Spot Edge Detection in Microarray Images Using Bi-Dimensional Empirical Mode Decomposition

Harikiran, J.; NarasimhaRao, Y.; Saichandana, B.; Lakshmi, P.; Kumar, R. Kiran

doi:10.1016/j.protcy.2012.05.034

Cited by 6 publications

(2 citation statements)

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“…The gene expression value depends on the intensity values of foreground pixels (spot areas) and background pixels. Most of the existing algorithms for microarray image analysis are semi-automatic, which means that manual intervention is required to initialize the parameters to execute the gridding algorithm [3]. This article offers a fully automatic mesh generation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Gridding and Segmentation Method for DNA Microarray Images

Sivalakshmi¹

2021

TURCOMAT

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This article mainly explores meshing and segmentation techniques for microarray image analysis. The term "grid" refers to dividing an image into subgrids of dots and then dividing them into point detection. Most of the existing methods depend on input parameters such as the number of rows / columns, the number of points in each row / column, the size of the subarrays, etc. This article proposes a fully automatic mesh generation algorithm. This can remove any initialized parameter without any manual intervention. In the segmentation step, clustering algorithms are used because they do not consider the size and shape of the spots, do not depend on the initial state of the pixels, and do not require post-processing. In this article, a new method is proposed to estimate the initial parameters (centroid and number of clusters) required by any clustering algorithm. Qualitative and quantitative analysis shows that the algorithm can perform grid processing on microarray images well, and improves the performance of the clustering algorithm.

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Section: Introductionmentioning

confidence: 99%

Gridding and Segmentation Method for DNA Microarray Images

Sivalakshmi¹

2021

TURCOMAT

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“…In this paper, a new filter is designed based on Bi-dimensional Empirical Mode Decomposition [BEMD] and Mean filter. The BEMD method [3] decomposes the image band into several Intrinsic Mode Functions [IMF], in which the first function is the high frequency component, second function next high frequency component and so on, the last function denotes the low frequency component [5]. The mean filter is applied only to the few first high frequency components leaving the low frequency components, as the high frequency components contain noise.…”

Section: Introductionmentioning

confidence: 99%

Ga Based Dimensionality Reduction in Hyperspectral Image Segmentation Framework

Srinivas¹,

Prasad²

2019

IJCET

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Hyperspectral imaging system contains stack of images collected from the sensor with different wavelengths representing the same scene on the earth. This paper presents GA based dimensionality reduction method in framework for hyperspectral image segmentation. The framework consists of four stages in segmenting a hyperspectral data set. In the first stage, filtering is done to remove noise in image bands. Second stage consists of dimensionality reduction algorithms, in which the bands that convey less information or redundant data will be removed. This deletion will decrease the storage requirement, computational load etc in processing the hyperspectral data. In the third stage, the informative bands which are selected in the second stage are merged into a single image using averaging method of fusion technique. The main goal of image fusion is to merge all the features from the selected image bands to form a single image. This single image is segmented using Fuzzy cmeans clustering algorithm. The experimental results show that this framework will segment the data set more accurately by combining all the features in the image bands after dimensionality reduction using proposed technique.

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