Mathematical Morphology Approach Research Articles

Morphological Modelling of a Metal Foam Supported SOFC Configuration

The EVOLVE cell, named after a running FP7 project, is an innovative SOFC concept that integrates advanced materials, providing multiple functionalities (electrochemical activity, ion or/and electron conduction, gas diffusion…), and an inventive anode current collector made of a NiCrAl preoxidized foam impregnated with a conductive perovskite. Therefore, this anode combines the beneficial characteristics of MSC and ASC technologies, and strong improvements in terms of reliability and durability (inter-diffusion, Ni coarsening and agglomeration, sulphur tolerance, mechanical robustness and chemical stability under redox cycling in temperature) are expected. In order to enhance the electrode efficiency, the 3D microstructure needs to be precisely described at the microscopic scale by a morphology model. An optimized microstructure (controlled pores size, morphology and connectivity, grains size distribution, 3D tortuosity and 3D percolation of phases, volume fraction of phases, final thickness of components …) is closely related to the starting parameters (nature and quantity of starting powder, binder, shaping aids…) and to the control of the shaping process parameters (sintering treatment and atmosphere). It should meet the best compromise between a good electrocatalytic activity and a low ohmic resistivity while ensuring a stisfactory long term thermomechanical stability on the life time of the device. Based on a mathematical morphology approach applied on symmetrical LST/CGO anode layers, the present work aims at showing how a morphology model can be establised and 3D microstructural data relevant for shaping and performances can be derived from back-scattered electrons (BSE) SEM observations. For each sample, a series of images were used as input information for the microstructural modelling. All the original images were first filtered to remove the noise, thresholded and finally a series of morphological operators (openings, surface openings, closings and reconstruction with markers) were applied to remove artefacts. A 3D pluri-Gaussian model has been generated from 2D images, and then computed to predict volume fraction of the 3 phases, 3D tortuosity, gas permeation and ionic conductivities. These results were used for the optimization of the shaping process that includes the integration of the metal foam.

Mammographic image analysis based on adaptive morphological fuzzy logic CAD system

The proposed method is the cancer detection in mammogram images based on adaptive mathematical morphology and fuzzy logic approach. The mathematical morphology is a power tool based on the set theory meant to extract various features from colour and grey level images. These operators are adaptive with respective to space or intensity along with the non-linear filters for giving the accurate features of image. Fuzzy logic offers an efficient mathematically logical background to process and generate images with countless features. The proposed methodology highlights the usage of adaptive mathematical morphological operators and fuzzy logic on mammograms followed by converting the suspected area into colour space to identify the intensity level and shape of the suspicious area. Experimental results show the advanced characteristics of cancer images, the proposed method is demonstrated with various example images and evaluated metrics are tabulated.

Classification of schizophrenia patients on lattice computing resting-state fMRI features

Currently there are many efforts to find neurological biomarkers that can be extracted from resting state fMRI data. In this paper we concentrate on a study about the discrimination between schizophrenia patients and healthy control, as well as the discrimination of subpopulations of schizophrenia patients with and without auditory hallucinations. Specifically, we compute scalar measures of the high dimensional fMRI voxel time series, carrying out feature selection, feature extraction and classification by SVM over them. The dimensionality reduction is formalized as a supervised h-function proposed in recent Multivariate Mathematical Morphology approaches. It is computed as the recall error of a Lattice Auto-Associative Memory. Using as background and foreground seeds the average time series of regions of interest extracted from the brain ventricles and auditory cortex, respectively. Results on a database of healthy controls and schizophrenia patients with and without auditory hallucinations show that the approach can provide accurate discrimination between these populations.

Mathematical morphology pre-processing for enhanced segmentation of heterogeneous spatial regions

The very high spatial resolution of Pleiades images allows for the detection of small spatial objects such as buildings or isolated trees. However, the delineation of spatial regions, defined as associations between different spatial objects (such as open canopy forests or urban areas), becomes more challenging with the high level of details. On one hand, automated image segmentation algorithms often yield over-segmented polygons due to due to the high spectral heterogeneity of those regions. On the other hand, manual delineation was shown to end up with a significant bias from the interpreter and even a lack of consistency when the same person works more than one hour on the same task. In this study, we aimed at implementing a new filter to increase the contextual consistency of automated segmentation while preserving the geometric precision of the delineation of spectrally homogeneous spatial regions. A new mathematical morphology approach is proposed, which consists in applying a set of rules to an image based on the presence of absence of vegetation pixels within a structuring element. Two composite filters were then built based on the new filters. The opening filter removes isolated vegetation patches inside heterogeneous spatial regions, while the closing filter fills the gaps between those vegetation patches. The filters have been tested on a Pleiades images located in Belgium around the city of Leuven. A composite image was then created with the NIR and Red filtered bands stacked with the original image bands. The composite and the original bands were then segmented using e-Cognition software with the same parameters. The results show that the segmentation of the filtered images is spatially more consistent than the segmentation based on the unfiltered image. The over-segmentation is reduced in the heterogeneous areas, while the precision of the delineation is improved. The objects derived from the filtered images are thus more appropriate for the monitoring of spatial regions.

Mathematical morphology for TOFD image analysis and automatic crack detection

The aim of this work is to automate the interpretation of ultrasonic images during the non-destructive testing (NDT) technique called time-of-flight diffraction (TOFD) to aid in decision making.In this paper, the mathematical morphology approach is used to extract relevant pixels corresponding to the presence of a discontinuity, and a pattern recognition technique is used to characterize the discontinuity. The watershed technique is exploited to determine the region of interest and image background is removed using an erosion process, thereby improving the detection of connected shapes present in the image. Remaining shapes, are finally reduced to curves using a skeletonization technique. In the case of crack defects, the curve formed by such pixels has a parabolic form that can be automatically detected using the randomized Hough transform.

Contrast Enhancement Techniques for Images - A Visual Analysis

Image enhancement is one of the most interesting and visually appealing areas of image processing. It involves operations such as enhancing contrast, reducing noise for improving the quality of the image. This paper presents an analysis of the mathematical morphological approach with comparison to various other state-of-art techniques for addressing the problems of low contrast in images. Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. This method is simple and effective for global contrast enhancement of images but it suffers from some drawbacks. Contrast Limited Adaptive Histogram Equalization (CLAHE) enhances the local contrast of the images without the amplification of the noise. Morphological Contrast enhancement is performed using the white and black top-hat transformation. It can be performed at a single scale or at multiple scales of the structuring element. The structuring element can be of various shapes and sizes.

An Improved Technique for Gray Scale Image Enhancement based on Multiscale Morphological Approach

A method for enhancing the contrast of gray scale images using multiscale mathematical morphology is presented in this paper. The conventional theoretical concept of enhancing image has been extended from mathematical morphology to multiscale mathematical morphological approach. Gray scale images sometimes suffer from lack of contrast. So to enhance image efficiently a multiscale top hat transform based algorithm is proposed. In this algorithm multiscale structuring element of same shape and increasing size is used. By using multiscale top hat transform both white and black features at various scales of gray scale image are extracted and these features are added together to find the final enhanced image. For implementing scheme different morphological towers are built. Experiment has been performed on a set of raw gray scale images for testing efficiency both qualitatively and quantitatively and result is also compared with other state of art techniques and gets better result.

A HYBRID METHOD FOR MAMMOGRAPHY MASS DETECTION BASED ON WAVELET TRANSFORM

Introduction: Breast cancer is a leading cause of death among females throughout the world. Currently, radiologists are able to detect only 75% of breast cancer cases. Making use of computer-aided design (CAD) can play an important role in helping radiologists perform more accurate diagnoses. Material and Methods: Using our hybrid method, the background and the pectoral muscle were removed from mammography images and image contrast was enhanced using an adaptive density weighted method. First, suspected regions were extracted based on mathematical morphology and adaptive thresholding approaches. Then, in order to reduce the false positives in the suspected regions obtained in the first stage, the corresponding features were extracted using a wavelet transform, followed by the application of a support vector machine to detect masses. Results: A Mammographic Image Analysis Society (MIAS) database was used to evaluate the performance of the algorithm. The sensitivity of 81% and specificity of 84% were achieved in detecting masses. Improvement of sensitivity and specificity with our proposed hybrid algorithm was demonstrated by subjective expert-based and objective ROC-based techniques in comparison with the currently acceptable method by Masotti. Disscusion and Conclusion: In this paper, a hybrid method of pixel-based and region-based mass detection approaches is proposed to increase the specificity of the results. The accessory stage (using wavelet features) increased the sensitivity by 30%. It can be concluded that the proposed algorithm is an efficient and robust method for different types of mass detection in low-quality mammography images.

Estimation and extraction of B‐cell linear epitopes predicted by mathematical morphology approaches

B-cell epitope prediction facilitates the design and synthesis of short peptides for various immunological applications. Several algorithms have been developed to predict B-cell linear epitopes (LEs) from primary sequences of antigens, providing important information for immunobiological experiments and antibody design. This paper describes two robust methods, LE prediction with/without local peak extraction (LEP-LP and LEP-NLP), based on antigenicity scale and mathematical morphology for the prediction of B-cell LEs. Previous studies revealed that LEs could occur in regions with low-to-moderate but not globally high antigenicity scales. Hence, we developed a method adopting mathematical morphology to extract local peaks from a linear combination of the propensity scales of physico-chemical characteristics at each antigen residue. Comparison among LEP-LP/LEP-NLP, BepiPred and BEPITOPE revealed that our algorithms performed better in retrieving epitopes with low-to-moderate antigenicity and achieved comparable performance according to receiver operation characteristics (ROC) curve analysis. Of the identified LEs, over 30% were unable to be predicted by BepiPred and BEPITOPE employing an average threshold of antigenicity index or default settings. Our LEP-LP method provides a bioinformatics approach for predicting B-cell LEs with low- to-moderate antigenicity. The web-based server was established at http://biotools.cs.ntou.edu.tw/lepd_antigenicity. php for free use.

Analysis of phasic and tonic electromyographic signal characteristics: Electromyographic synthesis and comparison of novel morphological and linear-envelope approaches

The pattern of tonic and phasic components in an EMG signal reflects the underlying behaviour of the central nervous system (CNS) in controlling the musculature. One avenue for gaining a better understanding of this behaviour is to seek a quantitative characterisation of these phasic and tonic components. We propose that these signal characteristics can range between unvarying, tonic and intermittent, phasic activation through a continuum of EMG amplitude modulation. In this paper, we present two new algorithms for quantifying amplitude modulation: a linear-envelope approach, and a mathematical morphology approach. In addition we present an algorithm for synthesising EMG signals with known amplitude modulation. The efficacy of the synthesis algorithm is demonstrated using real EMG data. We present an evaluation and comparison of the two algorithms for quantifying amplitude modulation based on synthetic data generated by the proposed synthesis algorithm. The results demonstrate that the EMG synthesis parameters represent 91.9% and 96.2% of the variance of linear-envelopes extracted from lumbo-pelvic muscle EMG signals collected from subjects performing a repetitive-movement task. This depended, however, on the muscle and movement-speed considered ( F = 4.02, p < 0.001). Coefficients of determination between input and output amplitude modulation variables were used to quantify the accuracy of the linear-envelope and morphological signal processing algorithms. The linear-envelope algorithm exhibited higher coefficients of determination than the most accurate morphological approach (and hence greater accuracy, T = 8.16, p < 0.001). Similarly, the standard deviation of the coefficients of determination was 1.691 times smaller ( p < 0.001). This signal processing algorithm represents a novel tool for the quantification of amplitude modulation in continuous EMG signals and can be used in the study of CNS motor control of the musculature in repetitive-movement tasks.

Teeth Segmentation in Digitized Dental X-Ray Films Using Mathematical Morphology

Automating the process of postmortem identification of individuals using dental records is receiving increased attention. Teeth segmentation from dental radiographic films is an essential step for achieving highly automated postmortem identification. In this paper, we offer a mathematical morphology approach to the problem of teeth segmentation. We also propose a grayscale contrast stretching transformation to improve the performance of teeth segmentation. We compare and contrast our approach with other approaches proposed in the literature based on a theoretical and empirical basis. The results show that in addition to its capability of handling bitewing and periapical dental radiographic views, our approach exhibits the lowest failure rate among all approaches studied.

Segmentation and classification of cervix lesions by pattern and texture analysis

This work aims at automated segmentation of major lesions observed in early stages of uterine cervical cancer. Automated segmentation reduces subjective variability and cost in current manual evaluation methods used to determine the biopsy locations for diagnosis. Two different methods, a non-convex optimisation approach and mathematical morphological approach, are used to segment the aceto-white region. Within this region other abnormalities, such as mosaic patterns, are classified by fuzzy c-means using a textural feature obtained from skeletonised vascular structures. These vascular structures are extracted by a series of morphological operations. Minimisation of uncertainties for degraded images is also discussed.

A mathematical morphology approach to image based 3D particle shape analysis

Angularity is a critically important property in terms of the performance of natural particulate materials. It is also one of the most difficult to measure objectively using traditional methods. Here we present an innovative and efficient approach to the determination of particle angularity using image analysis. The direct use of three-dimensional data offers a more robust solution than the two-dimensional methods proposed previously. The algorithm is based on the application of mathematical morphological techniques to range imagery, and effectively simulates the natural wear processes by which rock particles become rounded. The analysis of simulated volume loss is used to provide a valuable measure of angularity that is geometrically commensurate with the traditional definitions. Experimental data obtained using real particle samples are presented and results correlated with existing methods in order to demonstrate the validity of the new approach. The implementation of technologies such as these has the potential to offer significant process optimisation and environmental benefits to the producers of aggregates and their composites. The technique is theoretically extendable to the quantification of surface texture.

Exploiting Spectral and Spatial Information in Hyperspectral Urban Data With High Resolution

Very high resolution hyperspectral data should be very useful to provide detailed maps of urban land cover. In order to provide such maps, both accurate and precise classification tools need, however, to be developed. In this letter, new methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple classifications and spatial analysis to improve mapping accuracy in urban areas. In particular, we compare spatial reclassification and mathematical morphology approaches. We show results for classification of DAIS data over the town of Pavia, in northern Italy. Classification maps of two test areas are given, and the overall and individual class accuracies are analyzed with respect to the parameters of the proposed classification procedures.

Integration of multi-sensor data for landscape modeling using a region-based approach

The increased availability of multi-sensor data, and elevation information in particular, leads to the need of advanced processing methods. In the context of landscape modeling tasks, we concentrate on one central component, the extraction of terrain surface from a Digital Surface Model (DSM). In contrast to conventional mathematical grey value morphology approaches (filtering methods) or to stochastical procedures, we propose an alternative methodology for this task by applying a region-based and multi-scale approach. It consists of segmentation and follow-up fuzzy logic classification based on several features derived from elevation and multi-spectral image data. The satisfying results obtained with a multi-sensor as well as with other datasets show the applicability of the approach.

A Mathematical Morphology Approach to the Star/Galaxy Characterization

The automated star/galaxy discrimination problem is a broadly studied issue of astronomical imaging. The success of a discrimination task depends on the features selected to characterize both classes of interest. In this work we propose an original approach to the characterization of these astronomical objects through the use of Mathematical Morphology, based on gray-level shape-size information. Our method consists of image preprocessing, segmentation and feature extraction steps, all of which employ Mathematical Morphology tools that were implemented using the MMach toolbox for the Khoros system. We briefly present a comparison between our segmentation results, based on the watershed method, and those of the SExtractor software package. The shape-size features are extracted through the use of the gray-level morphological pattern spectrum, which yields attractive analysis attributes that promise to be very suitable for future work in neural-network-based automatic classification

A mathematical morphological approach for segmenting heavily noise-corrupted images

Image segmentation is an important pre-processing step before object recognition. Here, we propose a method based on mathematical morphology for segmenting images which are heavily corrupted with noise. First we separate the foreground speck noise, the object surface and the noisy background in the image by means of the grey-scale skeleton transformation and the concept of maximal inscribed blocks. By removing the varying background and speck noise, the image is enhanced. To successfully recognize and separate the unwanted components, a size characterization algorithm is formulated based on the grey-scale morphological opening. Finally, a global thresholding can be applied to the enhanced image to obtain the object from the background. Experimental results are included to illustrate its superiority over the other two segmentation algorithms.

A mathematical morphology approach to image formation and image restoration in scanning tunnelling and atomic force microscopies

In the nanometer resolution range, image formation in STM and AFM cannot be described by a convolution process but is essentially governed by the geometrical interaction between the specimen surface and the tip surface. This non-linear process can be simply described by a dilation of the surface profile by a three-dimensional structuring element which has the shape of the tip. Accordingly, the restoration procedure using these concepts consists in performing the erosion of the image by the same structuring element. A preliminary investigation of a possible blind restoration procedure (i.e. restoration without a very precise knowledge of the tip shape and without the need of using a known test object) is performed.

A mathematical morphology approach to Euclidean distance transformation

A distance transformation technique for a binary digital image using a gray-scale mathematical morphology approach is presented. Applying well-developed decomposition properties of mathematical morphology, one can significantly reduce the tremendous cost of global operations to that of small neighborhood operations suitable for parallel pipelined computers. First, the distance transformation using mathematical morphology is developed. Then several approximations of the Euclidean distance are discussed. The decomposition of the Euclidean distance structuring element is presented. The decomposition technique employs a set of 3 by 3 gray scale morphological erosions with suitable weighted structuring elements and combines the outputs using the minimum operator. Real-valued distance transformations are considered during the processes and the result is approximated to the closest integer in the final output image.

Object representation and recognition using mathematical morphology model

The integration of representation and recognition of rigid solid objects is becoming increasingly important in computer-aided design (CAD), computer-aided manufacturing (CAM), computer graphics, computer vision, and other fields that deal with spatial phenomena. The mathematical framework used for modeling solid objects is mathematical morphology, which is based on set-theoretic concept. The mathematical characteristics of these operators are investigated in order to achieve a formal theory. Using mathematical morphology as a tool, our theoretical research aims at studying the representation schemes for the dimension and tolerance of the geometric structure. Object features can be also extracted by using the mathematical morphology approach. Through a distance transformation, we can obtain the shape number, significant points database, and skeleton. We have also developed the object recognition, localization, and corner and circle detection algorithms.