• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.827-833
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• 2015

In this paper, we have compared three level set-based active contour (LSAC) methods on inhomogeneous MR image segmentation which is known as an important role of brain diseases to diagnosis and treatment in early. MR image is often occurred a problem with similar intensities and weak boundaries which have been causing many segmentation methods. However, LSAC method could be able to segment the targets such as the level set based on the local image fitting energy, the local binary fitting energy, and local Gaussian distribution fitting energy. Our implemented and tested the subcortical image segmentations were the corpus callosum and hippocampus and finally demonstrated their effectiveness. Consequently, the level set based on local Gaussian distribution fitting energy has obtained the best model to accurate and robust for the subcortical image segmentation.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.61-70
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• 2023

Image segmentation is a very crucial step in effective digital image processing. In the past decade, several research contributions were given related to this field. However, a general segmentation algorithm suitable for various applications is still challenging. Among several image segmentation approaches, graph-based approach has gained popularity due to its basic ability which reflects global image properties. This paper proposes a methodology to partition the image with its pixel, region and texture along with its intensity. To make segmentation faster in large images, it is processed in parallel among several CPUs. A way to achieve this is to split images into tiles that are independently processed. However, regions overlapping the tile border are split or lost when the minimum size requirements of the segmentation algorithm are not met. Here the contributions are made to segment the image on the basis of its pixel using min-cut/max-flow algorithm along with edge-based segmentation of the image. To segment on the basis of the region using a homogenous optimum cut algorithm with boundary segmentation. On the basis of texture, the object type using spectral partitioning technique is identified which also minimizes the graph cut value.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.6
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• pp.19-28
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• 2007

The purpose of this paper is to present a new measure for fingerprint image quality assessment that has a considerable effect on evaluation of fingerprint databases. This paper introduces a hybrid segmentation method for measuring an image quality and evaluates the experimental results using various fingerprint databases. This study compares the performance of the proposed hybrid segmentation using variance and coherence of fingerprints against the NIST's NFIQ program. Although NFIQ is a most widely used tool, it classifies the image quality into 5 levels. However, the proposed hybrid method is developed to be conformant to the ISO standards and accordant to human visual perception. The experimental results demonstrate that the hybrid method is able to produce finer quality measures.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.213-220
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• 2024

Retinal vessel analysis plays a vital role in the detection of some diseases. For example, diabetic retinopathy which may lead to blindness is one of the most common diseases that cause retinal blood vessel structure to change. However, doctors usually take a lot of time and money to collect and label training sets. Thus, automated vessel segmentation as the first step toward computer-aided analysis of fundus remains an active research avenue. We propose an automated Retinal vessel segmentation method based on the GAN network. Traditional image segmentation networks are unsupervised, and GAN is a new semi-supervised network due to adding a Discriminator. By training the discriminator network, we can capture the quality of the generator's output and drive it closer to the true image features. In our experiment, we use DRIVE dataset for training and testing. The final segmentation effect is represented by the Dice coefficient. Experimental results show that the GAN network can effectively improve the edge effect of image segmentation. Compared with the traditional U-net network, GAN shows about 1.55% higher segmentation accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2483-2494
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• 2024

Instance segmentation is a challenging research in the field of computer vision, which combines the prediction results of object detection and semantic segmentation to provide richer image feature information. Focusing on the instance segmentation in the street scene, the real-time instance segmentation method based on SOLOv2 is proposed in this paper. First, a cross-stage fusion backbone network based on position attention is designed to increase the model accuracy and reduce the computational effort. Then, the loss of shallow location information is decreased by integrating two-way feature pyramid networks. Meanwhile, cross-stage mask feature fusion is designed to resolve the small objects missed segmentation. Finally, the adaptive minimum loss matching method is proposed to decrease the loss of segmentation accuracy due to object occlusion in the image. Compared with other mainstream methods, our method meets the real-time segmentation requirements and achieves competitive performance in segmentation accuracy.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.523-531
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• 2005

Existing methods for color image segmentation using diffusion can't preserve contour information, or noises with high gradients become more salient as the number of times of the diffusion increases, resulting in over-segmentation when applied to watershed. This paper proposes a method for color image segmentation by applying morphological operations together with nonlinear diffusion For an input image, transformed into LUV color space, closing by reconstruction and nonlinear diffusion are applied to obtain a simplified image which preserves contour information with noises removed. With gradients computed from this simplified image, watershed algorithm is applied. Experiments show that color images are segmented very effectively without over-segmentation.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.14 no.4
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• pp.201-210
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• 2010

We propose a new robust and accurate method for the numerical solution of medical image segmentation. The modified Allen-Cahn equation is used to model the boundaries of the image regions. Its numerical algorithm is based on operator splitting techniques. In the first step of the splitting scheme, we implicitly solve the heat equation with the variable diffusive coefficient and a source term. Then, in the second step, using a closed-form solution for the nonlinear equation, we get an analytic solution. We overcome the time step constraint associated with most numerical implementations of geometric active contours. We demonstrate performance of the proposed image segmentation algorithm on several artificial as well as real image examples.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.7
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• pp.566-577
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• 1991

In this paper, the method of image segmentation based on a pyramid of reduced resolution versions of the input input image is persented. In a pyramid structure, two regions (a given pixel and its mother pixels) are compared by the proposed inverse-mother-son variance ratio (IMSVR) method for the detection of an optinal object pixel and are determined whether they are similar enough to be viewed as one region or disparate to be viewed as ditinct regions By the proposed method, an l`timal object pixel has been setectedat some level, it is necessary to retrieve its boundary precisely. Moving down the pyramid to levels of higher resolution is requires. In this paper, the top-sown pyramid traversing algorithm for an image segmentation using a pyrmid structure is presented. Using the computer simulation, the results by the proposed statistical method and object traversing method are investigated for the binary image and the real image at the results of computer simulation, the proposed method of image segmentation based on a pyramid structure seem to have useful properties and deserve consideration as a possible alternative to existing methods of omage segmentation. The computation for the proposed method is required 0 (log n), for an TEX>$n{\times}n$ input image.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.22 no.3
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• pp.301-311
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• 2024

Hydride analysis is required to assess the mechanical integrity of spent nuclear fuel cladding. Image segmentation, which is a hydride analysis method, is a technique that can analyze the orientation and distribution of hydrides in cladding images of spent nuclear fuels. However, the segmentation results varied according to the image preprocessing. Inaccurate segmentation results can make hydride difficult to analyze. This study aims to analyze the segmentation performance of the Otsu algorithm according to the morphological operations of cladding images. Morphological operations were applied to four different cladding images, and segmentation performance was quantitatively compared using a histogram, between-class variance, and radial hydride fraction. As a result, this study found that morphological operations can induce errors in cladding images and that appropriate combinations of morphological operations can maximize segmentation performance. This study emphasizes the importance of image preprocessing methods, suggesting that they can enhance the accuracy of hydride analysis. These findings are expected to contribute to the advancements in integrity assessment of spent nuclear fuel cladding.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.21-29
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• 2007

Anisotropic diffusion is one of the widely used techniques in the field of image segmentation. In the conventional anisotropic diffusion [1]-[6], usually 4-neighborhood directions are used: north, south, west and east, except the image diagonal directions, which results in the loss of image details and causes false contours. Existing methods for image segmentation using conventional anisotroplc diffusion can't preserve contour information, or noises with high gradients become more salient as the umber of times of the diffusion increases, resulting in over-segmentation when applied to watershed. In this paper, to overcome the shortcoming of the conventional anisotropic diffusion method, a new arusotropic diffusion method based on diagonal edges is proposed. And a method of watershed segmentation is applied to the proposed method. Experimental results show that the process time of the proposed method including diagonal edges over conventional methods can be up to 2 times faster and the Circle image quality improvement can be better up to $0.45{\sim}2.33(dB)$. Experiments also show that images are segmented very effectively without over segmentation.