A fully automatic gridding method for cDNA microarray images

<p>Abstract</p> <p>Background</p> <p>Processing cDNA microarray images is a crucial step in gene expression analysis, since any errors in early stages affect subsequent steps, leading to possibly erroneous biological conclusions. When processing the underlying images, accurately separating the sub-grids and spots is extremely important for subsequent steps that include segmentation, quantification, normalization and clustering.</p> <p>Results</p> <p>We propose a parameterless and fully automatic approach that first detects the sub-grids given the entire microarray image, and then detects the locations of the spots in each sub-grid. The approach, first, detects and corrects rotations in the images by applying an affine transformation, followed by a polynomial-time optimal multi-level thresholding algorithm used to find the positions of the sub-grids in the image and the positions of the spots in each sub-grid. Additionally, a new validity index is proposed in order to find the correct number of sub-grids in the image, and the correct number of spots in each sub-grid. Moreover, a refinement procedure is used to correct possible misalignments and increase the accuracy of the method.</p> <p>Conclusions</p> <p>Extensive experiments on real-life microarray images and a comparison to other methods show that the proposed method performs these tasks fully automatically and with a very high degree of accuracy. Moreover, unlike previous methods, the proposed approach can be used in various type of microarray images with different resolutions and spot sizes and does not need any parameter to be adjusted.</p

M3G: Maximum Margin Microarray Gridding

<p>Abstract</p> <p>Background</p> <p>Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.</p> <p>Methods</p> <p>In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.</p> <p>Results</p> <p>The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.</p> <p>Conclusions</p> <p>The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p

Unsupervised SVM-based gridding for DNA microarray images

This paper presents a novel method for unsupervised DNA microarray gridding based on support vector machines (SVMs). Each spot is a small region on the microarray surface where chains of known DNA sequences are attached. The goal of microarray gridding is the separation of the spots into distinct cells. The positions of the spots on a DNA microarray image are first detected using image analysis operations and then a set of soft-margin linear SVM classifiers is used to estimate the optimal layout of the grid lines in the image. Each grid line is the separating line produced by one of the SVM classifiers, which maximizes the margin between two consecutive rows or columns of spots. The classifiers are trained using the spot locations as training vectors. The proposed method was evaluated on reference microarray images containing more than two million spots in total. The results illustrate its robustness in the presence of artifacts, noise and weakly expressed spots, as well as image rotation. The comparison to state of the art methods for microarray gridding reveals the superior performance of the proposed method. In 96.4% of the cases, the spots reside completely inside their respective grid cells. © 2009 Elsevier Ltd

FPGA-based system for real-time video texture analysis

This paper describes a novel system for real-time video texture analysis. The system utilizes hardware to extract second-order statistical features from video frames. These features are based on the Gray Level Co-occurrence Matrix (GLCM) and describe the textural content of the video frames. They can be used in a variety of video analysis and pattern recognition applications, such as remote sensing, industrial and medical. The hardware is implemented on a Virtex-XCV2000E-6 FPGA programmed in VHDL. It is based on an architecture that exploits the symmetry and the sparseness of the GLCM and calculates the features using integer and fixed point arithmetic. Moreover, it integrates an efficient algorithm for fast and accurate logarithm approximation, required in feature calculations. The software handles the video frame transfers from/to the hardware and executes only complementary floating point operations. The performance of the proposed system was experimentally evaluated using standard test video clips. The system was implemented and tested and its performance reached 133 and 532 fps for the analysis of CIF and QCIF video frames respectively. Compared to the state of the art GLCM feature extraction systems, the proposed system provides more efficient use of the memory bandwidth and the FPGA resources, in addition to higher processing throughput, that results in real time operation. Furthermore, its fundamental units can be used in any hardware application that requires sparse matrix representation or accurate and efficient logarithm estimation. © 2008 Springer Science+Business Media, LLC

M3G: Maximum margin microarray gridding

Background: Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.Methods: In this paper we propose M3G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.Results: The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M3G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.Conclusions: The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots. © 2010 Bariamis et al; licensee BioMed Central Ltd

Dedicated hardware for real-time computation of second-order statistical features for high resolution images

We present a novel dedicated hardware system for the extraction of second-order statistical features from high-resolution images. The selected features are based on gray level co-occurrence matrix analysis and are angular second moment, correlation, inverse difference moment and entropy. The proposed system was evaluated using input images with resolutions that range from 512×512 to 2048×2048 pixels. Each image is divided into blocks of user-defined size and a feature vector is extracted for each block. The system is implemented on a Xilinx VirtexE-2000 FPGA and uses integer arithmetic, a sparse co-occurrence matrix representation and a fast logarithm approximation to improve efficiency. It allows the parallel calculation of sixteen co-occurrence matrices and four feature vectors on the same FPGA core. The experimental results illustrate the feasibility of real-time feature extraction for input images of dimensions up to 2048×2048 pixels, where a performance of 32 images per second is achieved. © Springer-Verlag Berlin Heidelberg 2006

Adaptable, fast, area-efficient architecture for logarithm approximation with arbitrary accuracy on FPGA

This paper presents ALA (Adaptable Logarithm Approximation), a novel hardware architecture for the approximation of the base-2 logarithm of integers at an arbitrary accuracy, suitable for fast and area-efficient FPGA implementation. It is based on a piecewise linear approximation methodology, implemented so that an arbitrary number of linear segments approximate the logarithm function. The achieved approximation accuracy depends on the number of segments used, which also affects the size of a ROM used for storing the parameters that control the computation. The implementation of the ROM using an FPGA BlockRAM allows the parameters to be updated without reconfiguration of the FPGA core. This provides the considerable advantage of data set adaptability to the proposed architecture over the other relevant architectures, as the parameters can be easily updated to minimize the approximation error for different data sets. Both real and synthetic datasets have been used for evaluation purposes. The results show that ALA adapts well to all data sets used and requires significantly less FPGA slices than the CORDIC architecture to achieve the same or higher approximation accuracy. Moreover, it provides a throughput of one result per cycle and up to four times lower latency than the CORDIC core. © 2009 Springer Science+Business Media, LLC

An FPGA-based Architecture for Real Time Image Feature Extraction

We propose a novel FPGA-based architecture for the extraction of four texture features using Gray Level Cooccurrence Matrix (GLCM) analysis. These features are angular second moment, correlation, inverse difference moment, and entropy. The proposed architecture consists of a hardware and a software module. The hardware module is implemented on Xilinx Virtex-E V2000 FPGA using VHDL. It calculates many GLCMs and GLCM integer features in parallel. The software retrieves the feature vectors calculated in hardware and performs complementary computations. The architecture was evaluated using standard grayscale images and video clips. The results show that it can be efficiently used in realtime pattern recognition applications.