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Spatial Kernel Fuzzy Clustering Methods for Microarray Image Spot Segmentation
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DOI：10.1109/ACCESS.2023.3343155.png)
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Abstract
En 中文
Computational Intelligence (CI) techniques are used in Microarray image processing which is useful for gene profiling in the medical field, drug discovery and industrial research. The paper discusses microarray image processing technique specifically spot segmentation using machine/deep learning techniques. This interdisciplinary approach helps in analyzing biological sequences and genome content to identify the function of macro molecules. The fast-evolving techniques of CI with Fuzzy system (FI) paradigms helps in distinguishing gene information, protein expression calculation, molecular information discovery and genetic study from segmented spots of microarray images. The clustering methods proposed in this paper provide support in analyzing characteristics of data point which belong to particular cluster. The limitation of evolutionary Fuzzy C-Means (FCM) method is discussed in the paper. A significant approach is discussed to overcome above said limitations with two variants of FCM being proposed - Robust Spatial Kernel FCM (RSKFCM) and Generalized Spatial Kernel FCM (GSKFCM). The iterative approach converges the spots of microarray image to the aligned membership values of neighborhood which leads to clustering of spots. The discussed method uses Gaussian kernel function which helps in reducing noise impact amicably.
Keywords:
Clustering
fuzzy clustering
microarray images
segmentation
Journal
IF:
3.6
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9.8W
Citations:
29.4W
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