Browsing by Subject "Cell nucleus"
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Item Open Access Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images(IEEE, 2013) Arslan, S.; Ersahin, T.; Cetin-Atalay, R.; Gunduz-Demir, C.More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms. © 2012 IEEE.Item Open Access Color graphs for automated cancer diagnosis and grading(Institute of Electrical and Electronics Engineers, 2010-03) Altunbay, D.; Cigir, C.; Sokmensuer, C.; Gunduz Demir, C.This paper reports a new structural method to mathematically represent and quantify a tissue for the purpose of automated and objective cancer diagnosis and grading. Unlike the previous structural methods, which quantify a tissue considering the spatial distributions of its cell nuclei, the proposed method relies on the use of distributions of multiple tissue components for the representation. To this end, it constructs a graph on multiple tissue components and colors its edges depending on the component types of their endpoints. Subsequently, it extracts a new set of structural features from these color graphs and uses these features in the classification of tissues. Working with the images of colon tissues, our experiments demonstrate that the color-graph approach leads to 82.65% test accuracy and that it significantly improves the performance of its counterparts. © 2006 IEEE.Item Open Access Iterative H-minima-based marker-controlled watershed for cell nucleus segmentation(John Wiley & Sons, Inc., 2016) Koyuncu, Can Fahrettin; Akhan, Ece; Ersahin, T.; Cetin Atalay, R.; Gunduz Demir, ÇiğdemAutomated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker-controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts.Item Open Access Metastasis suppressor proteins in cutaneous squamous cell carcinoma(Elsevier, 2016-07) Bozdogan, O.; Vargel, I.; Cavusoglu, T.; Karabulut, A. A.; Karahan, G.; Sayar, N.; Atasoy, P.; Yulug, I. G.Cutaneous squamous cell carcinomas (cSCCs) are common human carcinomas. Despite having metastasizing capacities, they usually show less aggressive progression compared to squamous cell carcinoma (SCC) of other organs. Metastasis suppressor proteins (MSPs) are a group of proteins that control and slow-down the metastatic process. In this study, we established the importance of seven well-defined MSPs including NDRG1, NM23-H1, RhoGDI2, E-cadherin, CD82/KAI1, MKK4, and AKAP12 in cSCCs. Protein expression levels of the selected MSPs were detected in 32 cSCCs, 6 in situ SCCs, and two skin cell lines (HaCaT, A-431) by immunohistochemistry. The results were evaluated semi-quantitatively using the HSCORE system. In addition, mRNA expression levels were detected by qRT-PCR in the cell lines. The HSCOREs of NM23-H1 were similar in cSCCs and normal skin tissues, while RGHOGDI2, E-cadherin and AKAP12 were significantly downregulated in cSCCs compared to normal skin. The levels of MKK4, NDRG1 and CD82 were partially conserved in cSCCs. In stage I SCCs, nuclear staining of NM23-H1 (NM23-H1nuc) was significantly lower than in stage II/III SCCs. Only nuclear staining of MKK4 (MKK4nuc) showed significantly higher scores in in situ carcinomas compared to invasive SCCs. In conclusion, similar to other human tumors, we have demonstrated complex differential expression patterns for the MSPs in in-situ and invasive cSCCs. This complex MSP signature warrants further biological and experimental pathway research.