Multilevel Segmentation Optimized by Physical Information for Gridding of Microarray Images

Abstract

As one of the great advances in modern technology, the microarray is widely used in many fields, including biomedical research, clinical diagnosis, and so on. Evidently, in order to extract the intensity of fluorescence bio-probes accurately, we need to pay special attention to the gridding of microarray at first. To solve the poor effect of the traditional Otsu method for microarray gridding, an innovative algorithm of Otsu optimized by multilevel thresholds is proposed to improve the accuracy and effectiveness of the microarray image gridding and segmentation. The experimental results indicate that considering the physical information carried by microarrays, the improved algorithm of Otsu optimized by multilevel thresholds achieves high-quality gridding and establishes the bio-spot coordinates more precisely. Compared with the traditional Otsu method, its gridding error is reduced to zero, and the integrated relative error of bio-spot coordinates is decreased from 2.89% to 1.05%. This optimization of Otsu combined with physical information of spot-matrix will greatly improve the performance of segmentation so as to make the contribution to extracting the fluorescence intensity of microarray accurately. INDEX TERMS Microarray image, Otsu method, multilevel thresholds, gridding, physical information.

Identifification of Key Genes Involved in Colorectal Cancer: A Bioinformatics Analysis

“…[5]. The activation of PI3K-Akt signaling pathway is closely related to colorectal cancer metastasis and poor prognosis [6][7]. Research shows [8] that a deregulated PI3K-AKT signaling pathway in CRC patients, which might serve as therapeutic target(s).…”

“…Through integrated bioinformatics analysis, 10 hub genes were identi ed, including MCM3,MCM7, BUB1B,AUPKA,TPX2, TTK,FEN1, cyclin B1(CCNB1),cyclin A2(CCNA2) and KIF4A with a high degree of connectivity.Among them, MCM3 and MCM7 belong to the MCMs family, and their biological functions are primarily involved in the DNA replication [2].Multiple previous studies have demonstrated that minichromosome maintenance(MCM) protein additionally serve a role in the occurrence and development of varies malignancies [3][4][5].The MCM participates in DNA synthesis [6] and can be used as a biomarker of oral squamous cell carcinoma [7], melanoma [8], glioma [9] and colon cancer [10]. In this study, the KEGG pathway showed that the DEGs were mainly involved in the cell cycle and DNA replication.…”

“…It can simultaneously scan the hybridization signals of tens of thousands of gene probes in the chip and carry out quantitative analysis on the transcriptome pro le of samples. Recent advances especially in the algorithms of probe signal detection and analysis, such as the introduction of arti cial intelligence technologies, will make the results of microarray more accurate and reliable [6][7][8].The microarray technique also provides a powerful tool for exploring the gene regulation pattern and molecular mechanisms involved in oncogenesis and progression of CRC.Recently, different types of biomarkers including coding genes, miRNAs, long non-coding RNAs and circRNAs have been identi ed in colorectal cancer. Dysregulation of these molecules is involved in the tumor progression or is associated with the prognosis of patients [9][10][11][12].In view of the complexity of the molecular regulatory network of CRC, current studies on tumor biomarkers are not su cient.…”

Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation

“…Accidental microarray image rotation is detected and corrected using Radon transform, as reported in [6] . As described in [7], spot group detection is performed using an approach based on mathematical morphology, which identifies the groups of spots using an image closing procedure. The aforementioned preprocessing techniques transformed the original image I into several I p images, each containing one group of spots.…”

“…As reported in [4], [5], automatic grid alignment is performed using a SVM based approach, whereas based on a spot selection step, a set of grid lines are placed over the image in order to separate each pair of consecutive rows and columns. Unsupervised grid alignment methods for microarray images have been also proposed [6], [7] based on the use of optimal multilevel thresholding followed by a refinement procedure to find the positions of the sub-grids in the image and the positions of the spots in each detected sub-grid. For the segmentation of microarray spots, adaptive pixel clustering techniques were used in [8], [9], [10] .…”