Gene expression extraction in cervical cancer by segmentation of microarray images using a novel fuzzy method

IF 1.3 Q4 ENGINEERING, BIOMEDICAL

Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization Pub Date : 2023-09-30 DOI:10.1080/21681163.2023.2261555

Nayyer Mostaghim Bakhshayesh, Mousa Shamsi, Faegheh Golabi

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Abstract

It is necessary to obtain gene expression values to identify gene biomarkers involved in all types of cancers, and microarray data is one of the best data for this purpose. In order to extract gene expression values from microarray images that have different challenges. This article presents a completely automatic and comprehensive method that can deal with the various challenges in these images and obtain gene expression values with high accuracy. A pre-processing approach is proposed for contrast enhancement using a genetic algorithm and for removing noise and artefacts in microarray cells using wavelet transform based on a complex Gaussian scaling model. For each point, the coordinate centre is determined using Self Organising Maps. Then, using a new hybrid model based on the Fuzzy Local Information Gaussian Mixture Model (FLIGMM), the position of each spot is accurately determined. In this model, various features are obtained using local information about pixels, considering the pixel neighbourhood correlation coefficient. Finally, the gene expression values are obtained. The performance of the proposed algorithm was evaluated using real microarray images of cervical cancer from the GMRCL microarray dataset as well as simulated images. The results show that the proposed algorithm achieves 90.91% and 98% accuracy in segmenting microarray spots for noiseless and noisy spots, respectively.

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基于微阵列图像分割的子宫颈癌基因表达提取

获得基因表达值是识别所有类型癌症中涉及的基因生物标志物的必要条件，而微阵列数据是实现这一目的的最佳数据之一。为了从不同挑战的微阵列图像中提取基因表达值。本文提出了一种完全自动化和综合的方法，可以处理这些图像中的各种挑战，并获得高精度的基因表达值。提出了一种基于遗传算法的对比度增强预处理方法和基于复杂高斯尺度模型的小波变换去除微阵列细胞中的噪声和伪影的预处理方法。对于每个点，坐标中心是使用自组织地图确定的。然后，利用基于模糊局部信息高斯混合模型(FLIGMM)的混合模型，精确确定每个点的位置;在该模型中，考虑像素邻域相关系数，利用像素的局部信息获得各种特征。最后得到基因表达值。使用来自GMRCL微阵列数据集的真实宫颈癌微阵列图像以及模拟图像来评估所提出算法的性能。结果表明，该算法对无噪声点和有噪声点的分割准确率分别达到90.91%和98%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization ENGINEERING, BIOMEDICAL-

CiteScore

2.80

自引率

6.20%

发文量

102

期刊介绍： Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is an international journal whose main goals are to promote solutions of excellence for both imaging and visualization of biomedical data, and establish links among researchers, clinicians, the medical technology sector and end-users. The journal provides a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to imaging and visualization, including, but not limited to: Applications of Imaging and Visualization Computational Bio- imaging and Visualization Computer Aided Diagnosis, Surgery, Therapy and Treatment Data Processing and Analysis Devices for Imaging and Visualization Grid and High Performance Computing for Imaging and Visualization Human Perception in Imaging and Visualization Image Processing and Analysis Image-based Geometric Modelling Imaging and Visualization in Biomechanics Imaging and Visualization in Biomedical Engineering Medical Clinics Medical Imaging and Visualization Multi-modal Imaging and Visualization Multiscale Imaging and Visualization Scientific Visualization Software Development for Imaging and Visualization Telemedicine Systems and Applications Virtual Reality Visual Data Mining and Knowledge Discovery.

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