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An Efficient Fully Automated Method for Gridding Microarray Images
American Journal of Biomedical Engineering , 2012, DOI: 10.5923/j.ajbe.20120203.04
Abstract: DNA microarray is a powerful tool and is widely used in genetics to monitor expression levels of thousands of genes in parallel. The gene expression process consists of three stages: gridding, segmentation and quantification. Gridding deals with finding areas in the microarray image which contain one spot using grid lines. This step can be done manually or automatically. In this paper, we propose an efficient and simple automatic gridding method for microarray image analysis. This method was implemented using MATLAB software and found very effective for gridding arrays with low intensity, poor quality spotsand tested by a number of microarray images. Results show that this method gives high accuracy of 76.9% improved to 98.6% when a preprocessing step is considered, rendering the method a promising technique for an efficient and automatic gridding the noisy microarray images.
Robust Automated Tumour Segmentation on Histological and Immunohistochemical Tissue Images  [PDF]
Ching-Wei Wang
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0015818
Abstract: Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.
AMDA: an R package for the automated microarray data analysis
Mattia Pelizzola, Norman Pavelka, Maria Foti, Paola Ricciardi-Castagnoli
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-335
Abstract: To address these problems we have developed an automated microarray data analysis (AMDA) software, which provides scientists with an easy and integrated system for the analysis of Affymetrix microarray experiments. AMDA is free and it is available as an R package. It is based on the Bioconductor project that provides a number of powerful bioinformatics and microarray analysis tools. This automated pipeline integrates different functions available in the R and Bioconductor projects with newly developed functions. AMDA covers all of the steps, performing a full data analysis, including image analysis, quality controls, normalization, selection of differentially expressed genes, clustering, correspondence analysis and functional evaluation. Finally a LaTEX document is dynamically generated depending on the performed analysis steps. The generated report contains comments and analysis results as well as the references to several files for a deeper investigation.AMDA is freely available as an R package under the GPL license. The package as well as an example analysis report can be downloaded in the Services/Bioinformatics section of the Genopolis http://www.genopolis.it/ webciteMicroarrays have become common tools in many life-science laboratories. Despite their diffusion, it is still not easy to analyze the huge amount of data generated by this powerful technology. Microarray data analysis is in fact a multi-step procedure, and an overwhelming amount of different published methods exist for each step. While the research community has yet to agree on a golden standard, some methods have already been shown to be more appropriate in some situations [1]. On one hand, biologists that need to analyze their own microarray dataset may lack the necessary computational and statistical knowledge to address all aspects of a typical analysis work-flow. On the other hand, service providers that provide data analysis support to their user, have to face the challenge of transferring all
Microarray BASICA: Background Adjustment, Segmentation, Image Compression and Analysis of Microarray Images  [cached]
Kenneth R. Castleman,Qiang Wu,Zixiang Xiong,Zhongmin Liu
EURASIP Journal on Advances in Signal Processing , 2004, DOI: 10.1155/s1687617204309200
Abstract: This paper presents microarray BASICA: an integrated image processing tool for background adjustment, segmentation, image compression, and analysis of cDNA microarray images. BASICA uses a fast Mann-Whitney test-based algorithm to segment cDNA microarray images, and performs postprocessing to eliminate the segmentation irregularities. The segmentation results, along with the foreground and background intensities obtained with the background adjustment, are then used for independent compression of the foreground and background. We introduce a new distortion measurement for cDNA microarray image compression and devise a coding scheme by modifying the embedded block coding with optimized truncation (EBCOT) algorithm (Taubman, 2000) to achieve optimal rate-distortion performance in lossy coding while still maintaining outstanding lossless compression performance. Experimental results show that the bit rate required to ensure sufficiently accurate gene expression measurement varies and depends on the quality of cDNA microarray images. For homogeneously hybridized cDNA microarray images, BASICA is able to provide from a bit rate as low as 5 bpp the gene expression data that are 99% in agreement with those of the original 32 bpp images.
Automated detection of regions of interest for tissue microarray experiments: an image texture analysis
Bilge Kara?ali, Aydin T?zeren
BMC Medical Imaging , 2007, DOI: 10.1186/1471-2342-7-2
Abstract: This study presents a high throughput analysis of texture heterogeneity on breast tissue images for the purpose of identifying regions of interest in the tissue for molecular profiling via tissue microarray technology. Image texture of breast histology slides was described in terms of three parameters: the percentage of area occupied in an image block by chromatin (B), percentage occupied by stroma-like regions (P), and a statistical heterogeneity index H commonly used in image analysis. Texture parameters were defined and computed for each of the thousands of image blocks in our dataset using both the gray scale and color segmentation. The image blocks were then classified into three categories using the texture feature parameters in a novel statistical learning algorithm. These categories are as follows: image blocks specific to normal breast tissue, blocks specific to cancerous tissue, and those image blocks that are non-specific to normal and disease states.Gray scale and color segmentation techniques led to identification of same regions in histology slides as cancer-specific. Moreover the image blocks identified as cancer-specific belonged to those cell crowded regions in whole section image slides that were marked by two pathologists as regions of interest for further histological studies.These results indicate the high efficiency of our automated method for identifying pathologic regions of interest on histology slides. Automation of critical region identification will help minimize the inter-rater variability among different raters (pathologists) as hundreds of tumors that are used to develop an array have typically been evaluated (graded) by different pathologists. The region of interest information gathered from the whole section images will guide the excision of tissue for constructing tissue microarrays and for high throughput profiling of global gene expression.The standard procedure in clinical assessment of invasive breast cancer is the classificatio
Morphological Spot Detection and Analysis for Microarray Images  [PDF]
Dr. Manjunath S S,,Shreenidhi B S,,Asst. Prof. Nagaraja J,,Dr Pradeep B S
International Journal of Innovative Technology and Exploring Engineering , 2013,
Abstract: DNA microarray technology has promised a very accelerating research inclination in recent years. There are numerous applications of this technology, including clinical diagnosis and treatment, drug design and discovery, tumor detection, and in the environmental health research. Enhancement is the major pre-processing step in microarray image analysis. Microarray images when corrupted with noise may drastically affect the subsequent stages of image analysis and finally affects gene expression profile. Spot detection is the major preprocessing stage in microarray image segmentation. In this paper, morphological approach to detect spots in a subgrid. The proposed approach consists of two phases. First phase is morphological preprocessing, second phase includes spot detection model uses bottomhat transform. Experiments on Stanford, TBDB and UNC database illustrate robustness of the proposed approach in the presence of noise, artifacts and weakly expressed spots. Experimental results and analysis illustrates the performance of the proposed method with the contemporary methods discussed in the literature.
Automated Localization of Optic Disc in Retinal Images  [PDF]
Deepali A.Godse,Dr.Dattatraya S.Bormane
International Journal of Advanced Computer Sciences and Applications , 2013,
Abstract: An efficient detection of optic disc (OD) in colour retinal images is a significant task in an automated retinal image analysis system. Most of the algorithms developed for OD detection are especially applicable to normal and healthy retinal images. It is a challenging task to detect OD in all types of retinal images, that is, normal, healthy images as well as abnormal, that is, images affected due to disease. This paper presents an automated system to locate an OD and its centre in all types of retinal images. The ensemble of steps based on different criteria produces more accurate results. The proposed algorithm gives excellent results and avoids false OD detection. The technique is developed and tested on standard databases provided for researchers on internet, Diaretdb0 (130 images), Diaretdb1 (89 images), Drive (40 images) and local database (194 images). The local database images are collected from ophthalmic clinics. It is able to locate OD and its centre in 98.45% of all tested cases. The results achieved by different algorithms can be compared when algorithms are applied on same standard databases. This comparison is also discussed in this paper which shows that the proposed algorithm is more efficient.
Segmentation and intensity estimation for microarray images with saturated pixels
Yan Yang, Phillip Stafford, YoonJoo Kim
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-462
Abstract: We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.Microarray technology has been used in many areas of biomedical research and drug development to study the function of thousands of genes in a single experiment. As an important early step in microarray studies, microarray image analysis produces the input spot intensity data to downstream analysis such as classification and identification of differentially regulated genes. Thus, image processing can have profound effects on those and subsequent analysis. Microarray images with saturated hybridization signals are common when the dynamic range of expression of biological quantities is large. To enhance weak signals, a common practice is to increase the photometric gain at scannin
Automated Alignment of Imperfect EM Images for Neural Reconstruction  [PDF]
Louis K. Scheffer,Bill Karsh,Shiv Vitaladevun
Quantitative Biology , 2013,
Abstract: The most established method of reconstructing neural circuits from animals involves slicing tissue very thin, then taking mosaics of electron microscope (EM) images. To trace neurons across different images and through different sections, these images must be accurately aligned, both with the others in the same section and to the sections above and below. Unfortunately, sectioning and imaging are not ideal processes - some of the problems that make alignment difficult include lens distortion, tissue shrinkage during imaging, tears and folds in the sectioned tissue, and dust and other artifacts. In addition the data sets are large (hundreds of thousands of images) and each image must be aligned with many neighbors, so the process must be automated and reliable. This paper discusses methods of dealing with these problems, with numeric results describing the accuracy of the resulting alignments.
Automated Generation of Geometric Theorems from Images of Diagrams  [PDF]
Xiaoyu Chen,Dan Song,Dongming Wang
Computer Science , 2014,
Abstract: We propose an approach to generate geometric theorems from electronic images of diagrams automatically. The approach makes use of techniques of Hough transform to recognize geometric objects and their labels and of numeric verification to mine basic geometric relations. Candidate propositions are generated from the retrieved information by using six strategies and geometric theorems are obtained from the candidates via algebraic computation. Experiments with a preliminary implementation illustrate the effectiveness and efficiency of the proposed approach for generating nontrivial theorems from images of diagrams. This work demonstrates the feasibility of automated discovery of profound geometric knowledge from simple image data and has potential applications in geometric knowledge management and education.
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