Showing papers on "Structuring element published in 2009"

Image Processing and Mathematical Morphology: Fundamentals and Applications

Abstract: Introduction to Mathematical Morphology Basic Concept in Digital Image Processing Brief History of Mathematical Morphology Essential Morphological Approach to Image Analysis Scope of This Book Binary Morphology Set Operations on Binary Images Logical Operations on Binary Images Binary Dilation Binary Erosion Opening and Closing Hit-or-Miss Transformation Grayscale Morphology Grayscale Dilation and Erosion Grayscale Dilation Erosion Duality Theorem Grayscale Opening and Closing Basic Morphological Algorithms Boundary Extraction Region Filling Extraction of Connected Components Convex Hull Thinning Thickening Skeletonization Pruning Morphological Edge Operator Basic Morphological Filters Alternating Sequential Filters Recursive Morphological Filters Soft Morphological Filters OSSM Filters RSM Filters (RSMFs) ROSSM Filters Regulated Morphological Filters Fuzzy Morphological Filters Distance Transformation DT by Iterative Operations DT by Mathematical Morphology Approximation of Euclidean Distances Decomposition of Distance SEs Iterative Erosion Algorithm Two Scan-Based Algorithm Three-Dimensional Euclidean Distance Acquiring Approaches Deriving Approaches 7 Feature Extraction Edge Linking by MM Corner Detection by Regulated Morphology Shape Database with Hierarchical Features Corner and Circle Detection Size Histogram Object Representation Object Representation and Tolerances Skeletonization or MA Transformation Morphological Shape Description Decomposition of Morphological Structuring Elements Decomposition of Geometric-Shaped SEs Decomposition of Binary SEs Decomposition of Grayscale SEs Architectures for Mathematical Morphology Threshold Decomposition of Grayscale Morphology into Binary Morphology Implementing Morphological Operations Using Programmable Neural Networks MLP as Processing Modules A Systolic Array Architecture Implementation on Multicomputers General Sweep Mathematical Morphology Theoretical Development of General Sweep MM Blending of Sweep Surfaces with Deformations Image Enhancement Edge Linking Geometric Modeling and Sweep MM Formal Language and Sweep Morphology Grammars Parsing Algorithms Morphological Approach to Shortest Path Planning Relationships between Shortest Path Finding and MM Rotational MM The Shortest Path-Finding Algorithm Experimental Results and Discussions Dynamic Rotational MM The Rule of Distance Functions in Shortest Path Planning Index

Adaptivity and group invariance in mathematical morphology

Abstract: The standard morphological operators are (i) defined on Euclidean space, (ii) based on structuring elements, and (iii) invariant with respect to translation. There are several ways to generalise this. One way is to make the operators adaptive by letting the size or shape of structuring elements depend on image location or on image features. Another one is to extend translation invariance to more general invariance groups, where the shape of the structuring element spatially adapts in such a way that global group invariance is maintained. We review group-invariant morphology, discuss the relations with adaptive morphology, point out some pitfalls, and show that there is no inherent incompatibility between a spatially adaptive structuring element and global translation invariance of the corresponding morphological operators.

Binary Morphology With Spatially Variant Structuring Elements: Algorithm and Architecture

Abstract: Mathematical morphology with spatially variant structuring elements outperforms translation-invariant structuring elements in various applications and has been studied in the literature over the years. However, supporting a variable structuring element shape imposes an overwhelming computational complexity, dramatically increasing with the size of the structuring element. Limiting the supported class of structuring elements to rectangles has allowed for a fast algorithm to be developed, which is efficient in terms of number of operations per pixel, has a low memory requirement, and a low latency. These properties make this algorithm useful in both software and hardware implementations, not only for spatially variant, but also translation-invariant morphology. This paper also presents a dedicated hardware architecture intended to be used as an accelerator in embedded system applications, with corresponding implementation results when targeted for both field programmable gate arrays and application specific integrated circuits.

Study on nonlocal morphological operators

Abstract: This paper studies nonlocal morphological operators. Following the strategy proposed for the Nonlocal means filter, these operators are based on a local and adaptive definition of the structuring element which is specified by searching for patches in the image that are similar to the one surrounding the pixel being filtered. After presenting the basic nonlocal morphological operators using generic structuring elements, we study their robustness in the presence of noise. Then, we illustrate the interest of some nonlocal morphological operators using flat structuring elements for denoising.

Spatially-Variant Anisotropic Morphological Filters Driven by Gradient Fields

Abstract: This paper deals with the theory and applications of spatially-variant mathematical morphology. We formalize the definition of spatially variant dilation/erosion and opening/closing for gray-level images using exclusively the structuring function, without resorting to complement. This sound theoretical framework allows to build morphological operators whose structuring elements can locally adapt their orientation across the dominant direction of image structures. The orientation at each pixel is extracted by means of a diffusion process of the average square gradient field, which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the image. The proposed filters are used for enhancement of anisotropic images features such as coherent, flow-like structures.

Medical feature based evaluation of structuring elements for morphological enhancement of ultrasonic images.

Abstract: This paper investigates the use of morphology-based nonlinear filters, and performs deterministic and statistical analysis of the linear combinations of the filters for the image quality enhancement of B-mode ultrasound images. The fact that the structuring element shape greatly influences the output of the filter, is one of the most important features of mathematical morphology. The present reported work comparatively evaluates the structuring elements for morphological liver image enhancement and verifies the hypothesis that the speckles visible in US images are short, slightly ‘banana-shaped’ white lines. Initially, five different liver images were morphologically filtered using 10 different structuring elements and then the filtered images were assessed quantitatively. Image quality parameters such as peak signal-to-noise ratio, mean square error and correlation coefficient have been used to evaluate the performance of the morphological filters with different structuring elements. To endorse the obser...

Morphological filtering assisted field-pipeline small leakage detection

Abstract: Field-pipelines leakage detection based on the negative pressure wave theory is mainly aiming to effectively process pressure sample signal in time domain. Previous methods often perform well for detecting large leakage but poorly for small leakage. In this paper, an alternative way using a particular morphological filter constructed by basic morphological operators is considered. The particular filter employed can remove noise and disturbance while retaining the basic geometry shape of pressure sample signal. This feature allows the filter facilitating signal small and slow change detection. The effect of the structuring element length selection is also analyzed on simulated data to acquire empirical rules for practical applications. An experiment is studied to validate the performance of the employed morphological filter.

Pretopological operators for gray-level image analysis

Abstract: This paper deals with new operators for gray-level image analysis. These operators are based on concepts of pretopology and they extend mathematical morphology operators. Instead of using one structuring element, these new operators use a basis of several structuring elements. If this basis is composed of only one element, these operators are equivalent to mathematical mor- phology ones. This article presents the pretopological representation space and four pretopological structures of operators. Relations between these new operators and the corresponding morpholog- ical operators are described and compared. Properties and examples are displayed.

New Morphological Filtering Algorithm for Image Noise Reduction

Abstract: Conventional morphological filter is disabling to effectively preserve image details while removing noises from an image. The proposed algorithm of the selfadaptive median morphological filter is implemented as follows. First, the extreme value operation is displaced by the median operation in erosion and dilation. Then, the structuring element unit (SEU) is built based on the zero square matrix. Finally, the peak signal to noise ratio (PSNR) is used as the estimation function to select the size of the structuring element. Simulation results show that the proposed filter can effectively resolve the problem between detail-preserving and noise-removing, and its performance is obviously superior to others especially in the low signal to noise ratio situation. When the noise density is 75%, its PSNR is higher 11-14dB than the conventional morphological filter and 10-12dB than the median filter.

Segmentation of high resolution satellite imagery based on mean shift algorithm and morphological operations

Abstract: Data-driven unsupervised segmentation of high resolution remotely sensed images is a primary step in understanding remotely sensed images. A new fully automatic method to delineate the segments corresponding to objects in high resolution remotely sensed images is introduced. There are extensive methods proposed in the literature which are mainly concentrated on pixel level information. The proposed method combines the structural information extracted by morphological processing with feature space analysis based on mean shift algorithm. The spectral and spatial bandwidth parameters of mean shift are adaptively determined by exploiting differential morphological profile (DMP). Spectral bandwidth is determined in relation to the first maximum value of DMP at each pixel and spatial bandwidth is determined by the corresponding index in DMP. In this method there is also no need to specify initially the maximum size of the structuring element for the morphological processes. By the use of mean shift filtering, the feature space points are grouped together which are close to each other both in the range of spatial and spectral bandwidths. The proposed method is applied on panchromatic high resolution QuickBird satellite images taken from urban areas. The results we obtained appear to be effective in terms of segmentation and combining the spectral and spatial information to extract more precise and more meaningful objects compared to fixed bandwidth mean shift segmentation.

Adaptive mathematical morphology: A unified representation theory

Abstract: In this paper, we present a general theory of adaptive mathematical morphology (AMM) in the Euclidean space. The proposed theory preserves the notion of a structuring element, which is crucial in the design of geometrical signal and image processing applications. Moreover, we demonstrate the theoretical and practical distinctions between adaptive and spatially-variant mathematical morphology. We provide examples of the use of AMM in various image processing applications, and show the power of the proposed framework in image denoising and segmentation.

Edge detection using morphological amoebas in noisy images

Abstract: Edge detection is a significant step in image processing. Morphological edge detectors developed until now used a fixed structuring element (SE) on all the image pixels; however, they cannot consider the local features of an image due to the fixed SE and we should choose an appropriate SE by lots of experiments. In this paper, new morphological edge detectors using amoebas, dynamic structuring elements which adapt their shapes to image contours, are proposed. The experimental results show that amoeba-based edge detectors have better performance than corresponding classic edge detectors. The proposed methods have less sensitivity to noise while detecting more details of image than other morphological edge detectors with a fixed SE.

Tumor segmentation of multi-echo MR T2-weighted images with morphological operators

Abstract: In the present work an automatic brain tumor segmentation procedure based on mathematical morphology is proposed. The approach considers sequences of eight multi-echo MR T2-weighted images. The relaxation time T2 characterizes the relaxation of water protons in the brain tissue: white matter, gray matter, cerebrospinal fluid (CSF) or pathological tissue. Image data is initially regularized by the application of a log-convex filter in order to adjust its geometrical properties to those of noiseless data, which exhibits monotonously decreasing convex behavior. Finally the regularized data is analyzed by means of an 8-dimensional morphological eccentricity filter. In a first stage, the filter was used for the spatial homogenization of the tissues in the image, replacing each pixel by the most representative pixel within its structuring element, i.e. the one which exhibits the minimum total distance to all members in the structuring element. On the filtered images, the relaxation time T2 is estimated by means of least square regression algorithm and the histogram of T2 is determined. The T2 histogram was partitioned using the watershed morphological operator; relaxation time classes were established and used for tissue classification and segmentation of the image. The method was validated on 15 sets of MRI data with excellent results.

Generalized Structuring Element and Expended Morphological Filtering

Abstract: Structuring element and morphological filtering are important methods to process and analyze images. The conception and character of generalized structuring element (GS) and expended morphological filtering (EMF) are presented. The advantage of GS and EMF is given out comparing to the traditional structuring element and morphological filtering. Two typical experiments using the theory of GS and EMF show that the novel method can extract information of important components from complex image efficiently.

Improved research for overlapping cell images segmentation

Abstract: Cell images processing in medical often appear that the cell images have overlapped each other.The traditional water-shed algorithm always results in serious over segmentation phenomenon.It can avoid over segmentation well by using the distance transformation watershed algorithm.Considering that cells have a similar circular characteristic in common,doing the Ultra-erosion by using the fixed structuring element can result in cells' distortion easily.Eroding the overlapping cells by using the alternant structuring element can mark every single cell seed accurately,and it can avoid the cells’distortion by eroding effectively.Finally,it finds out the overlapping cells’watershed by using the dilation.The results show that the algorithm can avoid over segmentation effectively and improve the measurement accuracy.

Adaptive algorithm based on morphological top-hat transformation to segment Chinese square seals in bank checks

Abstract: To segment a binary seal image out of a check image without distortion,an adaptive morphological segmentation algorithm based on a top-hat transformation is proposed to accurately extract a binary Chinese square seal from a bank check.A gray scale square seal is extracted from a coloured bank check according to the colour information.Different Chinese characters have different stroke features and background evenness.To respectively process each character in the square seal,the seal is divided into four sub-squares.Then,the background across each sub-square of the grey scale seal image is smoothed by the top-hat transformation.The size of the structuring element in the top-hat transformation can have a significant influence on the segmentation.So an adaptive method is proposed to iteratively estimate the proper size of the structuring element according to the local foreground area.In each sub-square,the optimal size of the structuring elements,respectively,for the imprint frame and the character are estimated.With their optimal structuring elements,the character and the imprint frame in each sub-square are filtered by the top-hat transform and binarized by the Otsu's method.The experimental result shows that when 350 different square seals are segmented in bank checks,only 2 segmented seals have distortions.It is proved that the proposed algorithm can correctly segment Chinese characters with intricate and dense strokes in a bank check square seal.Adhesion and incompleteness distortions in the segmentation results are reduced,even when the original square seal has poor quality.

Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images

Abstract: In this paper, a new morphology-based homomorphic filtering technique is presented to enhance features in medical images. The homomorphic filtering is performed based on the morphological sub-bands, in which an image is morphologically decomposed. An evolutionary design is carried to find an optimal gain and structuring element of each sub-band. As a search algorithm, Differential Evolution scheme is utilized. Simulations show that the proposed filter improves the contrast of the interest feature in medical images.

Anchors of Morphological Operators and Algebraic Openings

Abstract: Opening and closing operators play an important role in the field of mathematical morphology, mainly because of their useful property of idempotence, which is similar to the notion of ideal filter in linear filtering. From a theoretical point of view, the study of openings has focused on the algebraic characterization of the operators themselves. Morphological filters have been studied for more than 30 years, the effects of the first filters (erosions, openings, and so on) are known in depth. In discussing the effects of a filter, it is not only the operator that is studied but also its relationship with the processed function or image and, in the particular case of mathematical morphology, the structuring element. In addition, there are several approaches to this analysis. For example, the analysis can consider the whole function or some subparts of the function, as in Eckhardt (2003), who introduced the notion of morphological anchors. Anchors were defined in the context of morphological openings, as defined by the cascade of an erosion followed by a dilation. The extension to other kinds of openings is not straightforward. Despite the fact that all morphological openings induce the appearance of anchors, some opening operators (like the quantization opening defined in this paper) might have no anchors. This chapter presents the theory of anchors related to morphological erosions and openings and then establishes some properties for the extended scope of algebraic openings. It is shown under which circumstances anchors exist for algebraic openings and how to locate some anchors; for example, it suffices for an opening to be spatial or shift-invariant to guarantee the existence of anchors. As for morphological openings, the existence of anchors may help clarify some algorithms or lead to new algorithms to compute algebraic openings.

IR image signature of target detection based on the morphology filter with self-adaptive optimized genetic algorithms

Abstract: It is utilized the morphology filter and self-adaptive genetic algorithm to present the morphology filter with selfoptimized genetic algorithms (MFGA) for detecting IR image signature of the target. According to training the structuring element from original image data, some constraint conditions such as the prior knowledge and statistics laws , we summarize a judgment rule on finding out the best of structuring elements. As two special applications about IR image signature of the detections, one is detected solid thruster plume IR image and the other is weak-small infrared target under complex background. Compared the experimental results of the MFGA with those of the morphology filter (MF), we find that the MFGA has high convergence speed, greatly enhanced the Signal Noise ratio of target detection and effectively detecting target from complex background. And the experimental results and methods have a great significance in aerial forecasting and space defense.

Disturbance detection using an improved hit-or-miss transform

Abstract: Power disturbances have always been an important issue in the power system protection. This paper presents a novel algorithm, improved hit-or-miss transform (IHMT), for the detection of power disturbances. IHMT is a morphological operator that uses a structuring element (SE) consisting of two sets. Besides, it employs basic morphological operators only, which ensures the algorithm to perform fast calculation. Applied to power systems, IHMT detects the location and duration of disturbances. A variety of power disturbances have been simulated to evaluate the validity of IHMT.

Cylinder pressure signal processing in engine based on morphological filter

Abstract: In order to remove the trend term,polynomial least square method is adopted during the course of signal preprocessing,and then the different combination of structuring element of mathematical morphology is utilized,which can effectively remove the zero drift of data acquisition and filter the noise of ignition impulseThe experiment data is processed by using generalized,different structuring element sequential clustered morphological filterThe results show that the method can eliminate the trend term and de-noise the cylinder pressure signal in engine successfully

Microarray spot detection using flat structuring periodic line and specific distance shaped morphology

Abstract: Microarray is one of the most recent and important technology for exploring the genome [1,2]. Image analysis is an important role of microarray experiments, one, which can have a potentially large impact on subsequent analysis such as clustering or the identification of differentially expressed genes [3]. Microarray image grid and spot position determination is a very important step in the analysis of microarray image because it is the first part we need to do for the analysis. By comparing gene expression in normal and abnormal cells, microarrays may be used to identify genes, which are involved in particular diseases. These genes may then be targeted by therapeutic drug. Making this part automated and fast is also important. So we consider this as indeed problem of spot detection and overcome it by using flat structuring element disk-diamond shaped morphological technique, which gives satisfied results. And also we compare the original &resultant image results on the basis of image quality measure [4]. KeywordsMicroarray, flat structuring element, morphology, segmentation, spot detection

Edge Detection using Morphological Amoebas Noisy Images

Abstract: Edge detection in images has been widely used in image processing system and computer vision. Morphological edge detection has used structuring elements with fixed shapes. This paper presents morphological operators with non-fixed shape kernels, or amoebas, which take into account the image contour variations to adapt their shape. Experimental results are analyzed in both qualitative analysis through visual inspection and quantitative analysis with PFOM and ROC curves. The Experiments demonstrate that these novel operators outperform classical morphological operations with a fixed, space-invariant structuring elements for edge detection applications.

An architecture for calculation of the distance transform based on Mathematical Morphology

Abstract: This paper presents a hardware architecture for calculating the city-block and chessboard distance transform on binary images. It is based on applying multiple morphological erosions and adding the result, enabling both processing pixels in raster scan order and a deterministic execution time. Which distance metric to be calculated is determined by the shape of the structuring element, i.e. diamonds for the city-block and squares for the chessboard. These properties together with a low memory requirement make the architecture applicable in any streaming data real-time embedded system environment with hard timing constraints, e.g. set by the frame rate. Depending on the application, a priori knowledge of the maximum size of the clusters may be used to reduce execution time and memory requirement even further. The architecture has been implemented for both FPGA and ASIC in an embedded system environment with an image resolution of 320 × 240 at a frame rate of 25 fps, running at 100 MHz and 454 MHz, respectively.

Segment and Feature Extraction of Optical Coherence Tomography Image of Mouse's Skin In Vivo Using Mathematical Morphology

Abstract: We applied mathematical morphology to segmentation and characterization of optical coherence tomography (OCT) image of mouse's skin in vivo. The dilation and the opening operations are used in this study, and the structuring element is important for each operation. We experimentally found that the mouse's skin was covered with the coverslip for overcoming the irregularity of skin's surface. And it is demonstrated that pre- processing OCT image of skin with dilation operation, in which the structuring element is line-shaped, could fill the dark area which is due to statistical distribution of photoelectric detecting, and result in segmentation and flattening the skin. Furthermore, we analyzed that the OCT image of mouse's skin could be composed of various size particle, and opening operation with disk-shaped structuring element was employed to study the particle distribution. The size distribution of OCT image of mouse's skin could be described as a feature's characterization.