• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.141-144
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• 2008

본 논문에서는 세포영상을 분할하고 분류하는 알고리즘을 제안한다. 우선, 배경으로부터 세포를 분할한 후, 학습데이터로부터 얻은 Compactness, Smoothness, Moments와 같은 형태학적 특징을 추출한다. 전경세포들이 분할된 후에, 보다 정밀한 세포분석을 위해서 군집세포(Overlapped Cell)와 독립세포(Isolated Cell)를 분류 할 수 있는 알고리즘의 개발이 필수적이다. 이를 위해서 본 논문에서는 베이지안 네트워크와 각 노드에 대한 3개의 확률밀도함수를 사용하여 각 세포 영역을 분류한다. 분류된 군집세포영역은 향후 정확한 세포 분석을 위해서 군집세포가 포함하는 독립세포의 수만큼 마커를 찾고, Watershed 알고리즘과 병합과정을 거쳐 하나의 독립세포를 분리하게 된다. 현미경으로부터 얻은 세포영상에 대한 실험 결과는 이전 논문들에서 제안한 방법들과 비교했을 때, 각 군집세포의 독립세포로의 분리 이전에 세포영역에 대한 분류과정을 먼저 수행하였기 때문에 분할 성능이 크게 향상되었음을 확인할 수 있다.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.84-92
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• 2011

The automated segmentation technique of cell region in Bio Images helps biologists understand complex functions of cells. It is mightly important in that it can process the analysis of cells automatically which has been done manually before. The conventional methods for segmentation of cell and nuclei from multi-channel images consist of two steps. In the first step nuclei are extracted from DNA channel, and used as initial contour for the second step. In the second step cytoplasm are segmented from Actin channel by using Active Contour model based on intensity. However, conventional studies have some limitation that they let the cell segmentation performance fall by not considering inhomogeneous intensity problem in cell images. Therefore, the paper consider correlation between DNA and Actin channel, and then proposes the preprocessing algorithm by which the brightness of cell inside in Actin channel can be compensated homogeneously by using DNA channel information. Experiment result show that the proposed preprocessing method improves the cell segmentation performance compared to the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.431-436
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• 2014

Cell segmentation which extracts cell objects from background is one of basic works in bio-imaging which analyze cell images acquired from live cells in cell culture. In the case of clear images, they have a bi-modal histogram distribution and segmentation of them can easily be performed by global threshold algorithm such as Otsu algorithm. But In the case of degraded images, it is difficult to get exact segmentation results. In this paper, we developed a cell segmentation system that it classify input images by the type of their histogram distribution and then apply a proper segmentation algorithm. If it has a bi-modal distribution, a global threshold algorithm is applied for segmentation. Otherwise it has a uni-modal distribution, our algorithm is performed. By experimentation, our system gave exact segmentation results for uni-modal cell images as well as bi-modal cell images.

• 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 KIISE
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• v.42 no.10
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• pp.1239-1246
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• 2015

Cell counting is extensively used to analyze cell growth in biomedical research, and as a result automated cell counting methods have been developed to provide a more convenient and means to analyze cell growth. However, there are still many challenges to improving the accuracy of the cell counting for cells that proliferate abnormally, divide rapidly, and cluster easily, such as cancer cells. In this paper, we present an automated cell counting method for HeLa cells, which are used as reference for cancer research. We recognize and classify the morphological conditions of the cells by using a cell segmentation algorithm based on cell membrane extraction, and we then apply a cell back-tracking algorithm to improve the cell counting accuracy in cell clusters that have indistinct cell boundary lines. The experimental results indicate that our proposed segmentation method can identify each of the cells more accurately when compared to existing methods and, consequently, can improve the cell counting accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1153-1158
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• 2006

The classification of the background and cell areas is very important research area because of the ambiguous boundary. In this paper, the region of cell is extracted from an image of uterine cervical cytodiagnosis using the region growing method that increases the region of interest based on similarity between pixels. Segmented image from background and cell areas is binarized using a threshold value. And then 8-directional tracking algorithm for contour lines is applied to extract the cell area. First, the extracted nucleus is transformed to RGB color that is the original image. Second, the K-means clustering algorithm is employed to classify RGB pixels to the R, G, and B channels, respectively. Third, the Hue information of nucleus is extracted from the HSI models that is the transformation of the clustering values in R, G, and B channels. The backpropagation algorithm is employed to classify and identify the normal or abnormal nucleus.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.19-27
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• 2010

In recent years, high-throughput genome-wide RNA interference screening is emerging as an essential tool to biologists in understanding complex cellular processes. The manual analysis of the large number of images produced in each study spends much time and the labor. Hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. However, those factors such as the region overlapping, a variety of shapes, and non-uniform local characteristics of cellular images become obstacles to efficient cell segmentation. To avoid the problem, a new watershed-based cell segmentation algorithm using a localized segmentation method and a feature vector is proposed in this paper. Localized approach in segmentation resolves the problems caused by a variety of shapes and non-uniform characteristics. In addition, the poor performance of segmentation in overlapped regions can be improved by taking advantage of a feature vector whose component features complement each other. Simulation results show that the proposed method improves the segmentation performance compared to the method in Cellprofiler.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.533-535
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• 1999

자궁경부진 세포인식 시스템에 있어서 가장 중요한 것이 영상처리를 이용하여 세포핵과 세포질을 추출하여 세포의 형태적인 정보를 알아내는 과정이다. 기존의 전역 thresholding 기법이나 region growing의 경우는 pap smear 검사를 통해 얻어진 세포 영상을 분할할 수 있는 region growing 기법을 제안한다. 제안된 region growing 기법은 초기에 seed를 검출할 때 local threshold growing 기법을 제안한다. 제안된 region growing 기법은 초기에 seed를 검출할 때 local threshold 개념을 도입하여 seed의 검출을 고르게 하고, 2가지 확장 조건을 사용하여 영역을 확장한다. 첫 번째 확장 조건은 비정상 세포나 artifact가 많아서 어둡게 나타나는 영상이나 세포질과 배경의 경계가 뚜렷하지 않아서 세포질의 구별이 어려운 영상의 영역 분할이 가능하도록 그 특성을 반영하고, 두 번째 조건은 세포가 흡수하는 빛의 양이 일정하다는 가정으로 영상에서의 지역 특성(gray level, color 등을 반영한다. 제안된 기법은 정상세포 영상뿐만 아니라 비정상 세포 영상에 대하여 over-segment나 under-segment하는 경우를 줄여서 영역 분할에 좋은 결과를 보인다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.464-468
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• 2009

In this paper we propose an image-based approach, which is different from the traditional flow cytometric method to detect shape of apoptosis cells. This method can overcome the defects of cytometry and give precise recognition of apoptosis cells. In this work K-means clustering was used to do the rough segmentation and an active contour model, called 'snake' was used to do the precise edge detection. And then some features were extracted including physical feature, shape descriptor and texture features of the apoptosis cells. Finally a Mahalanobis distance classifier classifies the segmentation images as apoptosis and non-apoptosis cell.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.99-106
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• 2007

In this paper, we present an AAW(Adaptive Attention Window) based cell image segmentation method. For semantic AAW detection we create an initial Attention Window by using a luminance map. Then the initial AW is reduced to the optimal size of the real ROI(Region of Interest) by using a quad tree segmentation. The purpose of AAW is to remove the background and to reduce the amount of processing time for segmenting ROIs. Experimental results show that the proposed method segments one or more ROIs efficiently and gives the similar segmentation result as compared with the human perception.