• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.64-75
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• 2003

Medical image segmentation is the process by which an original image is partitioned into some homogeneous regions like bones, soft tissues, etc. This study demonstrates an automatic medical image segmentation technique based on independent component analysis. Independent component analysis is a generalization of principal component analysis which encodes the higher-order dependencies in the input in addition to the correlations. It extracts statistically independent components from input data. Use of automatic medical image segmentation technique using independent component analysis under the assumption that medical image consists of some statistically independent parts leads to a method that allows for more accurate segmentation of bones from CT data. The result of automatic segmentation using independent component analysis with square test data was evaluated using probability of error(PE) and ultimate measurement accuracy(UMA) value. It was also compared to a general segmentation method using threshold based on sensitivity(True Positive Rate), specificity(False Positive Rate) and mislabelling rate. The evaluation result was done statistical Paired-t test. Most of the results show that the automatic segmentation using independent component analysis has better result than general segmentation using threshold.

• Computers and Concrete
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• v.14 no.3
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• pp.315-325
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• 2014

Concrete is an important material in most of civil constructions. Many properties of concrete can be determined through analysis of concrete images. Image segmentation is the first step for the most of these analyses. An automated system for segmentation of concrete images into microstructures using texture analysis is proposed. The performance of five different classifiers has been evaluated and the results show that using an Artificial Neural Network classifier is the best choice for an automatic image segmentation of concrete.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.563-572
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• 2024

Image processing and computer vision technologies are becoming increasingly important in a variety of application fields that require techniques and tools for sophisticated image analysis. In particular, image segmentation is a technology that plays an important role in image analysis. In this study, in order to identify recent research trends on image segmentation techniques, we used the Web of Science(WoS) database to analyze the R&D topography based on the network structure of the author's keyword co-occurrence matrix. As a result, from 2015 to 2023, as a result of the analysis of the R&D map of research articles on image segmentation, R&D in this field is largely focused on four areas of research and development: (1) researches on collecting and preprocessing image data to build higher-performance image segmentation models, (2) the researches on image segmentation using statistics-based models or machine learning algorithms, (3) the researches on image segmentation for medical image analysis, and (4) deep learning-based image segmentation-related R&D. The scientometrics-based analysis performed in this study can not only map the trajectory of R&D related to image segmentation, but can also serve as a marker for future exploration in this dynamic field.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.38-47
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• 1998

The syntactic analysis for the practical machine translation should be able to analyze a long sentence, but the long sentence analysis is a critical problem because of its high analysis complexity. In this paper a sentence segmentation method is proposed for an efficient analysis of a long sentence and the method of determining optimal sentence segmentation positions using statistical information and genetic learning is introduced. It consists of two modules: (1) decomposable position determination which uses lexical contextual constraints acquired from a training data tagged with segmentation positions. (2) segmentation position selection by the selection function of which the weights of parameters are determined through genetic learning, which selects safe segmentation positions with enhancing the analysis efficiency as much as possible. The safe segmentation by the proposed sentence segmentation method and the efficiency enhancement of the analysis are presented through experiments.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1797-1808
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• 2021

Objective: To determine whether volumetric CT texture analysis (CTTA) using fully automatic tumor segmentation can help predict recurrence-free survival (RFS) in patients with intrahepatic mass-forming cholangiocarcinomas (IMCCs) after surgical resection. Materials and Methods: This retrospective study analyzed the preoperative CT scans of 89 patients with IMCCs (64 male; 25 female; mean age, 62.1 years; range, 38-78 years) who underwent surgical resection between January 2005 and December 2016. Volumetric CTTA of IMCCs was performed in late arterial phase images using both fully automatic and semi-automatic liver tumor segmentation techniques. The time spent on segmentation and texture analysis was compared, and the first-order and second-order texture parameters and shape features were extracted. The reliability of CTTA parameters between the techniques was evaluated using intraclass correlation coefficients (ICCs). Intra- and interobserver reproducibility of volumetric CTTAs were also obtained using ICCs. Cox proportional hazard regression were used to predict RFS using CTTA parameters and clinicopathological parameters. Results: The time spent on fully automatic tumor segmentation and CTTA was significantly shorter than that for semi-automatic segmentation: mean ± standard deviation of 1 minutes 37 seconds ± 50 seconds vs. 10 minutes 48 seconds ± 13 minutes 44 seconds (p < 0.001). ICCs of the texture features between the two techniques ranged from 0.215 to 0.980. ICCs for the intraobserver and interobserver reproducibility using fully automatic segmentation were 0.601-0.997 and 0.177-0.984, respectively. Multivariable analysis identified lower first-order mean (hazard ratio [HR], 0.982; p = 0.010), larger pathologic tumor size (HR, 1.171; p < 0.001), and positive lymph node involvement (HR, 2.193; p = 0.014) as significant parameters for shorter RFS using fully automatic segmentation. Conclusion: Volumetric CTTA parameters obtained using fully automatic segmentation could be utilized as prognostic markers in patients with IMCC, with comparable reproducibility in significantly less time compared with semi-automatic segmentation.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.23-38
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• 2007

Corporations adopt a segmentation depends on the existence of target variables, in general. In this paper, for the case of no target variables, a strategy through segmentation is proposed for corporations having a special status based on the management index. In case of segmentation using cluster analysis, however, if one classify according to many variables then he will be in face of difficulties in characterizing. Therefore, after extracting representative factors by factor analysis, a segmentation method through 2 step cluster analysis is employed on the basis of these representative factors. As a result, six segmentation groups are found and the resulting strategy is proposed which strengthens prominent factors and makes up defective factors for each group.

• Phonetics and Speech Sciences
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• v.3 no.1
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• pp.3-14
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• 2011

It has been posited that in English, native listeners use the Metrical Segmentation Strategy (MSS) for the segmentation of continuous speech. Strong syllables tend to be perceived as potential word onsets for English native speakers, which is due to the high proportion of strong syllables word-initially in the English vocabulary. This study investigates whether Koreans employ the same strategy when segmenting speech input in English. Word-spotting experiments were conducted using vowel-initial and consonant-initial bisyllabic targets embedded in nonsense trisyllables in Experiment 1 and 2, respectively. The effect of strong syllable was significant in the RT (reaction times) analysis but not in the error analysis. In both experiments, Korean listeners detected words more slowly when the word-initial syllable is strong (stressed) than when it is weak (unstressed). However, the error analysis showed that there was no effect of initial stress in Experiment 1 and in the item (F2) analysis in Experiment 2. Only the subject (F1) analysis in Experiment 2 showed that the participants made more errors when the word starts with a strong syllable. These findings suggest that Koran listeners do not use the Metrical Segmentation Strategy for segmenting English speech. They do not treat strong syllables as word beginnings, but rather have difficulties recognizing words when the word starts with a strong syllable. These results are discussed in terms of intonational properties of Korean prosodic phrases which are found to serve as lexical segmentation cues in the Korean language.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.4
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• pp.161-184
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• 2011

As movie demands become more and more diversified, it is necessary for movie related firms to segment a whole heterogeneous market into a number of small homogeneous markets in order to identify the specific needs of consumer groups. Relevant market segmentation helps them to develop valuable offer to target segments through effective marketing planning. In this article, we introduce various segmentation methods and compare their advantages and disadvantages. In particular, we analyze "2009~2010 consumer survey data of Korean Film Industry" by using Latent Class Analysis(LCA), a statistical segmentation method which identifies exclusive set of latent classes based on consumers' responses to an observed categorical and numerical variables. It is applied PROC LCA, a new SAS procedure for conducting LCA and finally get the result of 11 distinctive clusters showing unique characteristics on their buying behaviors.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.1-11
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• 2023

Clustering analysis is used in various fields including customer segmentation and clustering methods such as k-means are actively applied in the credit card customer segmentation. In this paper, we summarized the input features selection method of k-means clustering for the case of the credit card customer segmentation problem, and evaluated its feasibility through the analysis results. By using the label values of k-means clustering results as target features of a decision tree classification, we composed a method for prioritizing input features using the information gain of the branch. It is not easy to determine effectiveness with the clustering effectiveness index, but in the case of the CH index, cluster effectiveness is improved evidently in the method presented in this paper compared to the case of randomly determining priorities. The suggested method can be used for effectiveness of actively used clustering analysis including k-means method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.299-307
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• 2016

While segmenting "complex" images (with multiple objects, many details, etc.) we experienced a need to explore new ways for time-efficient and meaningful image segmentation. In this paper we propose a new technique for image segmentation which has only one variable for controlling the expected number of segments. The algorithm focuses on the treatment of pixels in transition zones between various label distributions. Results of the proposed algorithm (e.g. on the Berkeley image segmentation dataset) are comparable to those of GMM or HMM-EM segmentation, but are achieved with significantly reduced computation time.