K-Means with Bi-dimensional Empirical Mode Decomposition for segmentation of microarray image

Harikiran, J.; Phanendra, M.; Swamy, N. N.; Lakshmi, P.; Kumar, R. Kiran

doi:10.1145/2393216.2393288

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…These functions are called Intrinsic Mode Functions (IMF), and it satisfies two properties, (i) the number of zero crossings and extrema points should be equal or differ by one. (ii) Symmetric envelopes (zero mean) interpret by local maxima and minima [12]. The signal after decomposition using EMD is nondestructive means that the original signal can be obtained by adding the IMFs and residue.…”

Section: Bi-dimensional Empirical Mode Decomposition-dwt Thresholding Methodsmentioning

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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Section: Bi-dimensional Empirical Mode Decomposition-dwt Thresholding Methodsmentioning

confidence: 99%

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

Kondisetty

Hussain

2018

IJAAS

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“…In the mixture model analysis, the most important negative aspect is the detection of intensity near to its spot background. Approaches such as K-means , Fuzzy C means , Expectation-Maximization [19][20][21][22] etc., have been used by several scholars. K-means reflect on a few narrow features, like noise.…”

Section: Figure 1 Typical Microarray Spotsmentioning

confidence: 99%

Microarray spot partitioning by autonoumsly organising maps thorugh contour model

Karthik¹,

Manjunath²

2020

IJECE

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In cDNA microarray image analysis, classification of pixels as forefront area and the area covered by background is very challenging. In microarray experimentation, identifying forefront area of desired spots is nothing but computation of forefront pixels concentration, area covered by spot and shape of the spots. In this piece of writing, an innovative way for spot partitioning of microarray images using autonomously organizing maps (AOM) method through C-V model has been proposed. Concept of neural networks has been incorpated to train and to test microarray spots.In a trained AOM the comprehensive information arising from the prototypes of created neurons are clearly integrated to decide whether to get smaller or get bigger of contour. During the process of optimization, this is done in an iterative manner. Next using C-V model, inside curve area of trained spot is compared with test spot finally curve fitting is done.The presented model can handle spots with variations in terms of shape and quality of the spots and meanwhile it is robust to the noise. From the review of experimental work, presented approach is accurate over the approaches like C-means by fuzzy, Morphology sectionalization.

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“…Empirical mode decomposition [4] is a signal processing method that nondestructively fragments any non-linear and non-stationary signal into oscillatory functions by means of a mechanism called shifting process. These oscillatory functions are called Intrinsic Mode Functions (IMF), and each IMF satisfies two properties, (a) the number of zero crossings and extrema points should be equal or differ by one.…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

Kumar

Saichandana

Srinivas

2016

IJEECS

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K-Means with Bi-dimensional Empirical Mode Decomposition for segmentation of microarray image

Cited by 8 publications

References 7 publications

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

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

Microarray spot partitioning by autonoumsly organising maps thorugh contour model

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

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