A two-level method for segmenting cytological images based on active contour model

Murashov, Dmitry

doi:10.1134/s1054661808010215

Cited by 6 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The segmentation of nuclei images is necessary for further nuclei image analysis, in particular, for calculation of feature values. The two-level method for automated segmentation of nuclei images based on the active contour model was developed (Murashov, 2005). The main features of the method are: a) the rough segmentation is achieved using the blue component of the image taking into account the properties of the stain; the precise correction is made using the green component; b) the modified Gaussian filter based on the heat equation with heat source or sink is implemented.…”

Section: Segmentation Specimen Imagesmentioning

confidence: 99%

NUCLEI IMAGES ANALYSIS - Technology, Diagnostic Features and Experimental Study

Gurevich¹,

Murashov²,

Salvetti³

et al. 2007

Proceedings of the Second International Conference on Computer Vision Theory and Applications

View full text Add to dashboard Cite

The information technology for automated morphologic analysis of the cytological slides, taken from patients with the lymphatic system tumours, was developed. The main contributions of the paper are the technology, the set of features for representation of nuclei images in pattern recognition problems (automated diagnostics), and experimental study of the technology and the features informativeness. The main components of the technology are: acquisition of cytological slides, method for segmentation of nuclei in the cytological slides, synthesis of the feature based nuclei description for subsequent classification, nuclei image analysis based on pattern recognition and scale-space techniques. The experiments confirmed efficiency of the developed technology. The discussion of the obtained results is given. The developed technology is implemented in the software system.

show abstract

Section: Segmentation Specimen Imagesmentioning

confidence: 99%

NUCLEI IMAGES ANALYSIS - Technology, Diagnostic Features and Experimental Study

Gurevich¹,

Murashov²,

Salvetti³

et al. 2007

Proceedings of the Second International Conference on Computer Vision Theory and Applications

View full text Add to dashboard Cite

show abstract

“…Several papers deal with different image processing methods for cell segmentation [2][3][4][5][6][7]. Depending on the image quality or the dyeing process, different segmentation methods may be the appropriate choice.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the image quality or the dyeing process, different segmentation methods may be the appropriate choice. If, for example, a noisy image has to be analyzed, the use of active contours could be advisable [3,[5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Context-Based Separation of Cell Clusters for the Automatic Biocompatibility Testing of Implant Materials

Buhl

Neumann

Schäfer

2014

Journal of Computational Medicine

View full text Add to dashboard Cite

This paper presents a new method to separate cells on microscopic surfaces joined together in cell clusters into individual cells. Important features of this method are that the remaining object geometry is preserved and few contour points are required for finding joints between neighboring cells. There are alternative methods such as morphological operations or the watershed transformation based on the inverse distance transformation but they have certain disadvantages compared to the method presented in this paper. The discussed method contains knowledge-based components in form of a decision function and exchangeable rules to avoid unwanted separations.

show abstract

“…The segmented nuclei can be used to determine the topology of the cells and serve as references. Different 2D nuclei segmentation methods have been reported, such as simple thresholding (15), watershed algorithm (16, 17), level set boundary searching using gradient flow (18), active contour model for closely packed nuclei (19–21), and ellipse detection algorithm (22). A few 3D nuclei segmentation approaches (23, 24) are recently reported as well.…”

mentioning

confidence: 99%

Evolving generalized Voronoi diagrams for accurate cellular image segmentation

Lee

Hariharan

et al. 2010

Cytometry Pt A

View full text Add to dashboard Cite

Analyzing cellular morphologies on a cell-by-cell basis is vital for drug discovery, cell biology, and many other biological studies. Interactions between cells in their culture environments cause cells to touch each other in acquired microscopy images. Because of this phenomenon, cell segmentation is a challenging task, especially when the cells are of similar brightness and of highly variable shapes. The concept of topological dependence and the maximum common boundary (MCB) algorithm are presented in our previous work (Yu et al., Cytometry Part A 2009;75A:289-297). However, the MCB algorithm suffers a few shortcomings, such as low computational efficiency and difficulties in generalizing to higher dimensions. To overcome these limitations, we present the evolving generalized Voronoi diagram (EGVD) algorithm. Utilizing image intensity and geometric information, EGVD preserves topological dependence easily in both 2D and 3D images, such that touching cells can be segmented satisfactorily. A systematic comparison with other methods demonstrates that EGVD is accurate and much more efficient. ' 2010 International Society for Advancement of Cytometry Key terms image cytometry; cell segmentation; fluorescence microscopy; generalized Voronoi diagram ANALYZING cellular morphology is crucial in drug discovery, cell and developmental biology. Automated high-content image-based approaches are preeminent tools, which enable thousands of images to be acquired. However, acquiring high quality images is only the first step towards biological discoveries. Image processingcomputer-based interrogation is essential to extract useful data from the images acquired. To extract the quantitative information on a cell-by-cell basis, a critical but challenging task is to segment individual cells. Once cells have been segmented successfully, subsequent analysis including cell counting, morphology, and migration becomes possible.In the images acquired by high-content screening experiments, the cells can be classified into three groups; (i) isolated, (ii) touching, and (iii) overlapping. In monolayer cell cultures, as shown in Figure 1, isolated cells are the cells that are well separated from other cells, touching cells are the cells that adhere to other cells and share some common boundaries, and overlapping cells are the cells that lie on top of each other with no clear boundary. Touching cells form the majority of cells in normal culture conditions. Segmentation of the isolated cells is straightforward as simple thresholding can successfully segment them, while segmentation of the touching cells is much more challenging.Among many existing cell segmentation approaches, active contours represented through the level set is a successful way to segment cells of irregular shapes. The Mumford-Shah model (1) is proposed to segment two-phase piecewise constant images. This model is enhanced by Chan-Vese (2) using the level set concept introduced in (3). However, the level set formulation lacks the ability to constrain

show abstract

A two-level method for segmenting cytological images based on active contour model

Cited by 6 publications

References 25 publications

NUCLEI IMAGES ANALYSIS - Technology, Diagnostic Features and Experimental Study

NUCLEI IMAGES ANALYSIS - Technology, Diagnostic Features and Experimental Study

Context-Based Separation of Cell Clusters for the Automatic Biocompatibility Testing of Implant Materials

Evolving generalized Voronoi diagrams for accurate cellular image segmentation

Contact Info

Product

Resources

About