The Correction Effect of Motion Artifacts in PET/CT Image using System (PET/CT 검사 시 움직임 보정 기법의 유용성 평가)
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- Journal of the Korean Society of Radiology
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- v.18 no.1
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- pp.45-52
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- 2024
In this study, an AI-based algorithm was developed to prevent image quality deterioration and reading errors due to patient movement in PET/CT examinations that use radioisotopes in medical institutions to test cancer and other diseases. Using the Mothion Free software developed using, we checked the degree of correction of movement due to breathing, evaluated its usefulness, and conducted a study for clinical application. The experimental method was to use an RPM Phantom to inject the radioisotope 18F-FDG into a vacuum vial and a sphere of a NEMA IEC body Phantom of different sizes, and to produce images by directing the movement of the radioisotope into a moving lesion during respiration. The vacuum vial had different degrees of movement at different positions, and the spheres of the NEMA IEC body Phantom of different sizes produced different sizes of lesions. Through the acquired images, the lesion volume, maximum SUV, and average SUV were each measured to quantitatively evaluate the degree of motion correction by Motion Free. The average SUV of vacuum vial A, with a large degree of movement, was reduced by 23.36 %, and the error rate of vacuum vial B, with a small degree of movement, was reduced by 29.3 %. The average SUV error rate at the sphere 37mm and 22mm of the NEMA IEC body Phantom was reduced by 29.3 % and 26.51 %, respectively. The average error rate of the four measurements from which the error rate was calculated decreased by 30.03 %, indicating a more accurate average SUV value. In this study, only two-dimensional movements could be produced, so in order to obtain more accurate data, a Phantom that can embody the actual breathing movement of the human body was used, and if the diversity of the range of movement was configured, a more accurate evaluation of usability could be made.
Purpose This study aimed to investigate radiomics analysis of ultrasonographic images to develop a potential biomarker for predicting lymph node metastasis in papillary thyroid carcinoma (PTC) patients. Materials and Methods This study included 431 PTC patients from August 2013 to May 2014 and classified them into the training and validation sets. A total of 730 radiomics features, including texture matrices of gray-level co-occurrence matrix and gray-level run-length matrix and single-level discrete two-dimensional wavelet transform and other functions, were obtained. The least absolute shrinkage and selection operator method was used for selecting the most predictive features in the training data set. Results Lymph node metastasis was associated with the radiomics score (p < 0.001). It was also associated with other clinical variables such as young age (p = 0.007) and large tumor size (p = 0.007). The area under the receiver operating characteristic curve was 0.687 (95% confidence interval: 0.616-0.759) for the training set and 0.650 (95% confidence interval: 0.575-0.726) for the validation set. Conclusion This study showed the potential of ultrasonography-based radiomics to predict cervical lymph node metastasis in patients with PTC; thus, ultrasonography-based radiomics can act as a biomarker for PTC.
In tunnel construction, the stability is evaluated by the settlement of adjacent structures and ground, but the shear strain of the ground is the main factor that determines the failure mechanism of the ground due to the tunnel excavation and the change of the operating load, and can be used to review the stability of the tunnel excavation and to calculate the reinforcement area. In this study, a twin tunnel excavation was simulated on a soft ground in an urban area through a laboratory model test to analyze the behavior of the twin tunnel excavation on the adjacent pile grouped foundation and adjacent ground. Both the displacement and the shear strain of ground were obtained using a close-range photogrammetry during laboratory model test. In addition, two-dimensional finite element numerical analysis was performed based on the model test. The results of a back-analysis showed that the maximum shear strain rate tends to decrease as the horizontal distance between the pillars of the twin tunnel and the vertical distance between the toe of the pile group and the crown of the tunnel were decreased. The impact of the second tunnel on the first tunnel and pile group was decreased as the horizontal distance between the pillars of the twin tunnel was increased. In addition, the vertical distance between the toe of the pile group and the crown of the tunnel had a relatively greater impact on the shear strain results than the horizontal distance of the pillars between the twin tunnels. According to the results of the close-range photogrammetry and numerical analysis, the settlement of adjacent pile group and adjacent ground was measured within the design criteria, but the shear strain of the ground was judged to be outside the range of small strain in all cases and required reinforcement.
For the purpose of utilizing the sawdust having poor combining properties as board raw material and resulting in dimensional instability of board, polypropylene chip (abbreviated below as PP chip) or oriented PP thread was combined with sawdust particle from white meranti(Shorea sp.). The PP chip was prepared from PP thread in length of 0.25, 0.5, 1.0 and 1.5 cm for conventional blending application. Thereafter, the PP chip cut as above was combined with the sawdust particle by 3, 6, 9, 12 and 15% on the weight basis of board. Oriented PP threads were aligned with spacing of 0.5, 1.0 and 1.5cm along transverse direction of board. The physical and mechanical properties on one, two and three layer boards manufactured with the above combining conditions were investigated. The conclusions obtained at this study were summarized as follows: 1. In thickness swelling, all one layer boards combined with PP chips showed lower values than control sawdustboard, and gradually clear decreasing tendendy with the increase of PP chip composition. Two layer board showed higher swelling value than one layer board, but the majority of boards lower values than control sawdustboard. All three layer boards showed lower swelling values than control sawdustboard. 2. In the PP chip and oriented thread combining board, the swelling values of boards combining 0.5cm spacing oriented thread with 1.0 or 1.5cm long PP chip in 12 and 15% by board weight were much lower than the lowest of one or three layer. 3. In specific gravity of 0.51, modulus of rupture of one layer board combined with 3% PP chip showed higher value than control sawdustboard. However, moduli of rupture of the boards with every PP chip composition did not exceed 80kgf/cm2, the low limit value of type 100 board, Korean Industrial Standard KS F 3104 Particleboards. Moduli of rupture of 6%, 1.5cm-long and 3% PP chip combined boards in specific gravity of 0.63 as well as PP chip combined board in specific gravity of 0.72 exceeded 80kgf/
Thecodiplosis japonesis is sweeping the Pinus densiflora forests from south-west to north-east direction, destroying almost all the aged large trees as well as even the young ones. The front line of infestation is moving slowly but ceaselessly norhwards as a long bottle front. Estimation is that more than 40 percent of the area of P. densiflora forest has been damaged already, however some individuals could escapes from the damage and contribute to restore the site to the previous vegetation composition. When the stands were attacked by this insect, the drastic openings of the upper story of tree canopy formed by exclusively P. densiflora are usually resulted and some environmental factors such as light, temperature, litter accumulation, soil moisture and offers were naturally modified. With these changes after insect invasion, as the time passes, phytosociologic changes of the vegetation are gradually proceeding. If we select the forest according to four categories concerning the history of the insect outbreak, namely, non-attacked (healthy forest), recently damaged (the outbreak occured about 1-2 years ago), severely damaged (occured 5-6 years ago), damage prolonged (occured 10 years ago) and restored (occured about 20 years ago), any directional changes of vegetation composition could be traced these in line with four progressive stages. To elucidate these changes, three survey districts; (1) "Gongju" where the damage was severe and it was outbroken in 1977, (2) "Buyeo" where damage prolonged and (3) "Gochang" as restored, were set, (See Tab. 1). All these were located in the south temperate forest zone which was delimited mainly due to the temporature factor and generally accepted without any opposition at present. In view of temperature, the amount and distribution of precipitation and various soil factor, the overall homogeneity of environmental conditions between survey districts might be accepted. However this did not mean that small changes of edaphic and topographic conditions and microclimates can induce any alteration of vegetation patterns. Again four survey plots were set in each district and inter plot distance was 3 to 4 km. And again four subplots were set within a survey plot. The size of a subplot was
Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.
Internet commerce has been growing at a rapid pace for the last decade. Many firms try to reach wider consumer markets by adding the Internet channel to the existing traditional channels. Despite the various benefits of the Internet channel, a significant number of firms failed in managing the new type of channel. Previous studies could not cleary explain these conflicting results associated with the Internet channel. One of the major reasons is most of the previous studies conducted analyses under a specific market condition and claimed that as the impact of Internet channel introduction. Therefore, their results are strongly influenced by the specific market settings. However, firms face various market conditions in the real worlddensity and disutility of using the Internet. The purpose of this study is to investigate the impact of various market environments on a firm's optimal channel strategy by employing a flexible game theory model. We capture various market conditions with consumer density and disutility of using the Internet.
There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.
As distribution environment is changing rapidly and competition is more intensive in the channel of distribution, the importance of retailer image and retailer equity is increasing as a different competitive advantages. Also, consumers are not functionally oriented and that their behavior is significantly affected by the symbols such as retailer image which identify retailer in the market place. That is, consumers do not choose products or retailers for their material utilities but consume the symbolic meaning of those products or retailers as expressed in their self images. The concept of self-image congruence has been utilized by marketers and researchers as an aid in better understanding how consumers identify themselves with the brands they buy and the retailer they patronize. Although self-image congruity theory has been tested across many product categories, the theory has not been tested extensively in the retailing. Therefore, this study attempts to investigate the impact of self image congruence between retailer image and self image of consumer on retailer equity such as retailer awareness, retailer association, perceived retailer quality, and retailer loyalty. The purpose of this study is to find out whether retailer-self image congruence can be a new antecedent of retailer equity. In addition, this study tries to examine how four-dimensional retailer equity constructs (retailer awareness, retailer association, perceived retailer quality, and retailer loyalty) affect customers' repatronage intention. For this study, data were gathered by survey and analyzed by structural equation modeling. The sample size in the present study was 254. The reliability of the all seven dimensions was estimated with Cronbach's alpha, composite reliability values and average variance extracted values. We determined whether the measurement model supports the convergent validity and discriminant validity by Exploratory factor analysis and Confirmatory Factor Analysis. For each pair of constructs, the square root of the average variance extracted values exceeded their correlations, thus supporting the discriminant validity of the constructs. Hypotheses were tested using the AMOS 18.0. As expected, the image congruence hypotheses were supported. The greater the degree of congruence between retailer image and self-image, the more favorable were consumers' retailer evaluations. The all two retailer-self image congruence (actual self-image congruence and ideal self-image congruence) affected customer based retailer equity. This result means that retailer-self image congruence is important cue for customers to estimate retailer equity. In other words, consumers are often more likely to prefer products and retail stores that have images similar to their own self-image. Especially, it appeared that effect for the ideal self-image congruence was consistently larger than the actual self-image congruence on the retailer equity. The results mean that consumers prefer or search for stores that have images compatible with consumer's perception of ideal-self. In addition, this study revealed that customers' estimations toward customer based retailer equity affected the repatronage intention. The results showed that all four dimensions (retailer awareness, retailer association, perceived retailer quality, and retailer loyalty) had positive effect on the repatronage intention. That is, management and investment to improve image congruence between retailer and consumers' self make customers' positive evaluation of retailer equity, and then the positive customer based retailer equity can enhance the repatonage intention. And to conclude, retailer's image management is an important part of successful retailer performance management, and the retailer-self image congruence is an important antecedent of retailer equity. Therefore, it is more important to develop and improve retailer's image similar to consumers' image. Given the pressure to provide increased image congruence, it is not surprising that retailers have made significant investments in enhancing the fit between retailer image and self image of consumer. The enhancing such self-image congruence may allow marketers to target customers who may be influenced by image appeals in advertising.
With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.
shows the channel structures analyzed in this study. Before the Internet channel is introduced, a monopoly manufacturer sells its products through an independent physical store. From this structure, the manufacturer could introduce its own Internet channel (MI). The independent physical store could also introduce its own Internet channel and coordinate it with the existing physical store (RI). An independent Internet retailer such as Amazon could enter this market (II). In this case, two types of independent retailers compete with each other. In this model, consumers are uniformly distributed on the two dimensional space. Consumer heterogeneity is captured by a consumer's geographical location (ci) and his disutility of using the Internet channel ( shows various market conditions captured by the two consumer heterogeneities. (a) illustrates a market with symmetric consumer distributions. The model captures explicitly the asymmetric distributions of consumer disutility in a market as well. In a market like that is represented in (c), the average consumer disutility of using an Internet store is relatively smaller than that of using a physical store. For example, this case represents the market in which 1) the product is suitable for Internet transactions (e.g., books) or 2) the level of E-Commerce readiness is high such as in Denmark or Finland. On the other hand, the average consumer disutility when using an Internet store is relatively greater than that of using a physical store in a market like summarizes the various scenarios of consumer distributions analyzed in this study. The range for disutility of using the Internet (
summarizes the analysis results. As the average travel cost in a market decreases while the average disutility of Internet use remains the same, average retail price, total quantity sold, physical store profit, monopoly manufacturer profit, and thus, total channel profit increase. On the other hand, the quantity sold through the Internet and the profit of the Internet store decrease with a decreasing average travel cost relative to the average disutility of Internet use. We find that a channel that has an advantage over the other kind of channel serves a larger portion of the market. In a market with a high average travel cost, in which the Internet store has a relative advantage over the physical store, for example, the Internet store becomes a mass-retailer serving a larger portion of the market. This result implies that the Internet becomes a more significant distribution channel in those markets characterized by greater geographical dispersion of buyers, or as consumers become more proficient in Internet usage. The results indicate that the degree of price discrimination also varies depending on the distribution of consumer disutility in a market. The manufacturer in a market in which the average travel cost is higher than the average disutility of using the Internet has a stronger incentive for price discrimination than the manufacturer in a market where the average travel cost is relatively lower. We also find that the manufacturer has a stronger incentive to maintain a high price level when the average travel cost in a market is relatively low. Additionally, the retail competition effect due to Internet channel introduction strengthens as average travel cost in a market decreases. This result indicates that a manufacturer's channel power relative to that of the independent physical retailer becomes stronger with a decreasing average travel cost. This implication is counter-intuitive, because it is widely believed that the negative impact of Internet channel introduction on a competing physical retailer is more significant in a market like Russia, where consumers are more geographically dispersed, than in a market like Hong Kong, that has a condensed geographic distribution of consumers.
The Pattern Analysis of Financial Distress for Non-audited Firms using Data Mining
(데이터마이닝 기법을 활용한 비외감기업의 부실화 유형 분석)
The Effect of Retailer-Self Image Congruence on Retailer Equity and Repatronage Intention
(자아이미지 일치성이 소매점자산과 고객의 재이용의도에 미치는 영향)
Sentiment Analysis of Korean Reviews Using CNN: Focusing on Morpheme Embedding
(CNN을 적용한 한국어 상품평 감성분석: 형태소 임베딩을 중심으로)
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