Segmentation of Complementary DNA Microarray Images using Marker-Controlled Watershed Technique

SaadEl-Gawady, Aliaa; Eltoukhy, Mohamed Meselhy; El-Tawel, Ghada S.; Wahed, M. El-Sayed

doi:10.5120/19370-1059

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Region-based segmentation, containing seeded region growing (SRG) algorithm [22] and watershed algorithm [23]. The spots is segmented by splitting image into areas depend on the image topology;…”

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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Section: Introductionmentioning

confidence: 99%

Automatic microarray image segmentation with clustering-based algorithms

Shao

Zhang

et al. 2019

PLoS ONE

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“…In the proposed literature of many microarray image segmentation approaches have Fixed circle segmentation [7], Adaptive circle Segmentation Technique [8], Seeded region growing methods [9] and clustering algorithms [10] are the methods that deal with microarray image segmentation problem. This paper mainly focuses on clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Microarray Images

Kondisetty

Hussain

2018

IJAAS

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We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy c-means and self organizing maps clustering methods.

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Segmentation of Complementary DNA Microarray Images using Marker-Controlled Watershed Technique

Cited by 2 publications

References 16 publications

Automatic microarray image segmentation with clustering-based algorithms

Automatic microarray image segmentation with clustering-based algorithms

SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Microarray Images

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