A Spot Modeling Evolutionary Algorithm for Segmenting Microarray Images

Zacharia, Eleni; Maroulis, Dimitris

doi:10.5772/14745

Cited by 3 publications

(4 citation statements)

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“… Morphology-based techniques combine the mathematical morphology operations to realize the spot detection [ 15 , 26 , 30 – 31 ], yet the structure element is designed humanly and the segmentation effect is strongly dependent on the shape of structure element. Intelligent optimization methods (genetic algorithm or neural network) based algorithms make use of iterative operation to assign pixels into spot or non-spot classes [ 3 , 7 , 16 – 17 ]. However, it is prone to be affected by noises and requires parameter presetting or network training.…”

Section: Related Workmentioning

confidence: 99%

“…Intelligent optimization methods (genetic algorithm or neural network) based algorithms make use of iterative operation to assign pixels into spot or non-spot classes [ 3 , 7 , 16 – 17 ]. However, it is prone to be affected by noises and requires parameter presetting or network training.…”

Section: Related Workmentioning

confidence: 99%

“…Until now, except for those pioneering works with thresholding [ 13 ] and shape-based [ 14 ] methods, new algorithms are constantly emerging, such as mathematical morphological [ 15 ], genetic algorithm [ 16 ], artificial neural network [ 17 ], Markov random field [ 18 ], wavelet [ 19 ], clustering [ 20 ], and support vector machines [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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A Combinational Clustering Based Method for cDNA Microarray Image Segmentation

Shao

et al. 2015

PLoS ONE

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Combinational Clustering Based Method for cDNA Microarray Image Segmentation

Shao

et al. 2015

PLoS ONE

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“…Model-based segmentation, involving Markov Random Filed [19], 3D spot modeling [20] and total variation (TV)-based regularization method [21]. It segments spots by building spot model or modeling the image as a function according to a series of parameter estimation;…”

Section: Introductionmentioning

confidence: 99%

Automatic microarray image segmentation with clustering-based algorithms

Shao

Zhang

et al. 2019

PLoS ONE

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Image segmentation, as a key step of microarray image processing, is crucial for obtaining the spot expressions simultaneously. However, state-of-art clustering-based segmentation algorithms are sensitive to noises. To solve this problem and improve the segmentation accuracy, in this article, several improvements are introduced into the fast and simple clustering methods (K-means and Fuzzy C means). Firstly, a contrast enhancement algorithm is implemented in image preprocessing to improve the gridding precision. Secondly, the data-driven means are proposed for cluster center initialization, instead of usual random setting. The third improvement is that the multi features, including intensity features, spatial features, and shape features, are implemented in feature selection to replace the sole pixel intensity feature used in the traditional clustering-based methods to avoid taking noises as spot pixels. Moreover, the principal component analysis is adopted for various feature extraction. Finally, an adaptive adjustment algorithm is proposed based on data mining and learning for further dealing with the missing spots or low contrast spots. Experiments on real and simulation data sets indicate that the proposed improvements made our proposed method obtains higher segmented precision than the traditional K-means and Fuzzy C means clustering methods.

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