• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.47-51
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• 2013

Existing buildings now represent the greatest opportunity to improve building energy efficiency. Building performance analysis is becoming increasingly important because decision makers can have a better visualization of their building's performance and quickly make the solution for improving building energy efficiency and reducing environmental impacts. Nowadays, building information models (BIMs) have been widely created during the design phase of new buildings, and it can be easily imported to third party software to conduct various analyses. However, a BIM is not always available for all existing buildings. Even if a BIM is available during the design and construction phases, it is very challenging to keep updating it while a building is aged. A manual process to create or update a BIM is very time consuming and labor intensive. A laser scanning technology has been a popular tool to create as-is BIM. However it still needs labor-intensive manual processes to create a BIM out of point clouds. This paper introduces automatic as-is simplified BIM creation from point clouds for energy simulations. A framework of decision support system that can assist decision makers on retrofits for existing buildings is introduced as well. A case study on a residential house was tested in this study to validate the proposed framework, and the technical feasibility of the developed system was positively demonstrated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.588-590
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• 2022

Sharing both locations and positions of ships makes it possible to utilize critical item for their safe and efficient navigation in such diversifying meantime environments. AIS is the representative technology for the sharing solutions. The AIS is even used in airspace and ground stations, so that AIS could facilitate the ships' safety navigation and their prevention/rescue from endangers. Due to AIS's many advantages, IMO(International Maritime Organization) made adapting the AIS mandatory for international passenger ships and the ships that are over than 300 tons. AIS uses VHF band areas for transmitting information and the information can be propagated to several hundreds km in range. Due to the large range, AIS monitoring system can acquire huge number of ships, which makes system performance lower and busier. In this paper, we propose the strategy of AIS information extraction for efficient monitoring system. Thus, the monitoring system has higher processing performance and lower network usage. As well as, the proposal affects the monitoring system has more capacity to include other systems' targets, in result.

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.470-479
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• 2010

In relation to the extraction of opinion words, it may be difficult to directly apply most of the methods suggested in existing English studies to the Korean language. Additionally, the manual method suggested by studies in Korea poses a problem with the extraction of opinion words in that it takes a long time. In addition, English thesaurus-based extraction of Korean opinion words leaves a challenge to reconsider the deterioration of precision attributed to the one to one mismatching between Korean and English words. Studies based on Korean phrase analyzers may potentially fail due to the fact that they select opinion words with a low level of frequency. Therefore, this study will suggest the k-Structure (k=5 or 8) method, which may possibly improve the precision while mutually complementing existing studies in Korea, in automatically extracting opinion words from a simple sentence in a given Korean product review. A simple sentence is defined to be composed of at least 3 words, i.e., a sentence including an opinion word in ${\pm}2$ distance from the attribute name (e.g., the 'battery' of a camera) of a evaluated product (e.g., a 'camera'). In the performance experiment, the precision of those opinion words for 8 previously given attribute names were automatically extracted and estimated for 1,868 product reviews collected from major domestic shopping malls, by using k-Structure. The results showed that k=5 led to a recall of 79.0% and a precision of 87.0%; while k=8 led to a recall of 92.35% and a precision of 89.3%. Also, a test was conducted using PMI-IR (Pointwise Mutual Information - Information Retrieval) out of those methods suggested in English studies, which resulted in a recall of 55% and a precision of 57%.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.265-269
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• 2007

In this paper, we propose an automatic extraction method of gastric cancer region from electronic endoscopic images. We use the brightness and saturation of HSI in removing noises by illumination and shadows by the crookedness occurring in the endoscopic process. We partition the image into several areas with similar pigments of hemoglobin using IHb. The candidate areas for gastric cancer are defined as the areas that have high hemoglobin pigments and high value in every channel of RGB. Then the morphological characteristics of gastric cancer are used to decide the target region. In experiment, our method is sufficiently accurate in that it correctly identifies most cases (18 out of 20 cases) from real electronic endoscopic images, obtained by expert endoscopists.

• Journal of Soil and Groundwater Environment
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• v.18 no.1
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• pp.112-122
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• 2013

Current status of soil contamination with fluorine and its source were investigated. The basic principles and procedures of various techniques for the analysis of fluorine contents in soil and solid phase samples were summarized in this review. Analysis of fluorine in solid matrices can be achieved by two types of techniques: (i) UV/Vis spectrophotometer or ion selective electrode (ISE) analysis after performing appropriate extraction steps and (ii) direct solid analysis. As the former cases, the standard method of Korean ministry of environment, alkali fusion-ISE method, pyrohydrolysis, oxygen bomb combustion, aqua regia digestion-automatic analysis, and sequential extraction-ISE method were introduced. In addition, direct analysis methods (i.e., X-ray fluorescence spectrometry and proton induced gamma-ray emission spectrometry) and atomic spectrometry combining with the equipment for introducing solid phase sample were also reviewed. Fluorine analysis techniques can be reasonably selected through site-specific information such as matrix condition, contamination level, the amount of samples and the principles of various methods for the analysis of fluorine presented in this review.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2059-2064
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• 2004

Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.246-253
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• 2016

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.670-675
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• 2015

Medical imaging is one of the most powerful tools for gaining information about internal organs and tissues. It is a challenging task to develop sophisticated image analysis methods in order to improve the accuracy of diagnosis. The objective of this paper is to develop a Computer Aided Diagnostics (CAD) scheme for Brain Tumour detection from Magnetic Resonance Image (MRI) using active contour models and to investigate with several approaches for improving CAD performances. The problem in clinical medicine is the automatic detection of brain Tumours with maximum accuracy and in less time. This work involves the following steps: i) Segmentation performed by Fuzzy Clustering with Level Set Method (FCMLSM) and performance is compared with snake models based on Balloon force and Gradient Vector Force (GVF), Distance Regularized Level Set Method (DRLSE). ii) Feature extraction done by Shape and Texture based features. iii) Brain Tumour detection performed by various tree classifiers. Based on investigation FCMLSM is well suited segmentation method and Random Forest is the most optimum classifier for this problem. This method gives accuracy of 97% and with minimum classification error. The time taken to detect Tumour is approximately 2 mins for an examination (30 slices).

• Proceedings of the KSRS Conference
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• v.2
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• pp.610-614
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• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.