Morphological Image Operations Research Articles

Thoracic Aortic Three-Dimensional Geometry.

Aortic structure impacts cardiovascular health through multiple mechanisms. Aortic structural degeneration occurs with aging, increasing left ventricular afterload and promoting increased arterial pulsatility and target organ damage. Despite the impact of aortic structure on cardiovascular health, three-dimensional (3D) aortic geometry has not been comprehensively characterized in large populations. We segmented the complete thoracic aorta using a deep learning architecture and used morphological image operations to extract multiple aortic geometric phenotypes (AGPs, including diameter, length, curvature, and tortuosity) across various subsegments of the thoracic aorta. We deployed our segmentation approach on imaging scans from 54,241 participants in the UK Biobank and 8,456 participants in the Penn Medicine Biobank. Our method provides a fully automated approach towards quantifying the three-dimensional structural parameters of the aorta. This approach expands the available phenotypes in two large representative biobanks and will allow large-scale studies to elucidate the biology and clinical consequences of aortic degeneration related to aging and disease states.

PSO-HRVSO: Segmentation of Retinal Vessels Through Homomorphic Filtering Enhanced by PSO Optimization

The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust vessel segmentation, addressing prevalent challenges identified in existing literature. The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging PSO optimization. The main processing phase is bifurcated into two configurations. Initially, thick vessels are segmented utilizing an optimized top-hat approach, homo-morphic filtering, and median filter. Subsequently, the second configuration targets thin vessel segmentation, employing the optimized top-hat method, homomorphic filtering, and matched filter. Lastly, morphological image operations are conducted during the post-processing stage. The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868, 0.8576, and 0.9831 for the STARE dataset, respectively. The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when compared to current methods. Moreover, it sur-passes all leading unsupervised methods in terms of specificity and accuracy. Additionally, it outperforms the majority of state-of-the-art supervised methods without incurring the computational costs associated with such algorithms. Detailed visual analysis reveals that the PSO-HRVSO approach enables a more precise segmentation of thin vessels compared to alternative procedures.

A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations

Abstract. Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.

Combining Morphological and Histogram based Text Line Segmentation in the OCR Context

Text line segmentation is one of the pre-stages of modern optical character recognition systems. The algorithmic approach proposed by this paper has been designed for this exact purpose. Its main characteristic is the combination of two different techniques, morphological image operations and horizontal histogram projections. The method was developed to be applied on a historic data collection that commonly features quality issues, such as degraded paper, blurred text, or presence of noise. For that reason, the segmenter in question could be of particular interest for cultural institutions, that want access to robust line bounding boxes for a given historic document. Because of the promising segmentation results that are joined by low computational cost, the algorithm was incorporated into the OCR pipeline of the National Library of Luxembourg, in the context of the initiative of reprocessing their historic newspaper collection. The general contribution of this paper is to outline the approach and to evaluate the gains in terms of accuracy and speed, comparing it to the segmentation algorithm bundled with the used open source OCR software.

On Machine-Learning Morphological Image Operators

Morphological operators are nonlinear transformations commonly used in image processing. Their theoretical foundation is based on lattice theory, and it is a well-known result that a large class of image operators can be expressed in terms of two basic ones, the erosions and the dilations. In practice, useful operators can be built by combining these two operators, and the new operators can be further combined to implement more complex transformations. The possibility of implementing a compact combination that performs a complex transformation of images is particularly appealing in resource-constrained hardware scenarios. However, finding a proper combination may require a considerable trial-and-error effort. This difficulty has motivated the development of machine-learning-based approaches for designing morphological image operators. In this work, we present an overview of this topic, divided in three parts. First, we review and discuss the representation structure of morphological image operators. Then we address the problem of learning morphological image operators from data, and how representation manifests in the formulation of this problem as well as in the learned operators. In the last part we focus on recent morphological image operator learning methods that take advantage of deep-learning frameworks. We close with discussions and a list of prospective future research directions.

An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering

Background and objective: Automatic segmentation of retinal blood vessels makes a major contribution in CADx of various ophthalmic and cardiovascular diseases. A procedure to segment thin and thick retinal vessels is essential for medical analysis and diagnosis of related diseases. In this article, a novel methodology for robust vessel segmentation is proposed, handling the existing challenges presented in the literature.Methods: The proposed methodology consists of three stages, pre-processing, main processing, and post-processing. The first stage consists of applying filters for image smoothing. The main processing stage is divided into two configurations, the first to segment thick vessels through the new optimized top-hat, homomorphic filtering, and median filter. Then, the second configuration is used to segment thin vessels using the proposed optimized top-hat, homomorphic filtering, matched filter, and segmentation using the MCET-HHO multilevel algorithm. Finally, morphological image operations are carried out in the post-processing stage.Results: The proposed approach was assessed by using two publicly available databases (DRIVE and STARE) through three performance metrics: specificity, sensitivity, and accuracy. Analyzing the obtained results, an average of 0.9860, 0.7578 and 0.9667 were respectively achieved for DRIVE dataset and 0.9836, 0.7474 and 0.9580 for STARE dataset.Conclusions: The numerical results obtained by the proposed technique, achieve competitive average values with the up-to-date techniques. The proposed approach outperform all leading unsupervised methods discussed in terms of specificity and accuracy. In addition, it outperforms most of the state-of-the-art supervised methods without the computational cost associated with these algorithms. Detailed visual analysis has shown that a more precise segmentation of thin vessels was possible with the proposed approach when compared with other procedures.

Contrast-enhanced ultrasound imaging of acute changes in pancreatic cancer following targeted hyaluronan treatment.

The purpose of this study was to monitor acute changes in pancreatic tumor perfusion with contrast-enhanced ultrasound (CEUS) imaging following targeted hyaluronan (HA) treatment. Intratumoral accumulation of HA is one of contributing factors that can lead to an increased tumor interstitial pressure (TIP). These elevated TIP levels can hinder delivery of chemotherapeutic drugs and cause treatment failure. For this study, pancreatic cancer-bearing mice were imaged at baseline and again at 2 h after intravenous administration of physiological saline (control group) or PEGPH20, which targets HA (therapy group). CEUS data were collected for 5 min and the temporal sequence was first analyzed using a singular value filter (SVF) to remove any background clutter signal. Given the time history of contrast agent flow, a tumor perfusion parametric analysis was performed. A series of morphological image operations was applied to quantify structural features of the tumor angiogenic network including vessel count, density, length, diameter, tortuosity, and branching points. After imaging, animals were euthanized, and tumors excised for histological processing. Acute microvascular changes were found at 2 h after drug administration as confirmed by CEUS imaging. Further, histologic analysis of tumor sections revealed lower HA accumulation in the therapy group animals. Overall, these findings suggest that CEUS imaging of acute changes in tumor perfusion may help identify an optimal window whereby follow-up chemotherapeutic drug dosing would be more effective.

Metamorphic Relations Based Test Oracles for Image Processing Applications

Evaluation of output images carrying visual semantics is a challenging task which is carried out by domain experts through visual inspection. Automatic test oracle is required to augment the test oracle problem and to eliminate the manual efforts. Metamorphic testing is an effective technique to alleviate these problems. In this paper, the authors have demonstrated that how inherent properties of implementation under test can be used to generate an automatic test oracle for image processing applications. Metamorphic testing is a general technique in which follow-up test cases are generated using a transformation function and the anticipated output is evaluated. They have used some general metamorphic relations and also designed some algorithm specific metamorphic relations for morphological image operations. Selection of metamorphic relations is the most important step and the authors have analyzed relative effectiveness of different metamorphic relations using mutation analysis. The results show metamorphic testing is a very effective technique to automate output images evaluation and to alleviate oracle problem.

Variable Precision Fuzzy Hit-or-Miss Transformation Models to Object Identification in Grey-Scale Images

Morphological image operators are important geometry-based image transformations, whereas fuzzy set theory and fuzzy logic, favoured for their ability to describe and cope with imprecise and uncert...

A new classification scheme of plastic wastes based upon recycling labels

Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput.In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.

Estimation of Particulate Fines on Conveyor Belts by Useof Wavelets and Morphological Image Processing

Estimation of the amount of fines in images of mineral particles using standard segmentation approaches is difficult. In this paper, an approach based on multivariate image analysis is presented for estimation of the amount of fines in particles on conveyor belts. The approach is based on two-level wavelet decomposition and morphological image operations, followed by feature extraction from gray level co-occurrence matrices. These features could be used with a simple k nearest neighbour model to estimate the proportion of fines in particulate images. Experimental results with coal and iron ore particles show that the performance of the method can yield better results than those achievable with standard methods.

Assessing arterial blood flow and vessel area variations using real‐time zonal phase‐contrast MRI

To measure peripheral artery function using a real-time phase-contrast (PC)-MRI sequence with tailored image-processing algorithms for flow computation. An approach to real-time flow measurements was developed based on two-dimensional spatially selective excitation pulses and consecutive tailored processing of the data to derive blood flow and vessel area variations. The data acquisition strategy allows for flow measurements at high spatial and temporal resolutions of 1 mm(2) and 50 msec, respectively. In postprocessing the vessel area is automatically extracted using correlation measures in conjunction with morphological image operators. By means of in vitro and in vivo validations, it is shown that the current methods provide accurate and reproducible measurements of flow and vessel area variations. In vitro the comparison between the lumen area measured with the presented method and the values obtained by caliper gauge measurement showed a difference of 3.4% +/- 3.4% (mean +/- 2 SD). Similarly, the comparison between the stroke volumes determined with the presented method and by stopwatch and bucket measurements yielded a difference of 6.1% +/- 2.1%. In vivo the results from the real-time measurements for lumen area and stroke volume were compared with those from a gated PC-MRI technique with differences of 4.8% +/- 14% and 3.0% +/- 24.7%, respectively. The presented method constitutes a reliable tool set for quantifying the variations of blood flow and lumen area in the superficial femoral artery during reactive hyperemia and for studying their correlation with cardiovascular risk factors.

Iterative design of morphological binary image operators

Iterating (composing) a sequence of window operators results in an operator defined over the window determined by the dilation of the component windows. Although a statistically designed iterative operator uses potentially all of the variables in the large dilated window, the design of each component operator requires design only over a much smaller component window, thereby resulting in a reduced estimation error. This means that designed iterative operators can perform better than designed estimates of fully optimal operators over the same effective window. While the optimal iterative and fully optimal operators may differ substantially in their logical structure, they may be probabilistically very close as operators on the random image processes under consideration. Thus, a precisely designed iterative operator can be closer to optimal than a less precisely designed unconstrained operator. We present three measures by which to compare iterative operators, with main interest focusing on the difference in their mean-absolute errors (MAEs), and discuss iterative design procedures and relationships between MAEs occurring from various procedures. A key aspect of design is the dependency on sample size. Increasing the number of iterations may in theory produce a better filter but, like using large windows, increasing the number of iterations increases the amount of data required for precise design. We pay particular attention to this issue. Using both restoration and recognition operators, we consider the best number of iterations and window size. We also consider the manner in which the training data should be split when designing the individual component operators. Iteration number, window size, and training method are all dependent on the filtering task, image characteristics, and amount of training data available.

Image sharpening by morphological filtering

This paper introduces a class of iterative morphological image operators with applications to sharpen digitized gray-scale images. It is proved that all image operators using a concave structuring function have sharpening properties. By using a Laplacian property, we introduce the underlying partial differential equation that governs this class of iterative image operators. The parameters of the operator can be determined on the basis of an estimation of the amount of blur present in the image. For discrete implementations of the operator class it is shown that operators using a parabolic structuring function have an efficient implementation and isotropic sharpening behavior.

Adaptive Linear Filtering and Morphological Image Operations with the Filter Processor

A new image computer was developed in our laboratory. It has a modular structure, allowing us to tune the machine to the application for which it will be used. One of the modules, the filter processor, will be presented in this paper. It was designed to enlarge the power of our image computer and to gain insight and experience in hardware that is suitable for adaptive filtering of images. The module operates on images stored in memory modules, providing capabilities such as normal linear filtering, adaptive linear filtering, binary and greylevel morpholog ical filtering (erosion and dilation). The design resulted in a single board, high speed processor.