Morphological Grayscale Reconstruction Research Articles

Computed Tomography Image Segmentation of Lung Corona Virus Infection Region Based on Combination of Grayscale Morphological Reconstruction and Fast Marching Method

Recently, X-ray computed tomography-imaging modality is considered as golden standard for diagnosis of coronavirus lungs infection. In worldwide, infectious patients increase rapidly that lead to weariness in health services staff, as well as instant treatment required to avoid patients’ health deterioration due to infection development. Image processing would be reinforcing health services by considering computer-based segmentation. However, a ground glass computed tomography image fashion of coronavirus lungs infection characterized by disappearance of edge region of interest and lack of object structure. In this study, these challenges addressed by introducing a new algorithm that combined both morphological reconstruction and fast marching method. The proposed algorithm applied on archived computed tomography dataset for coronavirus infected patients, results showed consistent determination of ground glass infection region compared to manual delineation of senior physician. The proposed algorithm restricted to empirical adjustment of FMM’s threshold that would be addressed in upcoming study.

Semi-automated detection of rangeland runoff and erosion control berms using high-resolution topography data

An inventory of topographic modifications is essential to addressing their impacts on hydrological and morphological processes in human-altered watersheds. However, such inventories are generally lacking. This study presents two workflows for semi-automatic detection of linear earthen runoff and erosion control berms in rangelands using high-resolution topographic data. The workflows consist of initial object identification by applying either morphological grayscale reconstruction (MGR) or the Geomorphon (GEO) method, followed by identification refinements through filters based on objects’ horizontal and vertical information. Three sites were selected within the Altar Valley, Arizona, in the southwestern United States. One site was used for developing workflows and optimizing filter thresholds, and the other two sites were used to validate workflows. The results showed that: 1) The MGR-based workflow methodology could produce final precision and detection rates of up to 92% and 75%, respectively, and take less than 5 s for a 10.1 km2 site; 2) The workflow based on the MGR method yielded greater identification accuracy than did the GEO workflow; 3) Object length, orientation, and eccentricity were important characteristics for identifying earthen berms, and are sensitive to general channel flow direction and berm shape; 4) Manual interrogation of topographic data and imagery can significantly improve identification precision rates. The proposed workflows will be useful for developing inventories of runoff and erosion control structures in support of sustainable rangeland management.

Application of Image Processing and 3D Printing Technique to Development of Computer Tomography System for Automatic Segmentation and Quantitative Analysis of Pulmonary Bronchus

This study deals with the development of a computer tomography (CT) system for automatic segmentation and quantitative analysis of the pulmonary bronchus. It includes three parts. Part I employed an adaptive median and four neighbors low pass filters to eliminate the noise of CT. Then, k-means clustering was used to segment the lung region in the CT data. In Part II, the pulmonary airway was segmented. The three-grade segmentation was employed to divide all pixels in the lung region into three uncertain grades, including air, blood vessels, and tissues, and uncertain portions. The airway wall was reformed using a border pixel weight mask. Afterwards, the seed was calculated automatically with the front-end image masking the aggregation position of the lung region as the input of the region growing to obtain the initial airway. Afterwards, the micro bronchi with different radii were detected using morphological grayscale reconstruction to modify the initial airway. Part III adopted skeletonization to simplify the pulmonary airway, keeping the length and extension direction information. The information was recorded in a linked list with the world coordinates based on the patients’ carina, defined by the directions of the carina to the top end of the trachea and right and left main bronchi. The whole set of bronchi was recognized by matching the target bronchus direction and world coordinates using hierarchical classification. The proposed system could detect the location of the pulmonary airway and detect 11 generations’ bronchi with a bronchus recognition capability of 98.33%. Meanwhile, 20 airway parameters’ measurement and 3D printing verification have been processed. The diameter, length, volume, angle, and cross-sectional area of the main trachea and the right and left bronchi, the cross-sectional area of the junction, the left bronchus length, and the right bronchus length have been calculated for clinical practice guidelines. The system proposed in this study simultaneously maintained the advantages of automation and high accuracy and contributed to clinical diagnosis.

Sparse Regularization-Based Fuzzy C-Means Clustering Incorporating Morphological Grayscale Reconstruction and Wavelet Frames

The conventional fuzzy C-means (FCM) algorithm is not robust to noise and its rate of convergence is generally impacted by data distribution. Consequently, it is challenging to develop FCM-related algorithms that have good performance and require less computing time. In this article, we elaborate on a comprehensive FCM-related algorithm for image segmentation. To make FCM robust, we first utilize a morphological grayscale reconstruction (MGR) operation to filter observed images before clustering, which guarantees noise-immunity and image detail-preservation. Since real images can generally be approximated by sparse coefficients in a tight wavelet frame system, feature spaces of observed and filtered images can be obtained. Taking such features to be clustered, we investigate an improved FCM model in which a sparse regularization term is introduced into the objective function of FCM. We design a three-step iterative algorithm to solve the sparse regularization-based FCM model, which is constructed by the Lagrangian multiplier method, hard-threshold operator, and normalization operator, respectively. Such an algorithm can not only perform well for image segmentation, but also come with high computational efficiency. To further enhance the segmentation accuracy, we use MGR to filter the label set generated by clustering. Finally, a large number of supporting experiments and comparative studies with other FCM-related algorithms available in the literature are provided. The obtained results for synthetic, medical and color images indicate that the proposed algorithm has good ability for multiphase image segmentation, and performs better than other alternative FCM-related algorithms. Moreover, the proposed algorithm requires less time than most of the existing algorithms.

Region growing algorithm combined with morphology and skeleton analysis for segmenting airway tree in CT images.

Automatic segmentation of pulmonary airway tree is a challenging task in many clinical applications, including developing computer-aided detection and diagnosis schemes of lung diseases. To segment the pulmonary airway tree from the computed tomography (CT) chest images using a novel automatic method proposed in this study. This method combines a two-pass region growing algorithm with gray-scale morphological reconstruction and leakage elimination. The first-pass region growing is implemented to obtain a rough airway tree. The second-pass region growing and gray-scale morphological reconstruction are used to detect the distal airways. Finally, leakage detection is performed to remove leakage and refine the airway tree. Our methods were compared with the gold standards. Forty-five clinical CT lung image scan cases were used in the experiments. Statistics on tree division order, branch number, and airway length were adopted for evaluation. The proposed method detected up to 12 generations of bronchi. On average, 148.85 branches were extracted with a false positive rate of 0.75%. The results show that our method is accurate for pulmonary airway tree segmentation. The strategy of separating the leakage detection from the segmenting process is feasible and promising for ensuring a high branch detected rate with a low leakage volume.

Morphological Grayscale Reconstruction and ATLD for Recognition of Organic Pollutants in Drinking Water Based on Fluorescence Spectroscopy

This paper proposes a morphological grayscale reconstruction method combined with an alternating trilinear decomposition (ATLD) and threshold method based on 3D fluorescence spectroscopy to detect pollutants present at low concentrations in drinking water. First, the morphological grayscale reconstruction method was used to locate the fluorescence peaks of pollutants by comparing the original and reconstructed spectra obtained through expansion. The signal in the characteristic spectral region was then enhanced using an amplification factor. Feature extraction was subsequently performed by ATLD, and the threshold method was used to qualitatively distinguish water quality. By comparing the proposed method with the direct use of the ATLD and threshold method—which is a commonly used feature-extraction method—this study found that the application of the morphological grayscale reconstruction method can extrude characteristics of 3D fluorescence spectra. Given the typical spectral characteristics of phenol, salicylic acid, and rhodamine B, they were selected as experimental organic pollutants. Results illustrated that the morphological grayscale reconstruction with ATLD improved the spectral signal-to-noise ratio of pollutants and can effectively identify organic pollutants, especially those present at low concentrations.

Bridge deck delamination segmentation based on aerial thermography through regularized grayscale morphological reconstruction and gradient statistics

Environmental and surface texture-induced temperature variation across the bridge deck is a major source of errors in delamination detection through thermography. This type of external noise poises a significant challenge for conventional quantitative methods such as global thresholding and k-means clustering. An iterative top-down approach is proposed for delamination segmentation based on grayscale morphological reconstruction. A weight-decay function was used to regularize the reconstruction for regional maxima extraction. The mean and coefficient of variation of temperature gradient estimated from delamination boundaries were used for discrimination. The proposed approach was tested on a lab experiment and an in-service bridge deck. The results demonstrated the improved capability of the framework to handle the non-uniform background, and thus eliminates the need of inferencing the background temperature which is often required by existing methods. That the results also suggested that the gradient statistics of the delamination boundary in the thermal image could be valid criterions to refine the segmentation under the proposed framework. Therefore, the authors concluded that the proposed method is a valid delamination segmentation approach for processing field concrete deck thermal images. The parameter selection and the limitation of this approach were also discussed. Further work will be carried out in more field cases to fine tune the parameter selection of the framework.

NIMG-22MORPHOLOGICAL RECONSTRUCTION BASED AUTOMATED SEGMENTATION OF GLIOBLASTOMA TUMOR VOLUME ON MULTI-MODAL MRI IMAGES

BACKGROUND: Glioblastoma is the most aggressive brain tumor and, magnetic resonance imaging (MRI) is the most common imaging tool for brain tumor diagnosis and disease status monitoring post any intervention including craniotomy, irradiation and various chemotherapies. Tumor volume estimation using MRI is routinely used to determine responses, such as Macdonald's or RANO's criteria. Current techniques used for tumor volume measurements are manual measurements of the largest two-dimensional tumor area and they are inaccurate and operator-dependent. Automated techniques for estimating volumetric measurements are needed for minimizing human bias and to follow disease status objectively. We developed and implement automated segmentation of tumors by fusing information from multi-modal MRI. METHODS: High resolution three-dimensional (1 mm3 isotropic voxel) T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and T1-weighted (T1) pre- and post-contrast images were acquired on nine glioblastoma patients. The images were pre-processed for co-registration, skull-stripping, intensity non-uniformities correction, noise reduction, and intensity standardization. Regional maxima were obtained on T2/FLAIR images using grayscale morphological reconstruction followed by adaptive thresholding, and regional multi-modal density distribution, connectivity, and growing techniques. Enhanced region of the tumor was obtained from the T1 pre-/post-contrast images followed by the minimization of false classifications arising from enhancing vasculature. Classified regions from T2/FLAIR, and T1 pre-/post-contrast images were combined to obtain enhancing tumor component and the surrounding FLAIR changes representing vasogenic edema/tumor infiltration as the final segmentation for volumetric measurements. RESULTS: The automatic program is able to capture and calculate tumor volumes from T1 post contrast or T2/FLAIR images accurately, as judged by neuroradiologists, neurosurgeons and neuro-oncologists. Additionally, our technique performed consistently well across all the patients that we scanned without any human intervention. CONCLUSION: We developed and implemented automated technique that identifies tumor volumes precisely, improves the accuracy and efficiency of tumor status measurements significantly with minimal human bias.

Computer-aided pulmonary image analysis in small animal models.

To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models. The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases. First, the authors' system estimates an expected lung volume by utilizing a regression function between total lung capacity and approximated rib cage volume. A significant difference between the expected lung volume and the initial lung segmentation indicates the presence of severe pathology, and invokes a machine learning based abnormal imaging pattern detection system next. The final stage of the proposed framework is the automatic extraction of airway tree for which new affinity relationships within the fuzzy connectedness image segmentation framework are proposed by combining Hessian and gray-scale morphological reconstruction filters. 133 CT scans were collected from four different studies encompassing a wide spectrum of pulmonary abnormalities pertaining to two commonly used small animal models (ferret and rabbit). Sensitivity and specificity were greater than 90% for pathological lung segmentation (average dice similarity coefficient > 0.9). While qualitative visual assessments of airway tree extraction were performed by the participating expert radiologists, for quantitative evaluation the authors validated the proposed airway extraction method by using publicly available EXACT'09 data set. The authors developed a comprehensive computer-aided pulmonary image analysis framework for preclinical research applications. The proposed framework consists of automatic pathological lung segmentation and accurate airway tree extraction. The framework has high sensitivity and specificity; therefore, it can contribute advances in preclinical research in pulmonary diseases.

A Statistical Method Based on the Image of the Ore Partical Size Distribution

A statistical method of particle size distribution is researched in order to measure the mineral blasting effect. Aimed at resolving the problems of sensitivity to noise and over-segmentation existing in the traditional watershed algorithm(TWA), a improved watershed segmentation scheme based on morphological reconstruction and mark extraction is presented. Firstly, two-stage grayscale morphological reconstruction (TGMR)is employed to filter the image. Secondly, the watershed seed area and the watershed line area are pre-calibrated in the gradient image. Finally, the watershed transformation(WT) is conducted so as to further eliminate over-segmentation. Simulations show that the method can effectively remove the noise jamming and restrain the over-segmentation. It is also able to accurately locate the ore region contour in order to improve the segmentation results.

Development of software for digital image processing for analysis of neuroangiogenesis

The process of formation, growth and distribution of vessels within the developing central nervous system is difficult to analyze due to the complexity of the paths and branches within the system. The study of images of this area poses particular problems because the high levels of noise, blurring and poor contrast often prevent the objects of interest detected correctly. The design of algorithms for digital image processing suitable for this type of imagery remains a constant challenge. The aim of this work is to develop a computer tool to assist the specialist in processing these images. This paper proposes the use of morphological grayscale reconstruction and other morphological operators in order to segment the images properly. The results show that the algorithms allow a suitable segmentation of the objects of interest. Moreover, the interface developed for processing enables easy and simple analysis of them by the specialists.

Effective segmentation and classification for HCC biopsy images

Accurate grading for hepatocellular carcinoma (HCC) biopsy images is important to prognosis and treatment planning. In this paper, we propose an automatic system for grading HCC biopsy images. In preprocessing, we use a dual morphological grayscale reconstruction method to remove noise and accentuate nuclear shapes. A marker-controlled watershed transform is applied to obtain the initial contours of nuclei and a snake model is used to segment the shapes of nuclei smoothly and precisely. Fourteen features are then extracted based on six types of characteristics for HCC classification. Finally, we propose a SVM-based decision-graph classifier to classify HCC biopsy images. Experimental results show that 94.54% of classification accuracy can be achieved by using our SVM-based decision-graph classifier while 90.07% and 92.88% of classification accuracy can be achieved by using k-NN and SVM classifiers, respectively.

An 8$\times$8 Cell Analog Order-Statistic-Filter Array With Asynchronous Grayscale Morphology in 0.13-$\mu{\hbox{m}}$ CMOS

This paper presents an integrated 8 times 8 cell image processor array demonstrating the parallel implementation of analog order-statistic filtering and grayscale mathematical morphology. Each cell, connected locally to four nearest neighbors and sized 30 times 28 mum2, includes a digitally programmable five-input analog current-mode ranked-order filter. The array also demonstrates an implementation of asynchronously propagating morphological grayscale reconstruction, which is robust against device mismatch. The measurement results of all programmable order-statistic operations are shown, verifying the correct operation of the array. The chip was manufactured in a 0.13- mum 1-poly 6-metal CMOS technology and operates from a single 1.3-V power supply.

A morphological image processing method for locating myosin filaments in muscle electron micrographs

A morphological image processing based method for determining the positions of myosin filaments in electron micrographs of muscle cross-sections is described. The filaments are embedded in a noisy and variable background in the images, but lie on a relatively regular lattice. The filament positions are determined by a novel implementation of morphological grayscale reconstruction in which a threshold is optimised by using the lattice regularity of the derived filament markers. This approach leads to a robust algorithm. The method is applied to a number of muscle micrographs.

Morphological grayscale reconstruction in image analysis: applications and efficient algorithms

Two different formal definitions of gray-scale reconstruction are presented. The use of gray-scale reconstruction in various image processing applications discussed to illustrate the usefulness of this transformation for image filtering and segmentation tasks. The standard parallel and sequential approaches to reconstruction are reviewed. It is shown that their common drawback is their inefficiency on conventional computers. To improve this situation, an algorithm that is based on the notion of regional maxima and makes use of breadth-first image scannings implemented using a queue of pixels is introduced. Its combination with the sequential technique results in a hybrid gray-scale reconstruction algorithm which is an order of magnitude faster than any previously known algorithm.