Dataset Information

Automatic microarray image segmentation with clustering-based algorithms.

ABSTRACT: 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.

SUBMITTER: Shao G

PROVIDER: S-EPMC6342330 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Automatic microarray image segmentation with clustering-based algorithms.

Shao Guifang G Li Dongyao D Zhang Junfa J Yang Jianbo J Shangguan Yali Y

PloS one 20190122 1

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 gr ...[more]

PMID: 30668601

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Project description:On?board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real?time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image?guided radiotherapy (MR?IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k?means (FKM), k?harmonic means (KHM), and reaction?diffusion level set evolution (RD?LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR?TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR?TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD?LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP?TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high?contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR?TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on?board MR?IGRT system. PACS number(s): 87.57.nm, 87.57.N?, 87.61.Tg

Dataset Information

Automatic microarray image segmentation with clustering-based algorithms.

Publications

Automatic microarray image segmentation with clustering-based algorithms.

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