Digital Image Processing Algorithms Research Articles

Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images.

In recent years, the healthcare field has experienced significant advancements. New diagnostic techniques, treatments, and insights into the causes of various diseases have emerged. Despite these progressions, cancer remains a major concern. It is a widespread illness affecting individuals of all ages and leads to one out of every six deaths. Lung and colon cancer alone account for nearly two million fatalities. Though it is rare for lung and colon cancers to co-occur, the spread of cancer cells between these two areas-known as metastasis-is notably high. Early detection of cancer greatly increases survival rates. Currently, histopathological image (HI) diagnosis and appropriate treatment are key methods for reducing cancer mortality and enhancing survival rates. Digital image processing (DIP) and deep learning (DL) algorithms can be employed to analyze the HIs of five different types of lung and colon tissues. Therefore, this paper proposes a refined DL model that integrates feature fusion for the multi-classification of lung and colon cancers. The proposed model incorporates three DL architectures: ResNet-101V2, NASNetMobile, and EfficientNet-B0. Each model has limitations concerning variations in the shape and texture of input images. To address this, the proposed model utilizes a concatenate layer to merge the pre-trained individual feature vectors from ResNet-101V2, NASNetMobile, and EfficientNet-B0 into a single feature vector, which is then fine-tuned. As a result, the proposed DL model achieves high success in multi-classification by leveraging the strengths of all three models to enhance overall accuracy. This model aims to assist pathologists in the early detection of lung and colon cancer with reduced effort, time, and cost. The proposed DL model was evaluated using the LC25000 dataset, which contains colon and lung HIs. The dataset was pre-processed using resizing and normalization techniques. The model was tested and compared with recent DL models, achieving impressive results: 99.8% for precision, 99.8% for recall, 99.8% for F1-score, 99.96% for specificity, and 99.94% for accuracy. Thus, the proposed DL model demonstrates exceptional performance across all classification categories.

Development of Digital Image Processing Algorithms via FPGA Implementation

Real-time image processing is one of the fundamental elements in achieving IR 4.0. The rapid development of digital image processing techniques has enabled various applications in fields such as healthcare, transportation, and manufacturing. People are seeking higher-performance image processing as traditional image processing is no longer fulfilling the demands. FPGA-based digital image processing has become one of the choices for the public due to its parallel pipelining, which enables shorter processing time and better performance. Several digital image processing algorithms have been developed in this project, which are gray level transformation, brightness manipulation, contrast adjustment, thresholding, and inversion. They are the most popular algorithms used in digital image processing. Microsoft Paint is used to convert the format of the color input image to bitmap format, followed by MATLAB to convert it into a hexadecimal file to be read and written in FPGA. Platforms such as ModelSim Altera and Intel Quartus II are used to write Verilog HDL for digital image processing algorithms. As a result, five hexadecimal files are obtained from the simulation. The output hexadecimal files are further processed in MATLAB to generate respective images.

Improved Foucault method with double-shoot shadow grams in optical shop testing.

The lack of rapid, effective, and quantitative testing methods in the initial stages of optical processing has been identified as a significant challenge. In response, an innovative approach has been developed to enhance the Foucault knife-edge test method, integrating digital image processing and spatial geometry algorithms. This has resulted in the creation of a convenient and rapid optical testing method, which has been successfully implemented in practice. The method requires the collection of only two knife-edge shadow grams, which are then subjected to calculation. This enables the theoretical results of the knife-edge shadow gram at the focal point, and the reference results of the wavefront surface processing error of the mirror to be measured can be obtained. The method can be well applied to the testing work of optical workshops and provides a guiding solution with better performance for optical testing than the traditional knife-edge testing method.

Image Analysis Techniques Applied in the Drilling of a Carbon Fibre Reinforced Polymer and Aluminium Multi-Material to Assess the Delamination Damage

Due to the high abrasiveness and anisotropic nature of composites, along with the need to machine different materials at the same time, drilling multi-materials is a difficult task, and usually results in material damage, such as uncut fibres and delamination, hindering hole functionality and reliability. Image processing and analysis algorithms can be developed to effectively assess such damage, allowing for the calculation of delamination factors essential to the quality control of hole inspection in composite materials. In this study, a digital image processing and analysis algorithm was developed in Python to perform the delamination evaluation of drilled holes on a carbon fibre reinforced polymer (CFRP) and aluminium (Al) multi-material. This algorithm was designed to overcome several limitations often found in other algorithms developed with similar purposes, which frequently lead to user mistakes and incorrect results. The new algorithm is easy to use and, without requiring manual pre-editing of the input images, is fully automatic, provides more complete and reliable results (such as the delamination factor), and is a free-of-charge software. For example, the delamination factors of two drilled holes were calculated using the new algorithm and one previously developed in Matlab. Using the previous Matlab algorithm, the delamination factors of the two holes were 1.380 and 2.563, respectively, and using the new Python algorithm, the results were equal to 3.957 and 3.383, respectively. The Python results were more trustworthy, as the first hole had a higher delamination area, so its factor should be higher than that of the second one.

The Safe Catch: AI Protects Your Health from Formalin-Laced Fish

In Bangladesh, where fish is a staple food, ensuring its safety from formalin contamination poses a critical challenge due to its perishable nature. This study introduces an intelligent application employing digital image processing for the rapid and non-intrusive detection of formalin in fish. Leveraging image analysis of fish eyes, the system distinguishes between formalin and non-formalin treated fish. The proposed architecture, utilizing EfficientNet-B3 and VGG-16 models, achieved a 98.05% and 98% accuracy rate in training and validation on the dataset. This method offers a swift and accurate means of examination without damaging sample preparation, particularly beneficial in large-scale operations where manual inspection is impractical. Unlike human senses, digital image processing algorithms remain impartial, overcoming human biases and subjective judgments. Challenges persist, such as the diverse appearance of fish and external factors like varying illumination, which may impact the reliability and effectiveness of image processing programs for formalin detection. Nonetheless, this technology holds promise in addressing the pressing need for dependable and automated formalin detection in the fish supply chain, ensuring food safety and public health.

Modeling of Ethiopian Beef Meat Marbling Score Using Image Processing for Rapid Meat Grading.

Meat characterized by a high marbling value is typically anticipated to display enhanced sensory attributes. This study aimed to predict the marbling scores of rib-eye, steaks sourced from the Longissimus dorsi muscle of different cattle types, namely Boran, Senga, and Sheko, by employing digital image processing and machine-learning algorithms. Marbling was analyzed using digital image processing coupled with an extreme gradient boosting (GBoost) machine learning algorithm. Meat texture was assessed using a universal texture analyzer. Sensory characteristics of beef were evaluated through quantitative descriptive analysis with a trained panel of twenty. Using selected image features from digital image processing, the marbling score was predicted with R2 (prediction) = 0.83. Boran cattle had the highest fat content in sirloin and chuck cuts (12.68% and 12.40%, respectively), followed by Senga (11.59% and 11.56%) and Sheko (11.40% and 11.17%). Tenderness scores for sirloin and chuck cuts differed among the three breeds: Boran (7.06 ± 2.75 and 3.81 ± 2.24, respectively), Senga (5.54 ± 1.90 and 5.25 ± 2.47), and Sheko (5.43 ± 2.76 and 6.33 ± 2.28 Nmm). Sheko and Senga had similar sensory attributes. Marbling scores were higher in Boran (4.28 ± 1.43 and 3.68 ± 1.21) and Senga (2.88 ± 0.69 and 2.83 ± 0.98) compared to Sheko (2.73 ± 1.28 and 2.90 ± 1.52). The study achieved a remarkable milestone in developing a digital tool for predicting marbling scores of Ethiopian beef breeds. Furthermore, the relationship between quality attributes and beef marbling score has been verified. After further validation, the output of this research can be utilized in the meat industry and quality control authorities.

An Intelligent Automatic Sea Forecasting System Targeting Specific Areas on Sailing Routes

Sailing vessel navigators always want to receive state-of-the-art prompt and accurate marine weather-forecasting services. However, the weather-routing services by private sectors are expensive. Further, forecasting results from public institutes are usually free, and they are not in real-time or numerical modes, so they are not quite suitable for small-size or offshore vessels. In this study, an intelligent system was constructed for delivering sea forecasting at specific areas according to the navigator’s order. The system can automatically obtain web-based forecasting charts issued from multi-source meteorological agencies and convert the regional information into numerical text at requested points. During this step, several intelligent algorithms, like the OpenCV digital image processing algorithm and the YOLO wind vector deep learning recognition method, were applied. By applying this state-of-the-art system, navigators on board do not need to download different institutional graphics (usually with large stream bytes) to explore the future states of the sea surface in a specific area in the sailing route but can obtain the multi-source text forecasting information just by sending the area coordinates to a designated email address. The field tests confirmed that this auto-intelligent system could assist the navigator within a few minutes and thus greatly enhance the navigation safety with minor text-based communication costs. It is expected that by improving the efficiency of marine services and bringing in more artificial intelligence technology, maritime security would be more sustainable.

Automated identification of kinetics model in elastomer vulcanization using a rheometer MDR and Rubber-K pattern recognition software

This research developed a novel tool for the automatic characterization of elastomers during vulcanization. For this task, robust algorithms for digital image processing and pattern recognition related to curing kinetics were generated and integrated into Rubber-K software. Rheometer tests were performed under ASTM D2084 standard. Experimental data are imported in portable document format and fit the models established by Kamal-Ryan and Isayev-Deng, using a system of first-order ordinary differential equations. During the fitting of the experimental data, the reduced gradient method is used to minimize the error in the objective function. The models used in the algorithms allow higher accuracy and numerical stability compared to similar applications. Rubber-K software provides relevant information to the user during the injection molding parameterization process, allowing production optimization and significantly reducing non-conforming parts. In order to validate the results and accuracy of Rubber-K software, it was compared with data published by other software developers and similar models, whose findings confirm the power and applicability of the software.

Experimental study on the liquid film characteristics of annular flow in the helical rectangular channel

A detailed insight into the liquid film characteristics in the helical channel of single screw expander is of great significance for avoiding the leakage loss of higher pressure gas to lower pressure region. The liquid film thickness and fluctuation characteristics of the annular flow on the outer side of the helical rectangular channel is measured using the digital image processing algorithm, and they are evaluated under various flow rates, dimensionless curvature, and dimensionless pitch at different angle positions. Results show that increasing the Reg will decrease the instantaneous liquid film thickness, and a larger decrease in the liquid film fluctuation is found. A larger average liquid film thickness is located at the angle of 90° than that of the 180° and 270°. The instantaneous and average liquid film thicknesses are decreased with increasing the dimensionless curvature and dimensionless pitch at each angle position. Moreover, increasing the dimensionless curvature can weaken the effect of angle position on the liquid film thickness. The time distribution of the liquid film thickness changes dramatically in both wave height and amplitude as increasing the Rel at the smaller dimensionless pitch.

Reinforced concrete surface cracks length detection and length estimation by using digital image processing approach

Surface cracks are a common failure that occurs in reinforced concrete structures (RC). With the help of new technologies, access to crack properties should be easier and help the inspector to provide better results. However, most inspectors still prefer manual visual inspection approach, which leads to inconsistent results when investigating this flaw. Moreover, cracks with inconsistent shapes and irregularities are a difficult task for crack extraction, and inspectors overlook the details of cracks. Therefore, in this study, crack detection and thin crack appearance enhancement using various digital image processing algorithms (DSP) were proposed to improve the accuracy of crack length estimation. By using certain DSP on the captured crack images on RC, several algorithms were created and coded in MATLAB via the morphological approach to produce good quality of the original crack images. At the end of this study, the appearance of the thin crack was improved and helped to improve the estimate of the total length of the crack in pixels. The maximum percentage error between the estimated crack length was calculated and compared to the actual length and was 7.10 %. The surface crack detection algorithm and the has the potential as a helpful structural health monitoring (SHM) tool for crack inspection.

PLANT IDENTIFICATION USING MACHINE LEARNING

The preservation of therapeutic plants is crucial as it will help a broad variety of sectors, including medicine, botanic research, and plant taxonomy studies, among others. Existing technologies cannot imitate the range of therapeutic plant species present in India. The suggested technique facilitates the classification of medicinal plants by exploiting textural aspects crucial in leaf recognition and identification. The three key phases of the proposed technique are picture enhancement, feature extraction, and classification. The photographs of the leaves are shot using cellphones and then processed using digital image processing algorithms to extract the features that may be compared between them. Finally, the CNN classifier is employed to develop an automated classifier. Key Words: Plant identification, CNN algorithm, Image processing.

Remove ambiguous zones using floodfill algorithm in digital photoelasticity

Digital photoelasticity is a widely used technique for the full-field measurement of stress/strain distributions. However, ambiguous zones are present in the isochromatic phase map obtained with the commonly used ten-step phase shifting technique. Obtaining accurate isochromatic phase maps without wrapping can be challenging due to the presence of ambiguous zones. Existing methods to address this issue are complex and not fully automated. In this study, a novel approach is proposed to automatically and accurately remove the ambiguous zones using the Floodfill algorithm, an advanced digital image processing algorithm. The validity and robustness of the proposed method is demonstrated through benchmark tests on a disc and a ring subjected to diametral compression and a four-point bent beam, as well as a model with a complex geometry. The proposed method enhances the automation and simplicity of full-field stress/strain measurement in terms of the removal of ambiguous zones.

Influence of tool wear on chip-like burr formation during micro-milling, and image processing based measurement of inwardly-deflected burrs

During micro-milling, the regular top-burrs usually bend in a direction laterally opposite to the milled feature. This top-burr morphology changes as the cutting edge condition progressively deteriorates with inherent tool wear. An edge-chipped tool tends to produce unusual top-burr that resembles an incompletely separated chip. Such chip-like burr also has a tendency to flow inside the micro-milled feature. This article, for the first time, explores the integral roles of worn-out tool geometry, micro-milling mechanics and process parameters on chip-like burr formation on Ti–6Al–4V during machining using 0.5 mm diameter TiAlN-coated WC-6Co tools under minimum quantity lubrication. Round-edges of the coated micro-mill progressively pass through break-in wear, steady abrasive wear, adhesive wear, coating delamination, and edge-chipping. A comprehensive analysis is presented here to explore the mechanism of chip-like burr formation considering the changes in tool geometry and corresponding variations in chip-tool-work tribological contact, cutting forces, stress distribution, burr root thickness, burr nano-hardness and surface roughness. Further, an algorithm for digital image processing is developed for efficient and reliable measurement of the burr size and corresponding inwardly-bent fraction. Subsequently, the roles of process parameters are assessed to explore the scopes of controlling the inward bending of the chip-like burrs.

Morphological evolution of desiccation cracking in purple soil under the influence of aggregate size distribution

Desiccation cracking in purple soil subjected to rainfall-evaporation cycles is a common natural phenomenon, in the Three Gorges Reservoir area of China, that detrimentally affects the hydraulic and mechanical properties of soils. The significant spatial variability in the aggregate size distribution (ASD) of purple soil in this area leads to variations in crack morphology. In this study, we experimentally investigated the effects of ASD on morphological evolution of cracks and conducted a regression analysis based on the ASD indices of purple soil. The dynamic geometrical, topological, and statistical characteristics of the crack morphology were analysed using digital image processing and morphological algorithms. The results indicated that the process of crack evolution in purple soil mainly contained three stages, two intersection shapes, and three network forms. The crack morphology in soil with a larger initial mean aggregate size was more irregular and discontinuous. We proposed a cracking evolution function that depicts the crack geometry and topology considering the gravimetric water content as a variable, which proved to be a good fit for modeling the dynamic morphological characteristics of desiccation cracks. The distribution of crack length, crack area, and clod area were well described by the ExpDec function, whereas the crack width revealed a bimodal Gaussian distribution, with primary and secondary peaks of 1–1.5 and 2–3.5 mm, respectively. Moreover, Multiple linear regression models between the crack geometric parameters and ASD indices were established, which indicated that the ASD in the micro-aggregate state determines the final morphological characteristics of cracks, with the content of clay-sized aggregates having the most significant effect. The tensile stress of aggregates in the unsaturated state decreases with increasing radius, based on the microscopic mechanical analysis. These results could provides a theoretical basis for predicting the macroscopic cracking behavior of purple soil under various ASD conditions.

Design of a Low-Cost Diffuse Optical Mammography System for Biomedical Image Processing in Breast Cancer Diagnosis.

Worldwide, breast cancer is the most common type of cancer that mainly affects women. Several diagnosis techniques based on optical instrumentation and image analysis have been developed, and these are commonly used in conjunction with conventional diagnostic devices such as mammographs, ultrasound, and magnetic resonance imaging of the breast. The cost of using these instruments is increasing, and developing countries, whose deaths indices due to breast cancer are high, cannot access conventional diagnostic methods and have even less access to newer techniques. Other studies, based on the analysis of images acquired by traditional methods, require high resolutions and knowledge of the origin of the captures in order to avoid errors. For this reason, the design of a low-cost diffuse optical mammography system for biomedical image processing in breast cancer diagnosis is presented. The system combines the acquisition of breast tissue photographs, diffuse optical reflectance (as a biophotonics technique), and the processing of digital images for the study and diagnosis of breast cancer. The system was developed in the form of a medical examination table with a 638 nm red-light source, using light-emitted diode technology (LED) and a low-cost web camera for the acquisition of breast tissue images. The system is automatic, and its control, through a graphical user interface (GUI), saves costs and allows for the subsequent analysis of images using a digital image-processing algorithm. The results obtained allow for the possibility of planning in vivo measurements. In addition, the acquisition of images every 30° around the breast tissue could be used in future research in order to perform a three-dimensional (3D) reconstruction and an analysis of the captures through deep learning techniques. These could be combined with virtual, augmented, or mixed reality environments to predict the position of tumors, increase the likelihood of a correct medical diagnosis, and develop a training system for specialists. Furthermore, the system allows for the possibility to develop analysis of optical characterization for new phantom studies in breast cancer diagnosis through bioimaging techniques.

Grape-Bunch Identification and Location of Picking Points on Occluded Fruit Axis Based on YOLOv5-GAP

Due to the short fruit axis, many leaves, and complex background of grapes, most grape cluster axes are blocked from view, which increases robot positioning difficulty in harvesting. This study discussed the location method for picking points in the case of partial occlusion and proposed a grape cluster-detection algorithm “You Only Look Once v5-GAP” based on “You Only Look Once v5”. First, the Conv layer of the first layer of the YOLOv5 algorithm Backbone was changed to the Focus layer, then a convolution attention operation was performed on the first three C3 structures, the C3 structure layer was changed, and the Transformer in the Bottleneck module of the last layer of the C3 structure was used to reduce the computational amount and execute a better extraction of global feature information. Second, on the basis of bidirectional feature fusion, jump links were added and variable weights were used to strengthen the fusion of feature information for different resolutions. Then, the adaptive activation function was used to learn and decide whether neurons needed to be activated, such that the dynamic control of the network nonlinear degree was realized. Finally, the combination of a digital image processing algorithm and mathematical geometry was used to segment grape bunches identified by YOLOv5-GAP, and picking points were determined after finding centroid coordinates. Experimental results showed that the average precision of YOLOv5-GAP was 95.13%, which was 16.13%, 4.34%, and 2.35% higher than YOLOv4, YOLOv5, and YOLOv7 algorithms, respectively. The average positioning pixel error of the point was 6.3 pixels, which verified that the algorithm effectively detected grapes quickly and accurately.

A novel method incorporating digital image processing and A-star algorithm for soil-rock mixture (SRM) slope stability

A novel method incorporating digital image processing and A-star algorithm for soil-rock mixture (SRM) slope stability

A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces

Cracks seriously endanger the safe and stable operation of dams. It is important to detect surface cracks in a timely and accurate manner to ensure the safety and serviceability of a dam. The above-water crack detection technology of dams has been widely studied, but due to the complex underwater environment, above-water crack detection technology on dam surfaces cannot be directly applied to underwater crack detection. To adapt to the underwater detection environment and improve the efficiency and accuracy of underwater crack detection, many methods have been proposed for underwater crack detection, including sensor detection and image detection. This paper presents a systematic overview of the development and application practices of existing underwater crack detection technologies for concrete dams, focusing on methods that use underwater robots as underwater mobile carriers to acquire images that are combined with digital image processing algorithms to identify, locate, and quantify underwater cracks in dams. This method has been widely used for underwater crack detection on dam surfaces with the advantages of being non-contact, non-destructive, having high efficiency, and wide applicability. Finally, this paper looks further forward to the development trends and research challenges of detection technologies for underwater cracks on concrete dam surfaces, which will help researchers to complete further studies on underwater crack detection.

Irregular characteristic analysis of 3D particles—A novel virtual sieving technique

For digitizing the granular material systems, including asphalt mixture, the sieving size of the particles plays an irreplaceable role. However, the traditional methods based on equivalent conversion performed terribly in measuring the size of individual particles. This study combined digital image processing, ensemble learning, and over-sampling algorithms to develop a novel sieving method. Besides, the comprehensive importance ranking of variables was put forward to assist in optimizing the random forest. The results show that the data collection method proposed in this study can quickly gain bulks of digital aggregates without a complicated reconstruction process. The synthetic minority over-sampling algorithm was conducive to the accuracy ascension by balancing the database. By scoring variables' relative importance and function, the variable selection method can effectively reduce system complexity, thus improving computational efficiency and robustness. Based on the above-improving processes, the measuring precision of the aggregates' sieving opening was raised by over 20%.

Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity

The fast increase of the amount of quantitative and qualitative characteristics of digital visual data calls for the improvement of the performance of modern image processing devices. This article proposes new algorithms for 2D digital image processing based on the Winograd method in a general form. An analysis of the obtained results showed that the use of the Winograd method reduces the computational complexity of image processing by up to 84% compared to the traditional direct digital filtering method depending on the filter parameters and image fragments, while not affecting the quality of image processing. The resulting Winograd method transformation matrices and the algorithms developed can be used in image processing systems to improve the performance of the modern microelectronic devices that carry out image denoising, compression, and pattern recognition. Research directions that show promise for further research include hardware implementation on a field-programmable gate array and application-specific integrated circuit, development of algorithms for digital image processing based on the Winograd method in a general form for a 1D wavelet filter bank and for stride convolution used in convolutional neural networks.