Showing papers on "Structuring element published in 2018"

Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method.

Abstract: Periodic transient impulses are key indicators of rolling element bearing defects. Efficient acquisition of impact impulses concerned with the defects is of much concern to the precise detection of bearing defects. However, transient features of rolling element bearing are generally immersed in stochastic noise and harmonic interference. Therefore, in this paper, a new optimal scale morphology analysis method, named adaptive multiscale combination morphological filter-hat transform (AMCMFH), is proposed for rolling element bearing fault diagnosis, which can both reduce stochastic noise and reserve signal details. In this method, firstly, an adaptive selection strategy based on the feature energy factor (FEF) is introduced to determine the optimal structuring element (SE) scale of multiscale combination morphological filter-hat transform (MCMFH). Subsequently, MCMFH containing the optimal SE scale is applied to obtain the impulse components from the bearing vibration signal. Finally, fault types of bearing are confirmed by extracting the defective frequency from envelope spectrum of the impulse components. The validity of the proposed method is verified through the simulated analysis and bearing vibration data derived from the laboratory bench. Results indicate that the proposed method has a good capability to recognize localized faults appeared on rolling element bearing from vibration signal. The study supplies a novel technique for the detection of faulty bearing.

Iterative Deblending of Simultaneous-Source Seismic Data With Structuring Median Constraint

Abstract: Simultaneous-source shooting can help reduce the acquisition time cost, but at the expense of introducing strong interference (blending noise) into the acquired seismic data. It has been demonstrated previously that the deblending problem can be considered as an inversion process. In this letter, we propose a new iterative approach to solve this inversion problem. In the proposed approach, a new coherency-promoting constraint, called structuring median filtering (SMF), is proposed and used to regularize the estimated model in each iteration. The SMF processes the signal by the interactions of the input signal and another given small section of signal, namely, the structuring element. The SMF is more robust than other coherency-promoting filtering such as the median filtering and mathematical morphological filtering. Numerical experiments demonstrate that the iterative deblending based on the SMF constraint obtains a better performance and a faster convergence than the low-rank and compressed sensing constraint-based deblending approaches.

High-impedance fault detection and classification in power system distribution networks using morphological fault detector algorithm

Abstract: This study presents a fast, secure and reliable algorithm for the detection and classification of power system disturbances including high-impedance faults (HIFs). The proposed algorithm utilises mathematical morphology (MM) techniques, where the non-linear MM characteristics are exploited by strategic cascading of appropriate filtering functions to form a multistage morphological fault detector (MFD) for the extraction of features necessary for the characterisation of HIFs. The target features of the HIF are the randomness and arc extinction and re-ignition/unsymmetrical characteristics. The reliability and robustness in the extraction of the desired HIF features are enhanced by a weighted convex structuring element designed based on the attributes of power system signals. The performance of the proposed algorithm is tested under different types of disturbances including cases of HIFs on different contact surfaces. Moreover, the effectiveness of the algorithm is tested under noise condition to demonstrate its performance of the proposed MFD. All tests are simulated using IEEE13 bus test system.

Focal Dice Loss and Image Dilation for Brain Tumor Segmentation

Abstract: For accurate tumor segmentation in brain magnetic resonance (MR) images, the extreme class imbalance not only exists between the foreground and background, but among different sub-regions of tumor Inspired by the focal loss [3] that down-weights the well-segmented classes, our proposed Focal Dice Loss (FDL) considers the imbalance among structures of interest instead of the entire image including background Image dilation is applied to the training samples, which enlarges the tiny sub-regions, bridges the disconnected pieces of tumor structures and promotes understanding on overall tumor rather than complex details The structuring element for dilation is gradually downsized, resulting in a coarse-to-fine and incremental learning process with the structure of network unchanged Our experiments on the BRATS2015 dataset achieves the state-of-the-art in Dice Coefficient on average with relatively low computational cost

Focal Dice Loss and Image Dilation for Brain Tumor Segmentation.

Abstract: For accurate tumor segmentation in brain magnetic resonance (MR) images, the extreme class imbalance not only exists between the foreground and background, but among different sub-regions of tumor. Inspired by the focal loss [3] that down-weights the well-segmented classes, our proposed Focal Dice Loss (FDL) considers the imbalance among structures of interest instead of the entire image including background. Image dilation is applied to the training samples, which enlarges the tiny sub-regions, bridges the disconnected pieces of tumor structures and promotes understanding on overall tumor rather than complex details. The structuring element for dilation is gradually downsized, resulting in a coarse-to-fine and incremental learning process with the structure of network unchanged. Our experiments on the BRATS2015 dataset achieves the state-of-the-art in Dice Coefficient on average with relatively low computational cost.

An Efficient Technique for Detection and Removal of Lines with Text Stroke Crossings in Document Images

Abstract: Precise automatic reading of the characters in a document image is the functionality of Optical Character Recognition (OCR) systems. The overall recognition accuracy can be accomplished only through efficient pre-processing procedures. The recognition of characters in pre-printed document images is a highly challenging task as it desires unique pre-processing methods and it depends on the layout of document. In this paper we propose a pre-processing technique for removal of horizontal/vertical lines in the pre-printed documents. The major challenge involved in removal of the horizontal lines is retention of the pixels overlapped between line and characters in document. The proposed algorithm works in two phases; image enhancement and line detection is made in the first phase and the second phase comprises convolution process using rectangular structuring element for detection of text stroke crossings on lines which are detected in phase one. The output image is further subjected to undergo post enhancement and analysis operations using connected component analysis and area features for removal of broken/dotted line structures. The experimental outcomes achieved are quite satisfactory and consistent enough for subsequent processing of document.

Word bounding box detection

Abstract: A system for determining bounding boxes includes the input interface and a processor. The input interface is configured to receive an image. The processor is configured to detect a line associated with connected components in the image; determine gap sizes within the line; determine a word structuring element size using the gap sizes; and determine bounding boxes for the line based at least in part on the word structuring element size.

Adaptive Unsymmetrical Trim-Based Morphological Filter for High-Density Impulse Noise Removal

Abstract: The modified decision-based unsymmetrical trimmed median filter (MDBUTMF), which is an efficient tool for restoring images corrupted with high-density impulse noise, is only effective for certain types of images. This is because the size of the selected window is fixed and some of the center pixels are replaced by a mean value of pixels in the window. To address these issues, this paper proposes an adaptive unsymmetrical trim-based morphological filter. Firstly, a strict extremum estimation approach is used, in order to decide whether the pixel to be processed belongs to a monochrome or non-monochrome area. Then, the center pixel is replaced by a median value of pixels in a window for the monochrome area. Secondly, a relaxed extremum estimation approach is used to control the size of structuring elements. Then an adaptive structuring element is obtained and the center pixel is replaced by the output of constrained morphological operators, i.e., the minimum or maximum of pixels in a trimmed structuring element. Our experimental results show that the proposed filter is more robust and practical than the MDBUTMF. Moreover, the proposed filter provides a preferable performance compared to the existing median filters and vector median filters for high-density impulse noise removal.

Single-Camera Computer Vision Algorithm for Robot Shortest Path Estimator using morphological structuring element with variable sizes

Abstract: The robotic challeng of developing and implementing a shortest-path finding algorithm to reach a destination without bumping into obstacles has been tackled in this paper. The proposed algorithm is utilized a single ceiling fixed camera and based on the mask and robot size with different sizes for dilation mask. The implementation has been successfully produced thirty-three computer vision figures and two comparison tables of simulation results by using morphological structuring element with different sizes. This paper’s proposal of optimal path finding algorithm is effeciently save robot energy in reaching targets.

Sum of top-hat transform based algorithm for vessel enhancement in MRA images

Abstract: The Magnetic Resonance Angiography (MRA) is rich with information’s. But, they suffer from poor contrast, illumination and noise. Thus, it is required to enhance the images. But, these significant information can be lost if improper techniques are applied. Therefore, in this paper, we propose a new method of enhancement. We applied firstly the CLAHE method to increase the contrast of the image. Then, we applied the sum of Top-Hat Transform to increase the brightness of vessels. It is performed with the structuring element oriented in different angles. The methodology is tested and evaluated on the publicly available database BRAINIX. And, we used the measurement methods MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and SNR (Signal to Noise Ratio) for the evaluation. The results demonstrate that the proposed method could efficiently enhance the image details and is comparable with state of the art algorithms. Hence, the proposed method could be broadly used in various applications.

Structuring Elements in the Watershed Algorithm for the Segmentation of Mammography Images

Abstract: Reading mammography images has always been a challenging task even for experienced radiologists. With the advancements in computer technology, machine tools such as the Computer Aided Detection and Diagnosis (CAD) systems are widely engaged as a second reader to assist radiologists in image reading. One of the important processes in the CAD machine is the segmentation process. The morphological watershed algorithm is one of the hybrid technique that combines boundary and region criteria, but this algorithm has several drawbacks such as over-segmentation and sensitive to noise. In this research, the denoising method applies the Principal Component Analysis (PCA) filtering. Prior to the segmentation by the watershed algorithm, the Fuzzy C-Means (FCM) clustering algorithm is used to identify the image foreground, which is the region of interest (abnormality region). A marker-controlled watershed algorithm is implemented to overcome the over-segmentation drawback. Furthermore, applying a suitable shape of structuring element in the watershed algorithm has the same effect of reducing the over-segmentation problem. Thus, three shapes of structuring elements, which are the disk, diamond, and octagon are tested and compared. The aim of this research is to find a suitable shape of structuring element for the marker-controlled watershed algorithm. For the evaluation of the segmentation performance, three evaluation methods are used, which are the Jaccard Index (JI), Dice Similarity Coefficient (DSC) and Figure of Merit (FOM). The result of the comparison shows that the diamond-shaped structuring element is a suitable shape for the segmentation of mammography images.

Image Regularization Using Total Variation and Morphological Gradient Priors with Optimization of Structuring Element

Abstract: As an image prior for image restoration, the sum of morphological gradients for an image has previously been proposed. Optimization of the structuring element (SE) used for this morphological gradient using a genetic algorithm (GA) has also been proposed. This method uses the minimized value of the objective function of the restoration problem as the fitness of the GA. However, this value does not necessarily coincide with an objective evaluation such as the mean square error. Therefore, in this paper, we formulate the objective function using the morphological gradient and total variation as a new image prior for an image restoration problem. The proposed objective function makes it possible to almost match the fitness to the objective evaluation and can improve the restoration accuracy. It also solves the problem of the artifact due to the unsuitability of the SE for the image. An experiment shows the effectiveness of the proposed image restoration method.

A Comparative Study and Analysis of Contrast Enhancement Algorithms for MRI Brain Image Sequences

Abstract: Brain tumor extraction is a challenging task in medical imaging research because its structure is complicated and can be diagnosed appropriately only by expert radiologists. Magnetic Resonance Imaging (MRI) is a commonly used modality to effectively diagnose, treat and monitor brain disease. Contrast enhancement is an important pre-processing step in which perceptual information is improved to obtain detailed information in the image. The motivation of this paper is to perform a comparative study and analysis of five different contrast enhancement algorithms such as Histogram Equalization which is a global contrast enhancement method, Adaptive histogram equalization perform local contrast enhancement by transforming each pixel based on the histogram of surrounding pixels. Morphological enhancement, Morphological filtering performed at single scale and at multiple scales of structuring element and to identify the suitability of a particular algorithm for each type of MR sequences for trans-axial orientation. Analysis was performed on the international database collected from Whole brain Atlas. The performance was evaluated using the standard measures Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Tenengrad Measure(TGD)

Method and apparatus for removing noise based on mathematical morphology from geometric data of 3d space

Abstract: According to an embodiment of the present invention, provided is a method for removing space geometric information based on a mathematical morphology which comprises the steps of: setting a voxel value corresponding to each of a plurality of voxels dividing a target area into predetermined units based on space geometric information on the target area; selecting a plurality of first noise voxels among the plurality of voxels from a result performing a plurality of mathematical morphologies using at least one structuring element and the voxel value for each of the plurality of voxels; and removing a noise from the space geometric information based on the plurality of first noise voxels.

Detail preservation of morphological operations through image scaling

Abstract: Morphological techniques probe an image with a structuring element. By varying the size and the shape of structuring elements, geometrical information of different parts of an image and their interrelation can be extracted for the applications of demodulating boundary, identifying components or removing noise. While large size elements benefits eliminating noise, they may be disadvantageous for preserving details in an image. Taking this into consideration, in this paper, we propose an image scaling method that will preserve detailed information when applying morphological operations to remove noise. First, a binary image is obtained, from which a Preservation Ratio Scalar (PRS) is calculated. The PRS is used for upscaling the image before morphological operations, which aims at preserving structural fine details otherwise eliminated in the original image. Finally, the morphological operator processed image is downscaled using the PRS. Experiments of target detection demonstrated the effectiveness of the proposed method in preserving the structural details such as edges while eliminating noises.

Anatomical Segmentation of Human Brain MRI Using Morphological Masks

Abstract: Segmenting the anatomical parts of the human brain from MRI is a challenging task in medical image analysis. There is no evident scale for the distribution of intensity over a region in medical images. Region growing is performed to generate a mask to segment the anatomical parts from MRI. A new tailored version of dilation is used in renovating the segmentation mask. This custom-made dilation differs from typical dilation in computation. The structuring element size is fixed, and the anchor point norm is changed from usual dilation. The neighborhood evaluation is made only for certain pixels that are satisfied by the proposed constraints; thus, estimation is not made throughout the image. The computation of the classical dilation is reduced with the proposed custom-made dilation.

Detekcija jezgri stanica uz pomoć algoritama evolucijskog računanja

Abstract: Main problem elaborated in this paper is detecting cell's nucleus within given microscopic photo using methods of morphology transformations optimized by evolutionary computing algorithms and thresholding methods for segmentation featuring elements. Optimization problem is finding the most suitable configuration of given morphology transformations and their given structuring element used in image segmentation. Realized versions of optimization techniques are genetic algorithm and genetic programming which features are compared in paper. Final results suggested that resulted model wasn't as successful as modern day used segmentation techniques. Although, in much simpler environment model seems to be suited for the task given. Usage of great number of images in segmentation is very time consuming and it was a barrier for delivering better results on this problem.

Performance Analysis: Efficient Structuring Element for Object Identification

Abstract: Object recognition plays an important role in digital image processing. Binary images are full of abundant imperfections. Morphologic image transforming pursues the objectives of uprooting these imperfections by managing the form and structure of the image. Different types of structuring elements play vital role for recognizing objects. Paper presents analysis of performance of use of structuring element for identifying overlapping objects.

Morphological Operators with Multiple Structuring Elements and Its Training for Image Denoising and Completion

Abstract: This paper presents morphological filters with multiple structuring elements and its training for image denoising and completion. The structuring element of the morphological filter specifies the shape and size of the local structure that are preserved or eliminated during filtering. In the basic morphological filters, the structuring element is fixed over an image. In this paper, we introduce the morphological filter with multiple structuring elements and its training for image denoising and completion. We extend the dilation and the erosion, which are basic operators of the morphological filters, to the minimum of dilations(MOD) and the maximum of erosions(MOE), respectively. MOD and MOE are applied to pepper noise removal and image completion. The set of the structuring elements is trained by a stochastic gradient descent method with image database. We demonstrate the relationship between the number of structuring elements and the image recovery capability in terms of the peak-to-peak signal noise to ratio (PSNR). The image completion results show that the morphological Laplacian based image completion with multiple structuring elements obtains comparable PSNRs to total variation minimization method.