Mathematical Morphology Operations Research Articles

High throughput automated detection of axial malformations in Medaka embryo

Fish embryo models are widely used as screening tools to assess the efficacy and/or toxicity of chemicals. This assessment involves the analysis of embryo morphological abnormalities. In this article, we propose a multi-scale pipeline to allow automated classification of fish embryos (Medaka: Oryzias latipes) based on the presence or absence of spine malformations. The proposed pipeline relies on the acquisition of fish embryo 2D images, on feature extraction based on mathematical morphology operators and on machine learning classification. After image acquisition, segmentation tools are used to detect the embryo before analysing several morphological features. An approach based on machine learning is then applied to these features to automatically classify embryos according to the presence of axial malformations. We built and validated our learning model on 1459 images with a 10-fold cross-validation by comparison with the gold standard of 3D observations performed under a microscope by a trained operator. Our pipeline results in correct classification in 85% of the cases included in the database. This percentage is similar to the percentage of success of a trained human operator working on 2D images. The key benefit of our approach is the low computational cost of our image analysis pipeline, which guarantees optimal throughput analysis.

A robust and automated cell counting method in quantification of digital breast cancer immunohistochemistry images.

Quantitative analysis ofimmunohistochemically stained breast cancer specimens by cell counting is important for prognosis and treatment planning. This paper presents arobust, accurate, and novel method to label immunopositive and immunonegative cells automatically. During preprocessing, we developed anadaptive method to correct thecolour aberration caused by imaging conditions. Next, apixel-level segmentation was performed on preprocessed images using asupport vector machine with aradial basis function kernel in HSV colour space. Thesegmentation result was processed by mathematical morphology operations to correct error-segmented regions and extract themarker for each cell. Validation studies showed that theautomated cell-counting method had divergences varying from -5.05% to 3.99% compared with manual counting by apathologist, indicating considerable agreement ofthepresent automated cell counting method with manual counting. Thus, this method can free pathologists from laborious work and can potentially improve theaccuracy and thereproducibility ofdiagnosis.

An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features

The popularity of image editing software has made it increasingly easy to alter the content of images. These alterations threaten the authenticity and integrity of images, causing misjudgments and possibly even affecting social stability. The copy-move technique is one of the most commonly used approaches for manipulating images. As a defense, the image forensics technique has become popular for judging whether a picture has been tampered with via copy-move, splicing, or other forgery techniques. In this paper, a scheme based on accelerated-KAZE (A-KAZE) and speeded-up robust features (SURF) is proposed for image copy-move forgery detection (CMFD). It is difficult for most keypoint-based CMFD methods to obtain sufficient points in smooth regions. To remedy this defect, the response thresholds for the A-KAZE and SURF feature detection stages are set to small values in the proposed method. In addition, a new correlation coefficient map is presented, in which the duplicated regions are demarcated, combining filtering and mathematical morphology operations. Numerous experiments are conducted to demonstrate the effectiveness of the proposed method in searching for duplicated regions and its robustness against distortions and post-processing techniques, such as noise addition, rotation, scaling, image blurring, joint photographic expert group (JPEG) compression, and hybrid image manipulation. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested CMFD methods.

Extraction of image parking spaces in intelligent video surveillance systems

This paper discusses the algorithmic framework for image parking lot localization and classification for the video intelligent parking system. Perspective transformation, adaptive Otsu's binarization, mathematical morphology operations, representation of horizontal lines as vectors, creating and filtering vertical lines, and parking space coordinates determination are used for the localization of parking spaces in a~video frame. The algorithm for classification of parking spaces is based on the Histogram of Oriented Descriptors (HOG) and the Support Vector Machine (SVM) classifier. Parking lot descriptors are extracted based on HOG. The overall algorithmic framework consists of the following steps: vertical and horizontal gradient calculation for the image of the parking lot, gradient module vector and orientation calculation, power gradient accumulation in accordance with cell orientations, blocking of cells, second norm calculations, and normalization of cell orientation in blocks. The parameters of the descriptor have been optimized experimentally. The results demonstrate the improved classification accuracy over the class of similar algorithms and the proposed framework performs the best among the algorithms proposed earlier to solve the parking recognition problem.

A modified discrete tomography for improving thereconstruction of unknown multi-gray-level material in the `missing wedge' situation.

Full angular rotational projections cannot always be acquired in tomographic reconstructions because of the limited space in the experimental setup, leading to the `missing wedge' situation. In this paper, a recovering `missing wedge' discrete algebraic reconstruction technique algorithm (rmwDART) has been proposed to solve the `missing wedge' problem and improve the quality of the three-dimensional reconstruction without prior knowledge of the material component's number or the material's values. By using oversegmentation, boundary extraction and mathematical morphological operations, `missing wedge' artifact areas can be located. Then, in the iteration process, by updating the located areas and regions, high-quality reconstructions can be obtained from the simulations, and the reconstructed images based on the rmwDART algorithm can be obtained from soft X-ray nano-computed tomography experiments. The results showed that there is the potential for discrete tomography.

Microgrid Islanding Detection Based on Mathematical Morphology

Voltage and frequency stability are highly important for reliable performance of smart grids. In grid-connected mode, the utility controls these parameters, but when islanding occurs these parameters exceed their limits, which may result in irreparable damage to the system. This paper presents a time-domain approach which uses basic mathematical morphology (MM) operators, dilation and erosion filters, for microgrid islanding detection. The proposed method applies a dilation-erosion differential filter (DED) of the RMS signal (DEDFOR) at the point of common coupling (PCC) in a micro-grid connected to distributed generations (DGs). To evaluate the performance of the proposed approach, it is tested and compared with existing techniques in the literature under various conditions such as capacitor bank switching and motor starting. The results verify the accuracy and efficiency of the proposed technique for islanding detection under different operating conditions and various power mismatches.

Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images

Mapping tropical tree species at landscape scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. For this purpose, detection and delineation of individual tree crowns (ITCs) is a prerequisite. Here, we present a new method of automatic tree crown delineation based only on very high resolution images from WorldView-2 satellite and apply it to a region of the Atlantic rain forest with highly heterogeneous tropical canopy cover – the Santa Genebra forest reserve in Brazil. The method works in successive steps that involve pre-processing, selection of forested pixels, enhancement of borders, detection of pixels in the crown borders, correction of shade in large trees and, finally, segmentation of the tree crowns. Principally, the method uses four techniques: rolling ball algorithm and mathematical morphological operations to enhance the crown borders and ease the extraction of tree crowns; bimodal distribution parameters estimations to identify the shaded pixels in the gaps, borders, and crowns; and focal statistics for the analysis of neighbouring pixels. Crown detection is validated by comparing the delineated ITCs with a sample of ITCs delineated manually by visual interpretation. In addition, to test if the spectra of individual species are conserved in the automatic delineated crowns, we compare the accuracy of species prediction with automatic and manual delineated crowns with known species. We find that our method permits detection of up to 80% of ITCs. The seven species with over 10 crowns identified in the field were mapped with reasonable accuracy (30.5–96%) given that only WorldView-2 bands and texture features were used. Similar classification accuracies were obtained using both automatic and manual delineation, thereby confirming that species’ spectral responses are preserved in the automatic method and thus permitting the recognition of species at the landscape scale. Our method might support tropical forest applications, such as mapping species and canopy characteristics at the landscape scale.

A morphology phase unwrapping method with one code grating.

This paper presents a fast and robust phase unwrapping method with only one code grating, named the morphology phase unwrapping (MPU) method. Using mathematical morphology operations, more than 36 fringe orders can be obtained accurately from only one code grating rather than at least three code gratings. Comparison experiments demonstrate that MPU has the highest robustness with a high quality factor (Q > 99.9%) under different conditions. Fast-changing facial expressions are reconstructed, indicating potential applications in the fields of augmented reality and virtual reality.

Failure Detection in Row Crops From UAV Images Using Morphological Operators

The detection of failures (DF) in coffee crops is fundamental in evaluating product quality and the optimal occupation of planted areas. The use of unmanned aerial vehicles (UAVs) in precision agriculture has great potential as a tool to analyze critical parameters in cultivation, among them the detection of planting failures. This letter presents a novel methodology for DF from aerial images, obtained using a UAV capable of collecting high-resolution RGB images. The proposed approach uses mathematical morphology operators to detect failures over planted areas and returns both the individual positions of these failures and total failure length (sum of empty spaces between plants), thus facilitating decision making for further actions. Results show that the proposed DF method is reliable for accurately identifying failures over rows of planted coffee crops.

A Framework for Efficient Recognition and Classification of Acute Lymphoblastic Leukemia with a Novel Customized-KNN Classifier

Even in this modern era today, life's extent is still being challenged by many pathological diseases such as cancer. One such hazard is leukemia. Even a trivial setback in detecting leukemia lead to a severe outcome: the affected cells may eventually prove to be fatal. To combat this, we propose an algorithm to better segment the nucleus region of White Blood Cells (WBC) found in stained blood smear images with the intent of identifying Acute Lymphoblastic Leukemia (ALL). In our proposal, the image is made ready for segmentation in the preprocessing stage by changing its size, brightness, and contrast. In the segmentation stage, the nucleus region is segmented by mathematical operators and Otsu's thresholding. Then mathematical morphological operators are applied in post-processing stage, which makes the nucleus region convenient for feature extraction. Finally, the segmented regions are classified into ALL affected and regular cells by means of the proposed Customized K-Nearest Neighbor classifier algorithm. This work was experimented with over 80 images of the ALL-IDB2 dataset and attained an accuracy rate of 96.25%, 95% of sensitivity and 97% of specificity.

ASTEROID (21) LUTETIA: SEMI-AUTOMATIC IMPACT CRATERS DETECTION AND CLASSIFICATION

Abstract. The need to develop an automated method, independent of lighting and surface conditions, for the identification and measurement of impact craters, as well as the creation of a reliable and efficient tool, has become a justification of our studies. This paper presents a methodology for the detection of impact craters based on their spectral and spatial features. The analysis aims at evaluation of the algorithm capabilities to determinate the spatial parameters of impact craters presented in a time series. In this way, time-consuming visual interpretation of images would be reduced to the special cases. The developed algorithm is tested on a set of OSIRIS high resolution images of asteroid Lutetia surface which is characterized by varied landforms and the abundance of craters created by collisions with smaller bodies of the solar system.The proposed methodology consists of three main steps: characterisation of objects of interest on limited set of data, semi-automatic extraction of impact craters performed for total set of data by applying the Mathematical Morphology image processing (Serra, 1988, Soille, 2003), and finally, creating libraries of spatial and spectral parameters for extracted impact craters, i.e. the coordinates of the crater center, semi-major and semi-minor axis, shadow length and cross-section. The overall accuracy of the proposed method is 98 %, the Kappa coefficient is 0.84, the correlation coefficient is ∼ 0.80, the omission error 24.11 %, the commission error 3.45 %. The obtained results show that methods based on Mathematical Morphology operators are effective also with a limited number of data and low-contrast images.

Image dehazing using morphological opening, dilation and Gaussian filtering

Image pre-processing is a critical stage in computer vision systems, with greater relevance when the input images are captured in outdoor environments because the pictures could contain low contrast and modified colors. A common condition present in outdoor images is haze. In this work, a new dehazing algorithm based on dark channel prior mathematical morphology operations (opening and dilation), and a Gaussian filter, is proposed. Moreover, the proposed algorithm performance is compared qualitatively and quantitatively against previously reported algorithms. Obtained results show that the proposed algorithm requires less processing time providing higher quality dehazing results than other state-of-the-art approaches.

Microcalcifications Detection Using Image Processing

Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as masses and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for women’s quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. In this work, an effective methodology to detect microcalcifications in digitized mammograms is presented. This methodology is based on the synergy of image processing, pattern recognition and artificial intelligence. The methodology consists in four stages: image selection, image enhancement and feature extraction based on mathematical morphology operations applying coordinate logic filters, image segmentation based on partitional clustering methods such as k-means and self organizing maps and finally a classifier such as an artificial metaplasticity multilayer perceptron. The proposed system constitutes a promising approach for the detection of Microcalcifications. The experimental results show that the proposed methodology can locate Microcalcifications in an efficient way. The best values obtained in the experimental results are: accuracy 99.93% and specificity 99.95%, These results are very competitive with those reported in the state of the art.

Detection of Age-Related Macular Degeneration in Fundus Images by an Associative Classifier

In this paper we propose the application of a novel associative classifier, the Heaviside's Classifier, for the early detection of Age-Related Macular Degeneration un retinal fundus images. Retinal fundus images are, first, processed by a simple method based on the Homomorphic filtering and some basic mathematical morphology operations; in the second phase we extract relevant features of the images using the Zernike moments, we also apply a feature selection method to select the best features from the original features set. The dataset created from the images with the best features are used to train and test a new classification model whose learning and classification phases are based on the Heaviside's Function. Experimental results show that our method is capable to achieve an accuracy value about the 94.12% with a dataset created from images belonging to famous image repositories.

An Identification Method Based on Mathematical Morphology for Sympathetic Inrush

Sympathetic interaction between transformers is a quite normal phenomenon in power systems. For the purpose of preventing transformer differential protection relays from malfunction, this paper proposes a morphological method for the identification of sympathetic inrush, which is the first time when mathematical morphology is applied in this field. Since the waveform of differential current is symmetrical in an internal fault case while asymmetrical in a sympathetic inrush case, the proposed method uses a morphological operator to extract the peaks and valleys of the differential current to distinguish sympathetic inrush. Considering the possible current-transformer (CT) saturation conditions, this paper combines a morphological gradient with a weighted mathematical morphological operator to improve the effectiveness of the proposed method. The proposed method is evaluated on data collected from simulation cases established in PSCAD/EMTDC and from laboratory experiments, respectively. Identification results have verified that by comparing with the traditional second harmonic restrain method, the proposed method can distinguish sympathetic inrush from an internal fault current more accurately and more effectively, even when the CT is fully saturated.

The New Method of Sea-sky Line Detection Based on Mathematical Morphology

Abstract To solve the problem of low accuracy and robustness of sea-sky line detection, this paper presents a method of sea- sky line detection based on the mathematical morphology. Firstly, the mathematical morphology closed-open operation is used to filter and denoise the sea-sky image. Then the Canny operator is used to obtain the sea-sky boundary of the image, Then mathematical morphological operation is used to remove some disturbing points. Finally, the sea-sky line is detected by Hough transform. The experimental results show that the algorithm can accurately and efficiently detect the sea-sky line under the complex sea-sky background.

Comparative image filter-ing using monotonic morphological operators

In a previous work, we proposed a Comparative Morphology (CM) construction scheme that generalized Pytyev's Morphological Image Analysis approach onto a wider range of practical image comparison applications. Within a Guided Contrasting framework, a filtering procedure and a related change detection algorithm were developed. In this work, we propose a class of CM filtering in which Mathematical Morphology operators introduced by Serra are used as smoothing operators that offer monotonically non-increasing (non-decreasing) filtering, in contrast to linear diffusion filtering and non-linear median filtering. The results of experiments on change detection based on the new CM filtering are discussed in comparison with other morphological procedures.

Quantitative Assessment of the Heterogeneity and Reproducibility of Repacked Silica Sand Columns

Core Ideas We quantified heterogeneity and reproducibility of repacked sand column microstructure. The averaged pore geometric properties were quantified using Minkowski functionals. Spatial heterogeneity was analyzed using a mathematical morphological operation. Column experiments are widely used for mechanism and application studies in different fields. A major challenge and an advantage of the repacked porous column are its uniformity and reproducibility. Bulk density is a commonly used parameter to evaluate the uniformity and consistency of random packing. The internal structure of pore space is usually ignored and lumped into the bulk density. However, flow and transport characteristics of the repacked porous media are not fully defined from the bulk density but also depend on the internal structure. In this paper, we quantitatively assessed the heterogeneity and reproducibility of repacked sand columns using integral geometry methodology. X‐ray computed tomography is applied to visualize the pore space of the column repacked with different silica sands. The acquired tomography images are processed and quantitatively analyzed using Minkowski measures. The Minkowski functionals combined with the granulometric density function were applied to describe the spatial heterogeneity of microstructures. The results have demonstrated that the Minkowski functionals proportional to the well‐known geometric quantities are able to characterize the average geometrical properties of complex pore space. The pore size distributions of sand column are obtained through morphological opening operation. The two‐parameter Weibull distribution has been found reasonably describe the granulometric density acquired from the morphology analysis. The Minkowski densities joint with the critical pore diameter as well as parameters of Weibull function at different locations of each column have demonstrated the heterogeneity of the sand column. The small variation in the finer sand column indicates the heterogeneity is relatively smaller than that of the coarser ones. The relatively small differences among the repetitions of finer sand columns suggest more reliable reproducibility in comparison to the coarser sand columns.

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

Red blood cell counting, as a routine examination, plays an important role in medical diagnoses. Although automated hematology analyzers are widely used, manual microscopic examination by a hematologist or pathologist is still unavoidable, which is time-consuming and error-prone. This paper proposes a full-automatic red blood cell counting method which is based on microscopic hyperspectral imaging of blood smears and combines spatial and spectral information to achieve high precision. The acquired hyperspectral image data of the blood smear in the visible and near-infrared spectral range are firstly preprocessed, and then a quadratic blind linear unmixing algorithm is used to get endmember abundance images. Based on mathematical morphological operation and an adaptive Otsu’s method, a binaryzation process is performed on the abundance images. Finally, the connected component labeling algorithm with magnification-based parameter setting is applied to automatically select the binary images of red blood cell cytoplasm. Experimental results show that the proposed method can perform well and has potential for clinical applications.

Transport characteristics of saturated gas diffusion layers treated with hydrophobic coatings

Water flooding of cathodic gas diffusion layers (GDLs) in proton-exchange membrane fuel cells at high current densities or low temperatures limits efficient operation due to disturbed transport of reactants to the catalytic sites or products away into the channels. We utilize tomography-based direct pore-level simulations to provide quantitative insights into the transport characteristics of partially saturated GDLs with and without hydrophobic surface treatment to eventually guide the design of better GDLs. High-resolution (voxel size of 1.3 μm) computed tomography images of two different types commercial Toray TGP-H-120 GDLs, one of them untreated and the other treated by hydrophobic coatings, at different water saturation levels were taken. These images were then digitally processed to precisely segment the gas, water, and fiber phases. The digitalized phase information was used in direct pore-level numerical flow simulations to determine effective relative diffusivity of the gas phase, relative permeability of the gas and liquid water phases, and tortuosity in the gas phase. Mathematical morphology operations were used to calculate size distributions of the liquid water phase and the gas phase at different saturation levels for a better understanding of the pore occupation by water at different capillary pressure. Percolation simulations were used to provide information on the connectivity of the gas and liquid phases. The results were validated with reported experimental data and semi-empirical correlations. Power law expressions provide a good level of accuracy for curve fitting. The hydrophobic coating does not affect the relative permeability and effective realtive diffusivity of air; however, it improves the water permeability significantly. The quantitative results presented provide insights and guidance for designing GDLs with better transport behavior.