A new strategy to obtain robust markers for blood vessels segmentation by using the watersheds method

Rodríguez, Roberto; Alarcón, Teresa E.; Pacheco, Oriana

doi:10.1016/j.compbiomed.2004.06.003

Cited by 32 publications

(16 citation statements)

References 15 publications

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“…In the process of flooding, complete the merging of small regions. we propose an improved method, which region average gray value and edge strength criterion is used in merging operation and has a good effect on segmentation [7,8].…”

Section: The Methods Of Marker and Region Mergingmentioning

confidence: 99%

Silicon Wafer Defect Extraction Based on Morphological Filter and Watershed Algorithm

Huang

Zhang

et al. 2008

2008 International Conference on Computer Science and Software Engineering

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Defect extraction techniques are studied regarding the silicon wafer surface defect. We design a new filter based on multiple structuring elements and suggest an improved markerbased and region merging watershed. To begin with, the filter which generalized close-opening and open-closing filter based on the morphological filter with multiple structuring elements is introduced to eliminate the noise and simplify the image and morphological gradient image while preserving the details. And then in order to reduce the over-segmentation of the watershed algorithm, this paper suggests an improved marker-based and region merging method, region average gray value and edge strength criterion is used in merging operation and has a good effect on segmentation. Finally, the improved watershed algorithm is applied to the filtered gradient image to get the defect contours. The experiments show that this method can eliminate the noises and extract accurately location and closed region contours, which lays a good foundation for defect feature extraction and selection.

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Section: The Methods Of Marker and Region Mergingmentioning

confidence: 99%

Silicon Wafer Defect Extraction Based on Morphological Filter and Watershed Algorithm

Huang

Zhang

et al. 2008

2008 International Conference on Computer Science and Software Engineering

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“…vertebrae labelling in 3D CT data [45], or left ventricular myocardium segmentation from 3D+t MR data [46]). In particular, mathematical morphology operators that have been involved in vessel segmentation methods include: watershed transform [47,48], grey-level hit-or-miss transform [5,49], or connected filtering based on component-trees [50,51]. The usefulness of such operators is justified by their intrinsic capacity to model morphological information, and then to enable anatomical knowledge-guided approaches.…”

Section: Vessel Segmentation and Mathematical Morphologymentioning

confidence: 99%

3D segmentation of coronary arteries based on advanced mathematical morphology techniques

Bouraoui¹,

Ronse²,

Baruthio³

et al. 2010

Computerized Medical Imaging and Graphics

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In this article, we propose an automatic algorithm for coronary artery segmentation from 3D X-ray data sequences of a cardiac cycle (3D-CT scan, 64 detectors, 10 phases). This method is based on recent mathematical morphology techniques (some of them being extended in this article). It is also guided by anatomical knowledge, using discrete geometric tools to fit on the artery shape independently from any perturbation of the data. The application of the method on a validation dataset (60 images: 20 patients in 3 phases) led to 90% correct (and automatically obtained) segmentations, the 10% remaining cases corresponding to images where the SNR was very low.

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“…To identify areas of oral cancer, Jiang et al (2004) developed a fluorescent image system combined with a color image fusion algorithm. Rodríguez et al (2005) presented a strategy to automatically segment blood vessels from microscopic images of tumor biopsies, and later presented a modification of Otsu's method for segmenting vessels (Rodríguez, 2006). Loukas and Linney (2005) presented a relaxationlabeling algorithm for quantitative evaluation of tumor vasculature from microscopic images.…”

Section: Introductionmentioning

confidence: 99%

Color‐based tumor tissue segmentation for the automated estimation of oral cancer parameters

Sun

Wang

Chang

et al. 2009

Microscopy Res & Technique

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This article presents an automatic color-based feature extraction system for parameter estimation of oral cancer from optical microscopic images. The system first reduces image-to-image variations by means of color normalization. We then construct a database which consists of typical cancer images. The color parameters extracted from this database are then used in automated online sampling from oral cancer images. Principal component analysis is subsequently used to divide the color features into four tissue types. Each pixel in the cancer image is then classified into the corresponding tissue types based on the Mahalanobis distance. The aforementioned procedures are all fully automated; in particular, the automated sampling step greatly reduces the need for intensive labor in manual sampling and training. Experiments reveal high levels of consistency among the results achieved using the manual, semiautomatic, and fully automatic methods. Parameter comparisons between the four cancer stages are conducted, and only the mean parameters between early and late cancer stages are statistically different. In summary, the proposed system provides a useful and convenient tool for automatic segmentation and evaluation for stained biopsy samples of oral cancer. This tool can also be modified and applied to other tissue images with similar staining conditions.

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A new strategy to obtain robust markers for blood vessels segmentation by using the watersheds method

Cited by 32 publications

References 15 publications

Silicon Wafer Defect Extraction Based on Morphological Filter and Watershed Algorithm

Silicon Wafer Defect Extraction Based on Morphological Filter and Watershed Algorithm

3D segmentation of coronary arteries based on advanced mathematical morphology techniques

Color‐based tumor tissue segmentation for the automated estimation of oral cancer parameters

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