• Progress in Medical Physics
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• v.34 no.3
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• pp.23-32
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• 2023

Purpose: In radiomics analysis, to evaluate features, and predict genetic characteristics and survival time, the pixel values of lesions depicted in computed tomography (CT) and magnetic resonance imaging (MRI) images are used. CT and MRI offer three-dimensional images, thus producing three-dimensional features (Features_3d) as output. However, in reports, the superiority between Features_3d and two-dimensional features (Features_2d) is distinct. In this study, we aimed to investigate whether a difference exists in the prediction accuracy of radiomics analysis of lung cancer using Features_2d and Features_3d. Methods: A total of 38 cases of large cell carcinoma (LCC) and 40 cases of squamous cell carcinoma (SCC) were selected for this study. Two- and three-dimensional lesion segmentations were performed. A total of 774 features were obtained. Using least absolute shrinkage and selection operator regression, seven Features_2d and six Features_3d were obtained. Results: Linear discriminant analysis revealed that the sensitivities of Features_2d and Features_3d to LCC were 86.8% and 89.5%, respectively. The coefficients of determination through multiple regression analysis and the areas under the receiver operating characteristic curve (AUC) were 0.68 and 0.70 and 0.93 and 0.94, respectively. The P-value of the estimated AUC was 0.87. Conclusions: No difference was found in the prediction accuracy for LCC and SCC between Features_2d and Features_3d.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.9-16
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• 2015

We propose a hybrid pattern recognition method that effectively combines two different features for improving data classification. We first extract the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) features, both of which are widely used in pattern recognition, to construct a set of basic features, and then evaluate the separability of each basic feature. According to the results of evaluation, we select only the basic features that contain a large amount of discriminative information for construction of the combined features. The experimental results for the various data sets in the UCI machine learning repository show that using the proposed combined features give better recognition rates than when solely using the PCA or LDA features.

• Journal of Biomedical Engineering Research
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• v.21 no.2
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• pp.137-144
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• 2000

Clustered microcalcifications in X-ray mammograms are an important sign for the diagnosis of breast cancer. A shape-based method, which is based on the morphological features of clustered microcalcifications, is proposed for classifying clustered microcalcifications into benign or malignant categories. To verify the effectiveness of the proposed shape features, clinical mammograms were used to compare the classification performance of the proposed shape features with those of conventional textural features, such as the spatial gray-leve dependence method and the wavelet-based method. Image features extracted from these methods were used as inputs to a three-layer backpropagation neural network classifier. The classification performance of features extracted by each method was studied by using receiver operating-characteristics analysis. The proposed shape features were shown to be superior to the conventional textural features with respect to classification accuracy.

• Korean Institute of Interior Design Journal
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• v.24 no.6
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• pp.128-136
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• 2015

This research, which has been planned to appreciate the features of continuous observation of space, has applied the procedure of acquiring continuous visual information when the act of watching takes place along the time to analyze the space characteristics through the scenes and time so that the features of attention shown in the process of acquiring visual information at the time of observing continuous scenes might be estimated. For analysis of the features of continuous observation was set up the premise that the features of observation and perception vary depending on gender, when the women shops in department stores were selected as research objects. The observation features found at the time of continuous observation of selling spaces in department stores were focused on two analysis methods in order to compare the differences and characteristics of the two. The followings are the findings. First, the area with predominant observation was found to be 87.1% in both methods. It was found that the analysis of observation features by "Analysis I" was useful for inter-sectional comparison of continuous images. Second, in case of extracting predominant sections, the ceiling or the structures which are the backgrounds rarely attracted any eyes. Depending on analysis method, there was the gap of 14.3%~25.0% between observed sections. Third, in case that the hall is curved, the eyes were found to be expanded from side to side and up and down. The review of observation numbers of predominant sections makes it possible to decide whether it should be regarded as (1) unstability or (2) expanding search, and when the images are enlarged from distant view to close-range view, the weakening vanishing point results in the increase of expanded search of surroundings. Accordingly, it was found that the characteristics of images has effects on the observation features when any space was continuously observed. Furthermore, the difference of analysis methods also was found to be likely to cause big differences in the results of analyzing observation features.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.415-434
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• 2015

In this paper we overview the literature on common features analysis of economic time series. Starting from the seminal contributions by Engle and Kozicki (1993) and Vahid and Engle (1993), we present and discuss the various notions that have been proposed to detect and model common cyclical features in macroeconometrics. In particular, we analyze in details the link between common cyclical features and the reduced-rank regression model. We also illustrate similarities and differences between the common features methodology and other popular types of multivariate time series modelling. Finally, we discuss some recent developments in this area, such as the implications of common features for univariate time series models and the analysis of common autocorrelation in medium-large dimensional systems.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1635-1648
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• 2008

The extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we applied three-dimensional (3D) texture feature extraction methods to cell nuclei images and evaluated the validity of them for computerized cell nuclei grading. Individual images of 2,423 cell nuclei were extracted from 80 renal cell carcinomas (RCCs) using confocal laser scanning microscopy (CLSM). First, we applied the 3D texture mapping method to render the volume of entire tissue sections. Then, we determined the chromatin texture quantitatively by calculating 3D gray-level co-occurrence matrices (3D GLCM) and 3D run length matrices (3D GLRLM). Finally, to demonstrate the suitability of 3D texture features for grading, we performed a discriminant analysis. In addition, we conducted a principal component analysis to obtain optimized texture features. Automatic grading of cell nuclei using 3D texture features had an accuracy of 78.30%. Combining 3D textural and 3D morphological features improved the accuracy to 82.19%. As a comparative study, we also performed a stepwise feature selection. Using the 4 optimized features, we could obtain more improved accuracy of 84.32%. Three dimensional texture features have potential for use as fundamental elements in developing a new nuclear grading system with accurate diagnosis and predicting prognosis.

• Spatial Information Research
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• v.6 no.1
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• pp.11-23
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• 1998

Map generalization functions to visualize the spatial data or to change their scale by changing the level of details of data. Until recently, the studies on map generalization have concentrated more on line features than on point features. However, point features are one of the essential components of digital maps and cannnot be ignored because of the great amount of information they carry. This study, therefore, aimed to find out a detailed procedure of point features' generalization. Particularly, this work chose the distribution pattern of point features as the most important factor in the point generalization in investigating the geometric characteristics of source data. First, it attempted to find out the characteristics of distribution pattern of point features through quadrat analysis with Grieg-Smith method and nearest-neighbour analysis. It then generalized point features through the generalization threshold which did not alter the characteristics of distribution pattern and the removal of redudant point feautres. Therefore, the generalization procedure of point features provided by this work maintained the geometric characteristics as much as possible.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.3
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• pp.84-95
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• 2005

This paper presents solution heuristics to solving optimal multiple-viewpoint location problems that are based on topographic features. The visibility problem is to maximise the viewshed area for a set of viewpoints on digital elevation models (DEM). For this analysis, five areas are selected, and fundamental topographic features (peak, pass, and pit) are extracted from the DEMs of the study areas. To solve the visibility problem, at first, solution approaches based on the characteristics of the topographic features are explored, and then, a benchmark test is undertaken that solution performances of the solution methods, such as computing times, and visible area sizes, are compared with the performances of traditional spatial heuristics. The feasibility of the solution methods, then, are discussed with the benchmark test results. From the analysis, this paper can conclude that fundamental topographic features based solution methods suggest a new sight of visibility analysis approach which did not discuss in traditional algorithmic approaches. Finally, further research avenues are suggested such as exploring more sophisticated selection process of topographic features related to visibility analysis, exploiting systematic methods to extract topographic features, and robust spatial analytical techniques and optimization techniques that enable to use the topographic features effectively.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.8
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• pp.343-348
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• 2019

In recent years, extractive summarization systems based on end-to-end deep learning models have become popular. These systems do not require human-crafted features and adopt data-driven approaches. However, previous related studies have shown that linguistic analysis features such as part-of-speeches, named entities and word's frequencies are useful for extracting important sentences from a document to generate a summary. In this paper, we propose an extractive summarization system based on deep neural networks using conventional linguistic analysis features. In order to prove the usefulness of the linguistic analysis features, we compare the models with and without those features. The experimental results show that the model with the linguistic analysis features improves the Rouge-2 F1 score by 0.5 points compared to the model without those features.

• ETRI Journal
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• v.38 no.3
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• pp.494-501
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• 2016

Sentiment analysis incorporates natural language processing and artificial intelligence and has evolved as an important research area. Sentiment analysis on product reviews has been used in widespread applications to improve customer retention and business processes. In this paper, we propose a method for performing an intensified sentiment analysis on customer product reviews. The method involves the extraction of two feature sets from each of the given customer product reviews, a set of acoustic features (representing emotions) and a set of lexical features (representing sentiments). These sets are then combined and used in a supervised classifier to predict the sentiments of customers. We use an audio speech dataset prepared from Amazon product reviews and downloaded from the YouTube portal for the purposes of our experimental evaluations.