• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1245-1254
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• 2020

In this study, the limitations of the existing water level detection algorithm using CCTV images were recognized and the water level detection algorithm was improved by applying the Region growing technique. It applied three techniques (Horizontal projection profile, Texture analysis, and Optical flow) to estimate the water area, and the results were analyzed in a comprehensive analysis to select the initial water area. The water level was then continuously detected by the Region growing technique, referring to the initial water area. As a result, it was possible to confirm that the exact level of water was detected without being affected by environmental factors compared to the existing level detection algorithm, which had frequent mis-detection phenomena depending on the surrounding environmental factors. In addition, the water level was detected in the video showing flooded roads in urban areas, not in the video of the river. These results are believed to be able to supplement the difficulty of monitoring at all times with limited manpower by automatically detecting the level of water through numerous CCTV footage installed throughout the country, and to contribute to laying the foundation for preventing disasters caused by torrential rains and typhoons in advance.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.271-280
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• 2009

In this paper, we propose a smoke detection method using region growing method in outdoor video sequences. Our proposed method is composed of three steps; the initial change area detection step, the boundary finding and expanding step, and the smoke classification step. In the first step, we use a background subtraction to detect changed areas in the current input frame against the background image. In difference images of the background subtraction, we calculate a binary image using a threshold value and apply morphology operations to the binary image to remove noises. In the second step, we find boundaries of the changed areas using labeling algorithm and expand the boundaries to their neighbors using the region growing algorithm. In the final step, ellipses of the boundaries are estimated using moments. We classify whether the boundary is smoke by using the temporal information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.335-339
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• 2006

자궁 경부 세포진 영상의 핵 영역 분할은 자궁 경부암 자동화 검색 시스템의 가장 어렵고도 중요한 분야로 알려져 있다. 자궁 경부 세포진 영상은 배경과 세포의 영역이 확실히 구분되지 않는 경우가 많기 때문에 이들을 확실히 구분하는 것이 매우 중요하다. 본 논문에서는 이러한 문제점을 해결하기 위해 자궁 경부 세포진 영상에서 Region growing 기법을 적용하여 세포 영상을 분할한다. Region growing 기법은 화소간의 유사도를 측정하여 영역을 확장하여 분할하는 방법이다. 세포와 배경이 분할된 영상을 일정 임계값을 이용하여 영상을 이진화 한 후, 8방향 윤곽선 추적 알고리즘을 이용해 세포 영역을 추출한다. 추출된 세포 영역을 원 영상인 RGB 컬러로 변환한 후에 K-means 알고리즘을 적용하여 각 세포 영역의 RGB 화소를 R, G, B 채널로 각각 분리하여 클러스터링한다. 클러스터링된 각각의 R, G, B 채널의 클러스터 값을 이용하여 HSI 모델로 변환시킨 후에 세포핵 영역의 Hue 정보를 추출한다. 추출된 세포핵의 특징을 오류 역전파 알고리즘을 적용하여 정상 세포와 비정상 세포를 분류하고 인식한다.

• Journal of Biomedical Engineering Research
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• v.25 no.4
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• pp.235-242
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• 2004

In this paper, we proposed the algorithm for detection of microtalcification by automatic threshold decision based on region growing method. The region for optimal threshold is grown from local maximum pixel by increasing repeatedly threshold in microralcification candidate region. Then, the optimal threshold is automatically decided at the maximum value of the contrast and edge sharpness in this region. Microcalcifications could be efficiently detected as satisfied result that true positive ratio is 81.5% and average false positive numbers are 1.1 about total 299 microcalcifirations in real image. In a result, we showed that this algorithm can be used to aid diagnostic-radiologist for the diagnosis of the early phase of breast cancer.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.59-62
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• 2002

Watershed Segmentation with Multiple Merging Conditions in Region Growing Process The watershed segmentation method holds the merits of edge-based and region-based methods together, but still shows some problems such as over segmentation and merging fault. We propose an algorithm which overcomes the problems of the watershed method and shows efficient performance for .general images, not for specific ones. The algorithm segments or merges regions by thresholding the depths of the catchment basins, the similarities and the sizes of the regions. The experimental results shows the reduction of the number of the segmented regions that are suitable to human visual system and consciousness.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.72-75
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• 2008

Image segmentation techniques becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Seeded Region Growing (SRG) and Edge Information. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying SRG. Finally the region merging process, using region adjacency graph (RAG), was carried out to get the final segmentation result. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.1-8
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• 2021

Prostate cancer is one of the most diagnosed malignancies found across the world today. American cancer society in recent research predicted that over 174,600 new prostate cancer cases found and nearly 31,620 death cases recorded. Researchers are developing modest and accurate methodologies to detect and diagnose prostate cancer. Recent work has been done in radiology to detect prostate tumors using ultrasound imaging and resonance imaging techniques. Transrectal ultrasound and Magnetic resonance images of the prostate gland help in the detection of cancer in the prostate gland. The proposed paper is based on comparison and analysis between two novel image segmentation approaches. Seed region growing and cluster based image segmentation is used to extract the region from trans-rectal ultrasound prostate and MR prostate images. The region of extraction represents the abnormality area that presents in men's prostate gland. Detection of such abnormalities in the prostate gland helps in the identification and treatment of prostate cancer

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2313-2319
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• 2020

The goal of this project is to achieve an accurate segmentation of the pulmonary tumors besides shortening the time and increasing the accuracy. Here, improved region growing (IRG) algorithm is introduced in order to segment the lung tumor with a sufficient accuracy in a shorter time compared to the other basics methods. This comprehensive algorithm was applied on 4 patients CT images and the results of the various steps on segmentation improvement shown 98% accuracy as compared to the basic algorithm. The combination of "multipoint growth start" produced a desirable outcome in accurately bounding the tumor. The proposed algorithm improved tumor identification by less than 13% along with a sufficient percentage of compliance accuracy.

• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.353-359
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• 2004

This study proposes an approach to unsupervisedly estimate the number of classes and the parameters of defining the classes in order to train the classifier. In the proposed method, the image is segmented using a spatial region growing based on hierarchical clustering, and fuzzy training is then employed to find the sample classes that well represent the ground truth. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes. The experimental results show that the new scheme proposed in this study could be used to select the regions with different characteristics existed on the scene of observed image as an alternative of field survey that is so expensive.

• Korean Journal of Remote Sensing
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• v.21 no.1
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• pp.83-89
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• 2005

This study utilized a spatial region growing segmentation and a classification using fuzzy membership vectors to detect the changes in the images observed at different dates. Consider two co-registered images of the same scene, and one image is supposed to have the class map of the scene at the observation time. The method performs the unsupervised segmentation and the fuzzy classification for the other image, and then detects the changes in the scene by examining the changes in the fuzzy membership vectors of the segmented regions in the classification procedure. The algorithm was evaluated with simulated images and then applied to a real scene of the Korean Peninsula using the KOMPSAT-l EOC images. In the expertments, the proposed method showed a great performance for detecting changes in land-cover.