• Journal of Radiopharmaceuticals and Molecular Probes
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• v.4 no.1
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• pp.36-42
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• 2018

Molecular imaging refers to detect the biochemical process in living organisms at the cellular and molecular levels and to quantify them. Due to several advantages of nanomaterials, various molecular images using nanomaterials are being tried. Attempts have been made to combine nanoparticles, known as micro- or nanosized nanomaterials, with radioactive isotopes for molecular imaging probe. The radiolabeled nanoparticles will expend the molecular imaging due to nanoparticle's size-dependent nature. In particular, iron oxide nanoparticles can be used for magnetic resonance imaging, can be adjusted in size, easily functionalized, and biocompatible, making it a very good platform for molecular imaging. In addition, iron oxide nanoparticles may be the best example for a new approach to molecular imaging techniques. In this paper, we introduce various methods for preparation of iron oxide nanoparticle combined with radioisotope starting from various synthesis methods of iron oxide nanoparticles to utilize iron oxide nanoparticles as a platform for molecular imaging through radioactive labeling.

• ETRI Journal
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• v.37 no.5
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• pp.1023-1031
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• 2015

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.

• Fashion & Textile Research Journal
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• v.24 no.1
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• pp.117-126
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• 2022

This study aims to analyze the sizing information of women's ready-to-wear pants as indicated on online retail websites and to suggest better sizing communication that can assist customers in making successful apparel size selections. We gathered size specifications and size reference information for basic straight pants from 34 online apparel retail websites. Although the Korean standard recommends labeling the body dimension-based sizing code and specification, most websites preferred to use various types of sizing codes. Body measurements were only used by a few websites, and garment dimension descriptions were the most common method to indicate product size. Many websites provided size reference information through customer review boards and fit model images, however, there was insufficient body size information to allow customers to infer the fit of their body type. When using the size guidance tools, the major data input points were stature and weight measurements. However, the waist measurements of pants sizes guided only by stature and weight values revealed inconsistent ease allowance for corresponding body size populations, especially in the overweight group. Based on our findings, we propose a more effective method of communicating the size information of pants online. We expect that this will contribute to the efficiency of online apparel product display and build a better shopping environment that satisfies both sellers and consumers.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.188-194
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• 2013

In this paper, we proposed a new method for pulmonary vessels image segmentation and refinement from pulmonary image. Proposed method consist of following five steps. First, threshold estimation is performed by polynomial regression analysis of histogram variation rate of the pulmonary image. Second, segmentation of pulmonary vessels object is performed by density-based segmentation method based on estimated threshold in first step. Third, 2D connected component labeling method is applied to segmented pulmonary vessels. The seed point of both side diaphragms is determined by eccentricity and size of component. Fourth step is diaphragm extraction by 3D region growing method at the determined seed point. Finally, noise cancelation of pulmonary vessels image is performed by 3D connected component labeling method. The experimental result is showed accurately pulmonary vessels image segmentation, the diaphragm extraction and the noise cancelation of the pulmonary vessels image.

• Journal of the Korean Home Economics Association
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• v.38 no.12
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• pp.29-45
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• 2000

The purposes of this study were to determine what factors influence risk perceptions of females and males for milk produced using food-related biotechnology, to test whether risk perceptions or other factors influence self-protection actions and to estimate milk demand response in light of self-protection actions and other economic and demographic factors. The expected utility model was applied to explain the way consumers would take self-protection actions regarding risk perceptions and to drive milk demand. Telephone interviews were conducted and the data were collected from households(females=1,029, males=437) nationwide in the U.S. And the data were analyzed by Heckman two-step method using the software package LIMDEP. Risk perceptions were found to be influenced not by demographic factors but by outrage factors as well as attitudinal factors in both females and males, although some factors were different. In addition, risk perceptions and labeling availability were found to significantly influence self-protection actions in both groups. Furthermore, as an important concern in this study, self-protection action was found to significantly influence milk demand in only male group, implying a consistent behavior of males. Also milk price and household size were found to significantly influence milk demand in both groups. In fact, the results did demonstrate that labeling availability significantly influenced self-protection actions. That is, in markets where labeled laternatives were present, concerned consumers were more likely to self protect by substituting to these products. A policy implication of this result is that labeling food products produced using biotechnology enhances consumer choice. Hence, consumer could express a more accurate demand response and reduce the perceived food safety risk. Furthermore, education for females might be necessary to have a consistent behavior because self-protection action did not significantly influence female's milk demand, though they have greater risk perceptions than males have.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.9
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• pp.801-805
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• 2012

This paper proposes dynamic thresholding scheme for fingerprint identification. As a user authentication method by fingerprint recognition technology, verification method based on 1:1 matching was mainly used in the past, but identification method based on 1:N matching is generally used recently. The control of the value of FAR is very important in the application areas such as access control and time attendance systems. This paper proposes dynamic thresholding scheme which could properly control the value of FAR according to the field of applications and size of the fingerprints database.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.3
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• pp.224-230
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• 2002

In this paper, a counting algorithm hybridized with an adaptive automatic thresholding method based on Otsu's method and the algorithm that elongates markers obtained by the well-known watershed algorithm is proposed to enhance the exactness of the microcell counting in microscopic images. The proposed counting algorithm can be stated as follows. The transformed full image captured by CCD camera set up at microscope is divided into cropped images of m$\times$n blocks with an appropriate size. The thresholding value of the cropped image is obtained by Otsu's method and the image is transformed into binary image. The microbial cell images below prespecified pixels are regarded as noise and are removed in tile binary image. The smoothing procedure is done by the area opening and the morphological filter. Watershed algorithm and the elongating marker algorithm are applied. By repeating the above stated procedure for m$\times$n blocks, the m$\times$n segmented images are obtained. A superposed image with the size of 640$\times$480 pixels as same as original image is obtained from the m$\times$n segmented block images. By labeling the superposed image, the counting result on the image of microbial cells is achieved. To prove the effectiveness of the proposed mettled in counting the microbial cell on the image, we used Acinetobacter sp., a kind of ammonia-oxidizing bacteria, and compared the proposed method with the global Otsu's method the traditional watershed algorithm based on global thresholding value and human visual method. The result counted by the proposed method shows more approximated result to the human visual counting method than the result counted by any other method.

• International Journal of Contents
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• v.8 no.1
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• pp.30-38
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• 2012

Text extraction and binarization are the important pre-processing steps for text recognition. The performance of text binarization strongly related to the accuracy of recognition stage. In our proposed method, the first stage based on line detection and shape feature analysis applied to locate the position of a business card and detect the shape from the complex environment. In the second stage, several local regions contained the possible text components are separated based on the projection histogram. In each local region, the pixels grouped into several connected components based on the connected component labeling and projection histogram. Then, classify each connect component into text region and reject the non-text region based on the feature information analysis such as size of connected component and stroke width estimation.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.6
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• pp.3699-3703
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• 2013

Background: This study aimed to show the localization of estrogen / progesterone receptors, human epidermal growth factor receptor 2 (Her-2) and protein 53 (p53) by immunohistochemistry in a series of consecutive breast cancer patients. Materials and Methods: The study covered invasive breast cancers from 299 patients presenting at the Oncogenetic Clinic and Pathology Centers of Ahwaz Jondishapour University of Medical Sciences Hospital in Iran during the time period from 2009 to 2011. The Scarff-Bloom Richardson scoring method was used. Results: Of the 299, 27% (80/299) were <40, 33% (100/299) were 41-50, and the remaining 40% (119/299) were>50 years old. The highest incidence of breast cancer in this study population was in the group of more than 50 year age, and the most common histological type of breast cancer was the invasive ductal carcinoma, which accounted for 68% (203/299) of the cases. Out of possible total of 207, 6% (13/207), 41% (85/207), and 53% (109/207) were scored as grade I, II, III, respectively. Conclusion: Our findings demonstrated a lack of association between labeling for the markers studied and tumor size and age of the patients. We confirmed an association between ER labeling and nuclear grade of breast cancer. The conflicting results obtained compared with the literature be because of differences in the immunohistochemical techniques applied in the various studies and to the scoring systems used.

• Journal of IKEEE
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• v.24 no.4
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• pp.1141-1147
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• 2020

The hornet species are so similar in shape that they are difficult for non-experts to classify, and because the size of the objects is small and move fast, it is more difficult to detect and classify the species in real time. In this paper, we developed a system that classifies hornets species in real time based on a deep learning algorithm using a boundary box. In order to minimize the background area included in the bounding box when labeling the training image, we propose a method of selecting only the head and body of the hornet. It also experimentally compares existing boundary box-based object recognition algorithms to find the best algorithms that can detect wasps in real time and classify their species. As a result of the experiment, when the mish function was applied as the activation function of the convolution layer and the hornet images were tested using the YOLOv4 model with the Spatial Attention Module (SAM) applied before the object detection block, the average precision was 97.89% and the average recall was 98.69%.