves the following steps: i) Segmentation performed by Fuzzy Clustering with Level Set Method (FCMLSM) and performance is compared with snake models based on Balloon force and Gradient Vector Force (GVF), Distance Regularized Level Set Method (DRLSE). ii) Feature extraction done by Shape and Texture based features. iii) Brain Tumour detection performed by various tree classifiers. Based on investigation FCMLSM is well suited segmentation method and Random Forest is the most optimum classifier for this problem. This method gives accuracy of 97% and with minimum classification error. The time taken to detect Tumour is approximately 2 mins for an examination (30 slices).

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.2
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• pp.183-190
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• 2014

A multiphase pump was developed in this study. The optimum multiphase pump design was arrived at, and the interactions among the different geometric configurations were explained by applying numerical analysis and the DOE (design of experiments) method. First, we designed the base model to meet the specifications. Then, we defined the design parameters related to the meridional plane and the blade angle. Each design parameter was used for generating experiment sets, and numerical analyses were performed on these sets. Finally, the optimized design was selected based on the results of the DOE analysis. The numerical optimization resulted in the optimum model having higher efficiency than the base model. In addition, performance degradation due to changes in the GVF (gas volume fraction) is discussed.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.266-274
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• 2009

An abdominal aortic aneurysm occurs most commonly in older individuals (between 65 and 75), and more in men and smokers. The most important complication of an abdominal aortic aneurysm is rupture, which is most often a fatal event. An abdominal aortic aneurysm weakens the walls of the blood vessel, leaving it vulnerable to bursting open, or rupturing, and spilling large amounts of blood into the abdominal cavity. surface modeling is very useful to surgery for quantitative analysis of abdominal aortic aneurysm. the 3D representation and surface modeling an abdominal aortic aneurysm structure taken from Multi Detector Computed Tomography. The construction of the 3D model is generally carried out by staking the contours obtained from 2D segmentation of each CT slice, so the quality of the 3D model strongly defends on the precision of segmentation process. In this work we present deformable model algorithm. deformable model is an energy-minimizing spline guided by external constraint force. External force which we call Gradient Vector Flow, is computed as a diffusion of a gradient vectors of gray level or binary edge map derived from the image. Finally, we have used snakes successfully for abdominal aortic aneurysm segmentation the performance of snake was visually and quantitatively validated by experts.