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A Combinational Clustering Based Method for cDNA Microarray Image Segmentation.

Shao G, Li T, Zuo W, Wu S, Liu T - PLoS ONE (2015)

Bottom Line: Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously.First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality.In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets.

View Article: PubMed Central - PubMed

Affiliation: Department of Automation, Xiamen University, Xiamen, P.R. China.

ABSTRACT
Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1) Stanford Microarray Database (SMD), 2) Gene Expression Omnibus (GEO), 3) Baylor College of Medicine (BCM), 4) Swiss Institute of Bioinformatics (SIB), 5) Joe DeRisi's individual tiff files (DeRisi), and 6) University of California, San Francisco (UCSF), indicate that the improved approach is more robust and sensitive to weak spots. More importantly, it can obtain higher segmentation accuracy in the presence of noise, artifacts and weakly expressed spots compared with the other four methods.

No MeSH data available.


Related in: MedlinePlus

Segmentation examples fail to obtain boundary of spots.
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pone.0133025.g010: Segmentation examples fail to obtain boundary of spots.

Mentions: Furthermore, Fig 10 exhibits some examples that our improved method fails to obtain the boundaries of spots. For example, a crescent moon shape spot is obtained as shown in Fig 10a owing to the noises in that region have similar fluorescent with that of spot. Due to noises have higher gray values than spots so they are classified into spots by mistake as shown in Fig 10b,10c,10d,and 10e. In addition, a square shape spot is segmented in Fig 10f because that area is fulfilled with noises.


A Combinational Clustering Based Method for cDNA Microarray Image Segmentation.

Shao G, Li T, Zuo W, Wu S, Liu T - PLoS ONE (2015)

Segmentation examples fail to obtain boundary of spots.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4524615&req=5

pone.0133025.g010: Segmentation examples fail to obtain boundary of spots.
Mentions: Furthermore, Fig 10 exhibits some examples that our improved method fails to obtain the boundaries of spots. For example, a crescent moon shape spot is obtained as shown in Fig 10a owing to the noises in that region have similar fluorescent with that of spot. Due to noises have higher gray values than spots so they are classified into spots by mistake as shown in Fig 10b,10c,10d,and 10e. In addition, a square shape spot is segmented in Fig 10f because that area is fulfilled with noises.

Bottom Line: Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously.First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality.In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets.

View Article: PubMed Central - PubMed

Affiliation: Department of Automation, Xiamen University, Xiamen, P.R. China.

ABSTRACT
Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1) Stanford Microarray Database (SMD), 2) Gene Expression Omnibus (GEO), 3) Baylor College of Medicine (BCM), 4) Swiss Institute of Bioinformatics (SIB), 5) Joe DeRisi's individual tiff files (DeRisi), and 6) University of California, San Francisco (UCSF), indicate that the improved approach is more robust and sensitive to weak spots. More importantly, it can obtain higher segmentation accuracy in the presence of noise, artifacts and weakly expressed spots compared with the other four methods.

No MeSH data available.


Related in: MedlinePlus