Digital Image Processing Methods Research Articles

Fluidization characteristics of loaded vibrating fluidized bed and dewatering and deashing effect of moist fine-grained coal

ABSTRACT The influence of a thermal vibrating fluidized bed on the dewatering and deashing effect of moist fine-grained coal was investigated by digital image processing and signal analysis methods. Comparative analysis results of the pressure signals of a loaded vibrating fluidized bed indicated that high or low gas velocity and strong or weak vibration conditions easily caused the formation of numerous irregular bubbles and contributed to violent fluctuations in the bed. An interaction between gas velocity and vibration energy was found, with vibration energy being the dominant factor influencing the bubble behavior at low gas velocities. Moreover, at high gas velocities, the advantages arising from vibrations were weakened. Under the condition of f = 20 Hz, A = 2 mm, v = 18 cm⋅s−1, the contact efficiency between the gas – solid two phases was higher, and the pressure fluctuation of the bed became more stable. Under this condition, the ash content of clean coal and the moisture removal rate were 11.76% and 83.54%, respectively. Finally, comparison and analysis of the influence of different parameters on the calorific value of clean coal revealed that the influence of each parameter on heat generation of the selected product was found to be in the following order: f > A > V > T.

A comparison among a fuzzy algorithm for image rescaling with other methods of digital image processing

The aim of this paper is to compare the fuzzy-type algorithm for image rescaling introduced by Jurio et al., 2011, quoted in the list of references, with some other existing algorithms such as the classical bicubic algorithm and the sampling Kantorovich (SK) one. Note that the SK algorithm is a recent tool for image rescaling and enhancement that has been revealed to be useful in several applications to real world problems, while the bicubic algorithm is widely known in the literature. A comparison among the abovementioned algorithms (all implemented in the MatLab programming language) was performed in terms of suitable similarity indices such as the Peak-Signal-to-Noise-Ratio (PSNR) and the likelihood index $S$.

Enchancing Lung Disease Classification through K-Means Clustering, Chan-Vese Segmentation, and Canny Edge Detection on X-Ray Segmented Images

The lungs are one of the vital organs in the human body. Not only play a role in the respiratory system, the lungs are also responsible for the human circulatory system. Supporting examinations can also facilitate medical workers in determining the diagnosis. Usually a lung examination is complemented by a chest X-ray examination procedure. This examination aims to see directly and assess the severity of lung conditions. With current technological advances, image analysis can be done easily. Through digital image processing methods, information can be obtained from images that can be used for analysis as a support for diagnoses in the world of health. Image segmentation is a method in which digital images are divided into several segments or subgroups based on the characteristics of the pixels in the image. In this study, clustering with the K-Means method will be carried out on the results of segmentation of x-ray images of lung diseases, namely Covid-19, Tuberculosis, and Pneumonia. The segmentation method that will be implemented is the Chan-Vese Method and the Canny Edge Detection Method. This research shows that the results of the accuracy of applying the K-Means Clustering method to Chan-Vese and Canny Edge-Based Image Segmentation are 80%.

An improved corner dealiasing and recognition algorithm for 2D Wadell roundness computation

This paper optimizes the 2D Wadell roundness calculation of particles based on digital image processing methods. An algorithm for grouping corner key points is proposed to distinguish each independent corner. Additionally, the cyclic midpoint filtering method is introduced for corner dealiasing, aiming to mitigate aliasing issues effectively. The relationships between the number of corner pixels (m), the central angle of the corner (α) and the parameter of the dealiasing degree (n) are established. The Krumbein chart and a sandstone thin section image were used as examples to calculate the 2D Wadell roundness. A set of regular shapes is calculated, and the error of this method is discussed. When α ≥ 30°, the maximum error of Wadell roundness for regular shapes is 5.21%; when 12° ≤ α < 30°, the maximum error increases. By applying interpolation to increase the corner pixels to the minimum number (m0) within the allowable range of error, based on the α-m0 relational expression obtained in this study, the error of the corner circle can be minimized. The results indicate that as the value of m increases, the optimal range interval for n also widens. Additionally, a higher value of α leads to a lower dependence on m. The study's results can be applied to dealiasing and shape analysis of complex closed contours.

APPROACH TO THE AUTOMATIC CREATION OF AN ANNOTATED DATASET FOR THE DETECTION, LOCALIZATION AND CLASSIFICATION OF BLOOD CELLS IN AN IMAGE

Context. The paper considers the problem of automating the creation of an annotated dataset for further use in a system for detecting, localizing and classifying blood cells in an image using deep learning. The subject of the research is the processes of digital image processing for object detection and localization.&#x0D; Objective. The aim of this study is to create a pipeline of digital image processing methods that can automatically generate an annotated set of blood smear images. This set will then be used to train and validate deep learning models, significantly reducing the time required by machine learning specialists.&#x0D; Method. The proposed approach for object detection and localization is based on digital image processing methods such as filtering, thresholding, binarization, contour detection, and filling. The pipeline for detection and localization includes the following steps: The given fragment of text describes a process that involves noise reduction, conversion to the HSV color model, defining a mask for white blood cells and platelets, detecting the contours of white blood cells and platelets, determining the coordinates of the upper left and lower right corners of white blood cells and platelets, calculating the area of the region inside the bounding box, saving the obtained data, and determining the most common color in the image; filling the contours of leukocytes and platelets with said color; defining a mask for red blood cells; defining the contours of red blood cells; determining the coordinates of the upper left and lower right corners of red blood cells; calculating the area of the region within the bounding box; entering data about the found objects into the dataframe; saving to a .csv file for future use. With an unlabeled image dataset and a generated .csv file using image processing libraries, any researcher should be able to recreate a labeled dataset.&#x0D; Results. The developed approach was implemented in software for creating an annotated dataset of blood smear images&#x0D; Conclusions. The study proposes and justifies an approach to automatically create a set of annotated data. The pipeline is tested on a set of unlabelled data and a set of labelled data is obtained, consisting of cell images and a .csv file with the attributes “file name”, “type”, “xmin”, “ymin”, “xmax”, “ymax”, “area”, which are the coordinates of the bounding box for each object. The number of correctly, incorrectly, and unrecognised objects is calculated manually, and metrics are calculated to assess the accuracy and quality of object detection and localisation.

Experimental Study on Atomization Characteristics of Swirl Nozzle under Annular Airflow Impingement

Pressure nozzles are widely used in spray drying and other industries. In order to improve the atomization characteristics of pressure cyclone nozzles, a new type of annular jet gas impingement atomization device is developed. We use high-speed imaging and digital image processing and other methods to analyze the spray characteristics of the different annular device configurations (using four, six, and eight tubes) and under different gas–liquid mass flow rates. It is shown that with an increase in the Air–Liquid mass Ratio (ALR), the liquid film breakup process changes from undulating sheet breakup to perforated sheet breakup and the breakup length decreases. The breakup length decreases the most under the condition of six-tube airflow with the range of 31–55%, while the Sauter mean diameter (SMD) basically does not change. With the increase in ALR and the Weber number of liquid (Wel), the droplet size distribution becomes more uniform. The spray characteristics of the atomizer assisted by gas jets reaches the best state when Wel = 4596.3 and m˙g = 1.97 g/s. The experimental conclusions have some guiding significance for the design and optimization of the atomization devices in spray drying towers.

Sequence of Simple Digital Technologies for Detection of Platelets in Medical Images

Platelets play an important role in the diagnosis and detection of various diseases, the course of the disease in the future, and the possibility of justifying treatment methods. In this aspect, platelet counting is of key importance. For these purposes, it is important to correctly identify such objects. This served as the basis for the development and generalization of an appropriate medical image analysis procedure. The purpose of this study is to construct a generalized procedure for platelet identification in medical digital images. The work examined at least 30 images containing objects such as platelets. These images are approximately the same type, but with different intensity of the presence of the main objects in the blood smear. Similar but noisy images are also considered. These images were noisy with different types of noise. Thus, a total of at least 120 images were examined. In general, this allows us to evaluate the effectiveness of the proposed procedure for identifying platelets in medical images. This procedure includes simple methods of image analysis such as: binarization, morphological analysis, taking into account the influence of the sizes of different objects and comparative analysis of images at intermediate stages of the study. To summarize the results, estimates such as the percentage of false detection of platelets and the percentage of missed platelets were considered. The platelet identification results that were obtained for non-noisy images are as follows: false platelet isolation was less than 0.1%, missed platelets were within 2-2.5%. The worst result for noisy images is false platelet isolation – within 10% (for images with multiplicative noise), missed platelets – within 7.5-8% (for images with multiplicative noise). It should also be noted that noisy images are characterized by identification of platelets with distortion of their sizes: these sizes are reduced or increased. The percentage of such distortions does not exceed 1.3% (for images with Poisson noise).The problematic aspects of platelet identification in digital medical images are considered. Particular attention is paid to simple methods of digital image processing. Among the problematic aspects of the proposed approach there is a need to clearly take into account the geometric dimensions of platelets. The results obtained are acceptable and can be used as the basis for an automated blood smear analysis system.

Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes.

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.

Tunable optical spatial differential operation via photonic spin Hall effect in a Weyl semimetal.

Optical differential operation is the basic principle of optical image edge detection, which has the advantages of high efficiency, simple structure and markerless compared with the traditional digital image processing methods. In this paper, we propose an optical differential operation with high contrast based on the photonic spin Hall effect in a Weyl semimetal, which enables to switch between one- and two-dimensional edge detection. Due to the unique optical and electrical properties of the Weyl semimetal, a transport model for the differential operation is established, which is closely related to the beam shifts. By tuning the incidence conditions, we effectively manipulate the in-plane and transverse shifts to switch differential operations between one and two dimensions. The contrast of the differential operation is further regulated by changing the physical parameters of the Weyl semimetal, and can be improved by two orders of magnitude compared to the conventional differentiator. This study provides new possibilities in edge detection and image processing owing to the advantages of switchable dimension and high contrast.

Analysis of the Effect of Corner Points and Image Resolution in a Mechanical Test Combining Digital Image Processing and Mesh-free Method

Analysis of the Effect of Corner Points and Image Resolution in a Mechanical Test Combining Digital Image Processing and Mesh-free Method

Quantification of fibrosis extend and airspace availability in lung: A semi-automatic ImageJ/Fiji toolbox.

The evaluation of the structural integrity of mechanically dynamic organs such as lungs is critical for the diagnosis of numerous pathologies and the development of therapies. This task is classically performed by histology experts in a qualitative or semi-quantitative manner. Automatic digital image processing methods appeared in the last decades, and although immensely powerful, tools are highly specialized and lack the versatility required in various experimental designs. Here, a set of scripts for the image processing software ImageJ/Fiji to easily quantify fibrosis extend and alveolar airspace availability in Sirius Red or Masson's trichrome stained samples is presented. The toolbox consists in thirteen modules: sample detection, particles filtration (automatic and manual), border definition, air ducts identification, air ducts walls definition, parenchyma extraction, MT-staining specific pre-processing, fibrosis detection, fibrosis particles filtration, airspace detection, and visualizations (tissue only or tissue and airspace). While the process is largely automated, critical parameters are accessible to the user for increased adaptability. The modularity of the protocol allows for its adjustment to alternative experimental settings. Fibrosis and airspace can be combined as an evaluation of the structural integrity of the organ. All settings and intermediate states are saved to ensure reproducibility. These new analysis scripts allow for a rapid quantification of fibrosis and airspace in a large variety of experimental settings.

THE METHOD FOR OBJECTS DETECTION ON SATELLITE IMAGERY BASED ON THE FIREFLY ALGORITHM

The subject matter of the article is the method for detecting of objects on satellite imagery based on the firefly algorithm. The goal is to develop a method for detecting of objects on satellite imagery based on the firefly algorithm. The tasks are: analysis of existing methods for detecting of objects of interest on satellite imagery, development of a method for detecting of objects on satellite imagery, practical verification of the method for detecting of objects on satellite imagery based on the firefly algorithm, and quantitative assessment of the quality of the proposed method. The methods used are: methods of digital image processing, methods of data clustering, mathematical apparatus of matrix theory, methods of swarm intelligence, methods of mathematical modeling, methods of optimization theory, analytical and empirical methods of image comparison. The following results are obtained. The advantages and disadvantages of the main methods and approaches to the processing of satellite imagery for the purpose of detecting objects of interest on them are determined. The general principle of operation of the firefly algorithm is considered. It presents a flowchart of the method for detecting of objects on satellite imagery based on the firefly algorithm in one color channel. The values of the input data and parameters for the operation of the algorithm were selected experimentally. Experimental studies were conducted on the operation of the method for detecting of objects on a real satellite imagery based on the firefly algorithm. The values of the errors of the first and second kind for the processed image using the proposed method and the method based on the particle swarm algorithm were calculated. Conclusions. Analysis of the calculated values showed that the proposed method for detecting of objects on satellite imagery compared to the method based on the particle swarm algorithm: reduces the error of the first kind by about 11% and the error of the second kind by about 9%. The directions of further research are the study of the problem of selecting input parameters and data for the operation of the method based on the firefly algorithm.

Automatic detection and characterization of discontinuity traces and rock fragment size distribution using a digital image processing method

Rock materials naturally contain cracks and fractures, which may produce rock fragments or grains under geological and engineering conditions. Identification and characterization of the spatial extent of discontinuity traces and size distribution of fragments/grains are prerequisites for accurate geomechanical assessments of the stability of rocks containing natural or artificial discontinuities. This study introduces an advanced image processing methodology that leverages color gradient analysis and multi-threshold criteria to effectively detect and delineate fracture traces or boundary outlines. Furthermore, this approach facilitates the acquisition of precise fragment or grain size distribution statistics. To comprehensively characterize these discontinuity traces, the methodology employs the calculation of fractal dimensions alongside the use of an equivalent diameter conversion technique. These tools enable the quantification of the complexity and size distribution of the fragments or grains. The efficacy of this approach has been demonstrated through a series of case studies, encompassing a wide range of applications including digital images from numerical simulations, experimental tests, field observations, and micrographic analyses of rock structures. In these studies, the method proved capable of satisfactorily calculating the fractal dimensions of fractures, cracks, and contacts. Simultaneously, it accurately and efficiently determined the size distribution, as evidenced by frequency histograms and cumulative distribution curves obtained from the images. In essence, the proposed approach provides an automatic quantitative methodology to identify and characterize macroscopic and microscopic rock structures containing discontinuity traces or comprised of fragments/grains. This advancement holds significant potential for enhancing the precision and efficiency of geological and geomechanical assessments.

PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR

Component products used for manufacturing a machine in manufacturing companies have two types of products, type A and B. The problem that often occurs in the industry is product sorting errors due to the traditional sorting process, using human labor. The disadvantages are limited human labor so that fatigue can occur, causing errors in sorting products and losses for the company. Many studies discuss object detection, Industrial problems in the checking process can be approached with the help of this technology. Object detection works to analyze frames with the method of finding objects. There are methods in digital image processing, CNN algorithms which include methods in computer vision. The growing framework makes the CNN algorithm more powerful. YOLO includes a framework based on the CNN algorithm. YOLOv5 detects objects by taking into account the object's confidence value, the output of the detected object is a bounding box on the object. The problem in the industry in the checking process can be approached with the help of this technology. For this reason, this research aims to create a model for product object detection in manufacturing companies. The process carried out is data collection, image annotation, training, testing, evaluation. The images collected were 137 for training data and 34 for validation data totaling 171 image data. The results of the model using YOLOv5 with epoch 1000 get a precision value of 100%, recall 100% and mAP 99%, the product detection results get an average value of 100%.

TBNet: learning from scratch and limited training data, a CNN based tuberculosis bacilli detection

Tuberculosis (TB) is an infectious disease caused by the micro-bacteria. Several studies that have been conducted previously aimed to reduce the burden of observing tuberculosis bacilli using the digital image processing method. In this study, we proposed a newly developed convolutional neural network (CNN) based deep learning model to detect tuberculosis bacilli in sputum smear images. Recent advances in deep learning apply large scale image dataset to achieve convergent weight model. However, medical image dataset commonly available in relatively small quantity. In contrary with common deep learning approach, our model is capable to learn from our small dataset which consist of highly diverse hue and contrast of sputum smear images. Furthermore, its performance is proven to be reliable to detect sputum smear image content, which are TB bacillus and debris.

Application of digital image processing method for roasted coffee bean quality identification: a systematic literature review

Application of digital image processing method for roasted coffee bean quality identification: a systematic literature review

EARLY STAGES OF COLORECTAL CANCER CHARACTERIZATION BY AUTOFLUORESCENCE 3D MICROSCOPY: A PRELIMINARY STUDY.

Colorectal cancer is one of the most prevalent pathologies worldwide whose prognosis is linked to early detection. Colonoscopy is the gold standard for screening, and diagnosis is usually made histologically from biopsies. Aiming to reduce the inspection and diagnostic time as well as the biopsies and resources involved, other techniques are being promoted to conduct accurate in vivo colonoscopy assessments. Optical biopsy aims to detect normal and neoplastic tissues analysing the autofluorescence spectrum based on the changes in the distribution and concentration of autofluorescent molecules caused by colorectal cancer. Therefore, the autofluorescence contribution analysed by image processing techniques could be an approach to a faster characterization of the target tissue. Quantify intensity parameters through digital processing of two data sets of three-dimensional widefield autofluorescence microscopy images, acquired by fresh colon tissue samples from a colorectal cancer murine model. Additionally, analyse the autofluorescence data to provide a characterization over a volume of approximately 50 µm of the colon mucosa for each image, at second (2nd), fourth (4th) and eighth (8th) weeks after colorectal cancer induction. Development of a colorectal cancer murine model using azoxymethane/dextran sodium sulphate induction, and data sets acquisition of Z-stack images by widefield autofluorescence microscopy, from control and colorectal cancer induced animals. Pre-processing steps of intensity value adjustments followed by quantification and characterization procedures using image processing workflow automation by Fiji's macros, and statistical data analysis. The effectiveness of the colorectal cancer induction model was corroborated by a histological assessment to correlate and validate the link between histological and autofluorescence changes. The image digital processing methodology proposed was then performed on the three-dimensional images from control mice and from the 2nd, 4th, and 8th weeks after colorectal cancer chemical induction, for each data set. Statistical analyses found significant differences in the mean, standard deviation, and minimum parameters between control samples and those of the 2nd week after induction with respect to the 4th week of the first experimental study. This suggests that the characteristics of colorectal cancer can be detected after the 2nd week post-induction. The use of autofluorescence still exhibits levels of variability that prevent greater systematization of the data obtained during the progression of colorectal cancer. However, these preliminary outcomes could be considered an approach to the three-dimensional characterization of the autofluorescence of colorectal tissue, describing the autofluorescence features of samples coming from dysplasia to colorectal cancer. • A new digital image processing method was developed to measure intensity in 3D autofluorescence images of colorectal samples using a CRC mouse model. • This method showed that autofluorescence intensity in colon mucosa is similar in healthy tissue but changes significantly in tumor development. • Statistical analysis revealed CRC traits detectable from the second week post-induction, aiding in early CRC detection. • The study provides a basis for 3D autofluorescence characterization in colorectal tissue from dysplasia to cancer, although variability in autofluorescence limits data systematization during cancer progression.

Fish fry counter based on digital image processing method

Large quantities of ornamental fish fry can be time-consuming and error-prone to count manually. The tedious counting of ornamental fish fry can also be stressful and result in the death of the fish fry, which can result in lost sales for ornamental fish businesses. In order to solve these issues for the ornamental fish businesses, the goal of this research is to develop a system for automatically counting the number of fish fry using the thresholding and morphology methods based on digital image processing. The fish fry counter has been tested with four distinct types of fish fry, is capable of counting up to 130 fish fry in 1-3 seconds for a single operation. The final result generated by this tool are an image with a description of the total number of fish fry encountered, the date and time of data collection, and the number of fish fry detected. This information are stored in a database with .xlsx extension. The experiments result appears that this tool can count the number of fish fry corresponding to different colored fish species. However, when calculating the total amount of fish fry that can fit into the container to its full capacity, the tool has an accuracy of 95.86% and an average error of 4.14% that is caused by the side of the container which contains fish fry that are not visible to the detection camera (blind spot).

Investigation of the dynamic behavior and fracturing mechanism of granite

In examining the dynamic behavior and fracturing mechanism of granite using numerical simulation methods, many studies assume a random distribution of minerals, which fails to accurately represent the actual distribution. The primary objects of this study are therefore to combine digital image processing (DIP) method with the discrete element method (DEM) to develop a more accurate granite model that matches the real mineral distribution of the granite specimen. The DEM is then used to simulate the Split Hopkinson Pressure Bar (SHPB) test, exploring how mineral properties like mineral content, grain shape, grain size, and loading conditions such as strain rate and confining pressure, affect the dynamic behavior and fracture pattern of granite. The results demonstrate that granite models, despite differing grain shapes and sizes, display similar dynamic strength (varying by 4.8%) when they have comparable mineral contents. Feldspar content significantly affects stress variations, while quartz and biotite have minor impact. Stronger minerals like quartz and feldspar show similar fracture patterns, whereas biotite leads to a larger damage area and denser fracture distribution. Loading conditions, such as strain rate and confining pressure, significantly influence the fracture behavior of granite specimens. Fracture propagation is primarily characterized by tension cracks, with shear cracks following, and their distribution varies based on mineral properties and loading conditions.

Roselle Pest Detection and Classification Using Threshold and Template Matching

Roselle is a fiber-producing plant that has broad benefits for health food, so many farmers are interested in starting to cultivate it. This study aims to design a rosella plant pest detection system to reduce the risk of crop failure or reduced yields of rosella calyx. The design of a system for detecting and classifying rosella pests uses the threshold method as a digital image processing method connected via the internet with information media applications and template matching to detect and classify pests on rosella plants. Detection of pests on rosella plants has been successfully built using a detection system using thresholding and template matching methods. Datasets of rosella plant pests that are not yet widely available encourage the detection of rosella plant pests with datasets from rosella plant objects and limited data testing. Testing with 75% accuracy, the detection process is affected by light and camera quality.