Segmented Images Research Articles

Tuned weighted feature fusion with hybridized DNN-RNN framework for plant disease detection and classification

ABSTRACT In the world economy, agriculture is an essential part among individuals to earn money. But, the farmers face more obstacles because more diseases naturally affect the health of the plant. Hence, to overcome this limitation, automatic plant disease identification techniques are essential to monitor the growth as well as disease-affected plants and leaves. In most plants, symptoms related to plant disease can be identified by considering the leaves. Moreover, plant disease identification became more essential with deep learning architecture. Hence, it is important to enhance the solution for resource-constrained portable devices like smartphones, but it become a complicated issue as the deeply structured methods utilize extensive resources for the analysis. Hence, this paper aims to develop an automated plant disease detection framework with an advanced set of plant features. The plant images are taken from online sources. The acquired plant images undergo image enhancement using the contrast enhancement process. The enhanced images are fed in the feature retrieval phase 1, where the segmentation takes place using the Centre Optimized K-means clustering (CO-KMC) with the Modified Random Variable-based Black Widow Optimization (MRV-BWO). Later, the segmented images obtain the colour, shape, and texture features and they are considered as the first set of features. The second set of features is retrieved from the enhanced images using Multi-scale Dilated Attention CNN (MSDA-CNN). These two features are fused by the weighted feature fusion process using the developed MRV-BWO. Then, the plant diseases are detected using the Hybridized Deep Neural Network with Recurrent Neural Network (HDNN-RNN), which comprises RNN and DNN models. The parameter optimization takes place in the hybrid HDNN-RNN for improving the detection efficiency using MRV-BWO. From the verification of the findings, the recommended model obtains a 96.47 precision and accuracy rate. Therefore, the simulation results of the offered model reveal that it achieves enriched efficacy when comparing other baseline approaches.

Automated angular measurement for puncture angle using a computer-aided method in ultrasound-guided peripheral insertion.

Ultrasound guidance has become the gold standard for obtaining vascular access. Angle information, which indicates the entry angle of the needle into the vein, is required to ensure puncture success. Although various image processing-based methods, such as deep learning, have recently been applied to improve needle visibility, these methods have limitations, in that the puncture angle to the target organ is not measured. We aim to detect the target vessel and puncture needle and to derive the puncture angle by combining deep learning and conventional image processing methods such as the Hough transform. Median cubital vein US images were obtained from 20 healthy volunteers, and images of simulated blood vessels and needles were obtained during the puncture of a simulated blood vessel in four phantoms. The U-Net architecture was used to segment images of blood vessels and needles, and various image processing methods were employed to automatically measure angles. The experimental results indicated that the mean dice coefficients of median cubital veins, simulated blood vessels, and needles were 0.826, 0.931, and 0.773, respectively. The quantitative results of angular measurement showed good agreement between the expert and automatic measurements of the puncture angle with 0.847 correlations. Our findings indicate that the proposed method achieves extremely high segmentation accuracy and automated angular measurements. The proposed method reduces the variability and time required in manual angle measurements and presents the possibility where the operator can concentrate on delicate techniques related to the direction of the needle.

Multimodal Imaging of Choroidal Nodules in Neurofibromatosis Type I.

To clarify the possibilities and role of posterior segment imaging in patients with neurofibromatosis type I (NF1), and to show the prevalence of this disease in the pediatric population in Slovakia. Until recently, ophthalmologic consultations in patients with NF1 were limited mainly to the observation of Lisch nodules of the iris and the presence of optic nerve glioma. However, advances in imaging capabilities have made it possible to investigate and describe new f indings concerning the ocular manifestations of this disease. Between October 2020 and November 2021, we examined the anterior and posterior segment of 76 eyes (38 children – 12 boys and 26 girls) with genetically confirmed NF1 gene mutation at our clinic. The age of the patients ranged from 4 to 18 years. The anterior segment was checked for the presence of Lisch nodules biomicroscopically with a slit lamp. On the posterior segment, the presence of choroidal nodules was checked by various imaging methods – fundus camera, infrared confocal selective laser ophthalmoscopy, MultiColor imaging, OCT, and OCT angiography. All the patients had magnetic resonance imaging performed in order to detect potential optic nerve gliomas for the purpose of diagnosis. We observed the correlation between the patients’ age, presence of Lisch nodules and the presence of choroidal nodules. Eight patients also had other manifestations of the disease – optic nerve gliomas or microvascular changes (so-called “corkscrew” vessels). Out of 38 patients, Lisch iris nodules were present in 20 patients (53%) and choroidal nodules in 24 patients (63%). There was no positive correlation between the presence of these two manifestations within the same patient or eye, but there is a clear correlation between the presence of choroidal nodules and patient age. The results suggest that a previously unknown ocular manifestation of neurofibromatosis type I, namely choroidal nodules, has a higher prevalence than Lisch nodules also in the pediatric population and can be easily visualized using various imaging modalities. It will be important to include follow-up observation of this finding among the standard controls for ocular findings in NF1, and it will be very interesting to correlate this f inding with the exact NF1 mutation

Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.

Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach.

Lung nodules are generated based on the growth of small and round- or oval-shaped cells in the lung, which are either cancerous or non-cancerous. Accurate segmentation of these nodules is crucial for early detection and diagnosis of lung cancer. However, lung nodules can have various shapes, sizes, and densities, making their accurate segmentation a difficult task. Moreover, they can be easily confused with other structures in the lung, including blood vessels and airways, further complicating the segmentation process. To address this challenge, this paper proposes a novel multi-crop convolutional neural network (multi-crop CNN) model that utilizes different sized cropped regions of CT scan images for accurate segmentation of lung nodules. The model consists of three modules, namely the feature representation module, boundary refinement module, and segmentation module. The feature representation module captures features from the lung CT scan image using cropped regions of different sizes, while the boundary refinement module combines the boundary maps and feature maps to generate a final feature map for the segmentation process. The segmentation module produces a high-resolution segmentation map that shows improved accuracy in segmenting cancerous lung nodules. The proposed multi-crop CNN model is evaluated on two segmentation datasets namely LUNA 16 and LIDC-IDRI with an accuracy of 98.3% and 98.5%, respectively. The performances are measured in terms of accuracy, recall, precision, dice coefficient, specificity, AUC/ROC, Hausdorff distance, Jaccard index, and average Hausdorff. Overall, the proposed multi-crop CNN model demonstrates the potential to enhance the lung nodule segmentation accuracy, which could lead to earlier detection and diagnosis of lung cancer and ultimately reduce mortality rates associated with the disease.

Improving Unsupervised Object-Based Change Detection via Hierarchical Multi-Scale Binary Partition Tree Segmentation: A Case Study in the Yellow River Source Region

Change detection in remote sensing enables identifying alterations in surface characteristics over time, underpinning diverse applications. However, conventional pixel-based algorithms encounter constraints in terms of accuracy when applied to medium- and high-resolution remote sensing images. Although object-oriented methods offer a step forward, they frequently grapple with missing small objects or handling complex features effectively. To bridge these gaps, this paper proposes an unsupervised object-oriented change detection approach empowered by hierarchical multi-scale segmentation for generating binary ecosystem change maps. This approach meticulously segments images into optimal sizes and leverages multidimensional features to adapt the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) algorithm for GaoFen WFV data. We rigorously evaluated its performance in the Yellow River Source Region, a critical ecosystem conservation zone. The results unveil three key strengths: (1) the approach achieved excellent object-level change detection results, making it particularly suited for identifying changes in subtle features; (2) while simply increasing object features did not lead to a linear accuracy gain, optimized feature space construction effectively mitigated dimensionality issues; and (3) the scalability of our approach is underscored by its success in mapping the entire Yellow River Source Region, achieving an overall accuracy of 90.09% and F-score of 0.8844. Furthermore, our analysis reveals that from 2015 to 2022, changed ecosystems comprised approximately 1.42% of the total area, providing valuable insights into regional ecosystem dynamics.

The loading direction dramatically affects the mechanical properties of the mouse tibia.

Introduction: The in vivo tibial loading mouse model has been extensively used to evaluate bone adaptation in the tibia after mechanical loading treatment. However, there is a prevailing assumption that the load is applied axially to the tibia. The aim of this in silico study was to evaluate how much the apparent mechanical properties of the mouse tibia are affected by the loading direction, by using a validated micro-finite element (micro-FE) model of mice which have been ovariectomized and exposed to external mechanical loading over a two-week period. Methods: Longitudinal micro-computed tomography (micro-CT) images were taken of the tibiae of eleven ovariectomized mice at ages 18 and 20weeks. Six of the mice underwent a mechanical loading treatment at age 19weeks. Micro-FE models were generated, based on the segmented micro-CT images. Three models using unitary loads were linearly combined to simulate a range of loading directions, generated as a function of the angle from the inferior-superior axis (θ, 0°-30° range, 5° steps) and the angle from the anterior-posterior axis (ϕ, 0°: anterior axis, positive anticlockwise, 0°-355° range, 5° steps). The minimum principal strain was calculated and used to estimate the failure load, by linearly scaling the strain until 10% of the nodes reached the critical strain level of -14,420με. The apparent bone stiffness was calculated as the ratio between the axial applied force and the average displacement along the longitudinal direction, for the loaded nodes. Results: The results demonstrated a high sensitivity of the mouse tibia to the loading direction across all groups and time points. Higher failure loads were found for several loading directions (θ = 10°, ϕ 205°-210°) than for the nominal axial case (θ = 0°, ϕ = 0°), highlighting adaptation of the bone for loading directions far from the nominal axial one. Conclusion: These results suggest that in studies which use mouse tibia, the loading direction can significantly impact the failure load. Thus, the magnitude and direction of the applied load should be well controlled during the experiments.

Single-shot global localization via graph-theoretic correspondence matching

ABSTRACT This paper describes a method of single-shot global localization based on graph-theoretic matching of instances between a query and a prior map. The proposed framework employs correspondence matching based on the maximum clique problem (MCP). The framework is potentially applicable to other map and/or query modalities thanks to the graph-based abstraction of the problem, while many existing global localization methods rely on a query and the dataset in the same modality. We implement it with a semantically labeled 3D point cloud map, and a semantic segmentation image as a query. Leveraging the graph-theoretic framework, the proposed method realizes global localization exploiting only the map and the query. The method shows promising results on multiple large-scale simulated maps of urban scenes.

Influence of exposure protocol, voxel size, and artifact removal algorithm on the trueness of segmentation utilizing an artificial-intelligence-based system.

To evaluate the effects of exposure protocol, voxel sizes, and artifact removal algorithms on the trueness of segmentation in various mandible regions using an artificial intelligence (AI)-based system. Eleven dry human mandibles were scanned using a cone beam computed tomography (CBCT) scanner under differing exposure protocols (standard and ultra-low), voxel sizes (0.15mm, 0.3mm, and 0.45mm), and with or without artifact removal algorithm. The resulting datasets were segmented using an AI-based system, exported as 3D models, and compared to reference files derived from a white-light laboratory scanner. Deviation measurement was performed using a computer-aided design (CAD) program and recorded as root mean square (RMS). The RMS values were used as a representation of the trueness of the AI-segmented 3D models. A 4-way ANOVA was used to assess the impact of voxel size, exposure protocol, artifact removal algorithm, and location on RMS values (α = 0.05). Significant effects were found with voxel size (p < 0.001) and location (p < 0.001), but not with exposure protocol (p = 0.259) or artifact removal algorithm (p = 0.752). Standard exposure groups had significantly lower RMS values than the ultra-low exposure groups in the mandible body with 0.3mm (p = 0.014) or 0.45mm (p < 0.001) voxel sizes, the symphysis with a 0.45mm voxel size (p = 0.011), and the whole mandible with a 0.45mm voxel size (p = 0.001). Exposure protocol did not affect RMS values at teeth and alveolar bone (p = 0.544), mandible angles (p = 0.380), condyles (p = 0.114), and coronoids (p = 0.806) locations. This study informs optimal exposure protocol and voxel size choices in CBCT imaging for true AI-based automatic segmentation with minimal radiation. The artifact removal algorithm did not influence the trueness of AI segmentation. When using an ultra-low exposure protocol to minimize patient radiation exposure in AI segmentations, a voxel size of 0.15mm is recommended, while a voxel size of 0.45mm should be avoided.

Artificial intelligence and big data integration in anterior segment imaging for glaucoma.

The integration of artificial intelligence (AI) and big data in anterior segment (AS) imaging represents a transformative approach to glaucoma diagnosis and management. This article explores various AS imaging techniques, such as AS optical coherence tomography, ultrasound biomicroscopy, and goniophotography, highlighting their roles in identifying angle-closure diseases. The review focuses on advancements in AI, including machine learning and deep learning, which enhance image analysis and automate complex processes in glaucoma care, and provides current evidence on the performance and clinical applications of these technologies. In addition, the article discusses the integration of big data, detailing its potential to revolutionize medical imaging by enabling comprehensive data analysis, fostering enhanced clinical decision-making, and facilitating personalized treatment strategies. In this article, we address the challenges of standardizing and integrating diverse data sets and suggest that future collaborations and technological advancements could substantially improve the management and research of glaucoma. This synthesis of current evidence and new technologies emphasizes their clinical relevance, offering insights into their potential to change traditional approaches to glaucoma evaluation and care.

Deep learning-based segmentation of breast masses using convolutional neural networks

Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.6115 (malignant) and 0.6119 (benign) for U-Net, respectively. The results show that the DeepLabV3+ has significantly better segmentation performance and outperforms the U-Net network.

A Hybrid of Fuzzy C-Means for the segmentation in CT scan and X-ray images for screening the COVID-19 patients

In this paper, using CT scan and X-ray images, we present a hybrid approach, based on combining fuzzy C-means with k-means clustering, to evaluate and determine pneumonia infection caused by the coronavirus disease (COVID-19). To achieve this objective, we introduce a hybrid method that combines fuzzy C-means clustering with K-means clustering. This hybrid approach is designed to effectively segment object boundaries within medical images, enabling the precise identification of pneumonia-related features. In addition to our hybrid method, we compare its performance with two other segmentation approaches: the Expectation Maximization (EM) algorithm and 2D Entropy segmentation. Which, the method we propose uses a comparison between the performances of the based on a database of medical imaging test. Experimental results showed that the proposed approach outperforms, it was found that the hybrid fuzzy C-means algorithm segmentation images methods give better performance in terms of accuracy, precision, and F-measure, which is effective in boundaries segmentation. Comparative results of the accuracy and image quality index demonstrate the robustness of AI. It also helps to improve work efficiency with accurate analysis of COVID-19 infection on CT scan and X-rays. In addition, the approach helps radiologists make clinical decisions for diagnosis, follow-up, and prognosis.

Comparative Analysis of Machine-Learning Algorithms for Accurate Diagnosis of Lung Diseases Using Chest X-ray Images: A Study on Balanced and Unbalanced Data on Segmented and Unsegmented Images.

The study focused on the accurate diagnosis of lung diseases, considering the high number of lung disease-related deaths in the world.Chest x-rayimages were used as they are a cost-effective and widely available diagnostic tool. Eight different machine learning algorithms were evaluated: Logistic Regression, Naive Bayes, k-Nearest Neighbors (kNN), Decision Tree, Random Forest, Support Vector Machine (SVM), Ridge, and Least Absolute Shrinkage and Selection Operator (LASSO). The study evaluated balanced and imbalanced datasets and looked at both segmented and unsegmented chest x-ray images. COVID-19, pneumonia, normal, and others were the four classes that were used in the investigation. Prior to attribute reduction, Decision Tree and Random Forest performed well on the balanced dataset, obtaining 74% test accuracy and 92% training accuracy. SVM functioned well as well, obtaining a 74% test accuracy. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two attribute reduction approaches that were applied. Decision Trees and Random Forests were able to attain the maximum training accuracy of 92%, while SVM was able to retain a test accuracy of 74% after attribute reduction. The findings also imply that some algorithms' performance may be enhanced by attribute reduction methods like PCA and LDA. For imbalanced data, Random Forest and SVM perform the best in terms of balanced accuracy of 80%. However, further research and experimentation may be needed to optimize the models and explore other potential algorithms or techniques.

Toward improved image‐based root phenotyping: Handling temporal and cross‐site domain shifts in crop root segmentation models

AbstractCrop root segmentation models developed through deep learning have increased the throughput of in situ crop phenotyping studies. However, models trained to identify roots in one image dataset may not accurately identify roots in another dataset, especially when the new dataset contains known differences, called domain shifts. The objective of this study was to quantify how model performance changes when models are used to segment image datasets that contain domain shifts and evaluate approaches to reduce error associated with domain shifts. We collected maize root images at two growth stages (V7 and R2) in a field experiment and manually segmented images to measure total root length (TRL). We developed five segmentation models and evaluated each model's ability to handle a temporal (growth‐stage) domain shift. For the V7 growth stage, a growth‐stage‐specific model trained only on images captured at the V7 growth stage was best suited for measuring TRL. At the R2 growth stage, combining images from both growth stages into a single dataset to train a model resulted in the most accurate TRL measurements. We applied two of the field models to images from a greenhouse experiment to evaluate how model performance changed when exposed to a cross‐site domain shift. Field models were less accurate than models trained only on the greenhouse images even when crop growth stage was identical. Although models may perform well for one experiment, model error increases when applied to images from different experiments even when crop species, growth stage, and soil type are similar.

Brain Tumor Detection and Classification Using IFF-FLICM Segmentation and Optimized ELM Model

Brain cancer deaths are significantly increased in all categories of aged persons due to the abnormal growth of brain tumor tissues in the brain. The death rate can be controlled by accurate early stage brain tumor diagnosis. The detection and classification of brain tumors play a crucial role in early diagnosis and treatment planning. Brain tumor detection and classification have become challenging and time-consuming for domain-specific radiologists and pathologists in medical image analysis. So, automatic detection and classification are essential to reduce the time of diagnosis. In recent years, machine learning classifiers have played an essential role in automatically classifying brain tumors. In this research, an approach based on an improved fuzzy factor fuzzy local information C means (IFF-FLICM) segmentation and hybrid modified harmony search and sine cosine algorithm (MHS-SCA) optimized extreme learning machine (ELM) is proposed for brain tumor detection and classification. The IFF-FLICM algorithm is utilized to accurately segment the brain’s magnetic resonance (MR) images to identify the tumor regions. The Mexican hat wavelet transform is employed for feature extraction from the segmented images. The extracted features from the segmented regions are fed into the MHS-SCA-ELM classifier for classification. The MHS-SCA is proposed to optimize the weights of the ELM model to improve the classification performance. Five distinct multimodal and unimodal benchmark functions are considered for optimization to demonstrate the robustness of the proposed MHS-SCA optimization technique. The image Dataset-255 is considered for this study. The quality measures such as SSIM and PSNR are considered for segmentation. The proposed IFF-FLICM segmentation achieved a peak signal-to-noise ratio (PSNR) of 37.24 dB and a structural similarity index (SSIM) of 0.9823. The proposed MHS-SCA-based ELM model achieved a sensitivity, specificity, and accuracy of 98.78%, 99.23%, and 99.12%. The classification performance results of the proposed MHS-SCA-ELM model are compared with MHS-ELM, SCA-ELM, and PSO-ELM models, and the comparison results are presented.

Stromal area differences with epithelial-mesenchymal transition gene changes in conjunctival and orbital mucosa-associated lymphoid tissue lymphoma.

To examine the molecular biological differences between conjunctival mucosa-associated lymphoid tissue (MALT) lymphoma and orbital MALT lymphoma in ocular adnexa lymphoma. Observational case series. A total of 129 consecutive, randomized cases of ocular adnexa MALT lymphoma diagnosed histopathologically between 2008 and 2020.Total RNA was extracted from formalin-fixed paraffin-embedded tissue from ocular adnexa MALT lymphoma, and RNA-sequencing was performed. Orbital MALT lymphoma gene expression was compared with that of conjunctival MALT lymphoma. Gene set (GS) analysis detecting for gene set cluster was performed in RNA-sequence. Related proteins were further examined by immunohistochemical staining. In addition, artificial segmentation image used to count stromal area in HE images. GS analysis showed differences in expression in 29 GS types in primary orbital MALT lymphoma (N=5,5, FDR q-value <0.25). The GS with the greatest difference in expression was the GS of epithelial-mesenchymal transition (EMT). Based on this GS change, immunohistochemical staining was added using E-cadherin as an epithelial marker and vimentin as a mesenchymal marker for EMT. There was significant staining of vimentin in orbital lymphoma (P<0.01, N=129) and of E-cadherin in conjunctival lesions (P=0.023, N=129). Vimentin staining correlated with Ann Arbor staging (1 versus >1) independent of age and sex on multivariate analysis (P=0.004). Stroma area in tumor were significant difference(P<0.01). GS changes including EMT and stromal area in tumor were used to demonstrate the molecular biological differences between conjunctival MALT lymphoma and orbital MALT lymphoma in ocular adnexa lymphomas.

Shrimp classification for white spot syndrome detection through enhanced gated recurrent unit-based wild geese migration optimization algorithm.

The major dangerous viral infection for cultivated shrimps is WSSV. The virus is extremely dangerous, spreads swiftly, and may result in up to 100% mortality in 3-10days. The vast wrapped double stranded DNA virus known as WSSV describes a member of the Nimaviridae viral family's species Whispovirus. It impacts a variety of crustacean hosts but predominantly marine shrimp species that are raised for commercial purposes. The entire age groups are affected by the virus, which leads to widespread mortality. Mesodermal and ectodermal tissues, like the lymph nodes, gills, and cuticular epithelium, represents the centres of infection. Complete genome sequencing related to the WSSV strains from Thailand, China, and Taiwan has identified minute genetic variations amongst them. There exist conflicting findings on the causes of WSSV pathogenicity, which involve variations in the size associated with the genome, the count of tandem repeats, and the availability or lack of certain proteins. Hence, this paper plans to perform the shrimp classification for the WSSV on the basis of novel deep learning methodology. Initially, the data is gathered from the farms as well as internet sources. Next, the pre-processing of the gathered shrimp images is accomplished using the LBP technique. These pre-processed images undergo the segmentation process utilizing the TGVFCMS approach. The extraction of the features from these segmented images is performed by the PLDA technique. In the final step, the classification of the shrimp into healthy shrimp and WSSV affected shrimp is done by the EGRU, in which the parameter tuning is accomplished by the wild GMO algorithm with the consideration of accuracy maximization as the major objective function. Performance indicators for accuracy have been compared with those of various conventional methods, and the results show that the methodology is capable of accurately identifying the shrimp WSSV illness.

The Challenge of Single-Photon Emission Computed Tomography Image Segmentation in the Internal Dosimetry of 177Lu Molecular Therapies.

The aim of this article is to review the single photon emission computed tomography (SPECT) segmentation methods used in patient-specific dosimetry of 177Lu molecular therapy. Notably, 177Lu-labelled radiopharmaceuticals are currently used in molecular therapy of metastatic neuroendocrine tumours (ligands for somatostatin receptors) and metastatic prostate adenocarcinomas (PSMA ligands). The proper segmentation of the organs at risk and tumours in targeted radionuclide therapy is an important part of the optimisation process of internal patient dosimetry in this kind of therapy. Because this is the first step in dosimetry assessments, on which further dose calculations are based, it is important to know the level of uncertainty that is associated with this part of the analysis. However, the robust quantification of SPECT images, which would ensure accurate dosimetry assessments, is very hard to achieve due to the intrinsic features of this device. In this article, papers on this topic were collected and reviewed to weigh up the advantages and disadvantages of the segmentation methods used in clinical practice. Degrading factors of SPECT images were also studied to assess their impact on the quantification of 177Lu therapy images. Our review of the recent literature gives an insight into this important topic. However, based on the PubMed and IEEE databases, only a few papers investigating segmentation methods in 177Lumolecular therapy were found. Although segmentation is an important step in internal dose calculations, this subject has been relatively lightly investigated for SPECT systems. This is mostly due to the inner features of SPECT. What is more, even when studies are conducted, they usually utilise the diagnostic radionuclide 99mTc and not a therapeutic one like 177Lu, which could be of concern regarding SPECT camera performance and its overall outcome on dosimetry.

Black hole algorithm along edge detector and circular hough transform based iris projection with biometric identification systems

The circular parameters between the pupil and the iris are found using current iris identification techniques but the accuracy creates an issue for the detection process during image processing. The procedure of extracting the iris region from an eye image using circular parameters can be improved via approximately too many approaches in literature but remain some portions under slightly unconstrained circumstances. In this study, we presented a Black Hole Algorithm (BHA) along the Canny edge detector and circular Hough transform-based optimization technique for circular parameter identification of iris segmentation. The iris boundary is discovered using the suggested segmentation approach and a computational model of the pixel value. The BHA looks for the central radius of the iris and pupil. The system uses MATLAB to test the CASIA-V3 database. The segmented images exhibit 98.71% accuracy. For all future access control applications, the segmentation-based BHA is effective at identifying the iris. The integration of the BHA with the Hough transforms and Canny edge detector is the main method by which the iris segmentation is accomplished. This novel technique improves the accuracy and effectiveness of iris segmentation, with potential uses in image analysis and biometric identification.

Using a generative adversarial network to generate synthetic MRI images for multi-class automatic segmentation of brain tumors.

Challenging tasks such as lesion segmentation, classification, and analysis for the assessment of disease progression can be automatically achieved using deep learning (DL)-based algorithms. DL techniques such as 3D convolutional neural networks are trained using heterogeneous volumetric imaging data such as MRI, CT, and PET, among others. However, DL-based methods are usually only applicable in the presence of the desired number of inputs. In the absence of one of the required inputs, the method cannot be used. By implementing a generative adversarial network (GAN), we aim to apply multi-label automatic segmentation of brain tumors to synthetic images when not all inputs are present. The implemented GAN is based on the Pix2Pix architecture and has been extended to a 3D framework named Pix2PixNIfTI. For this study, 1,251 patients of the BraTS2021 dataset comprising sequences such as T1w, T2w, T1CE, and FLAIR images equipped with respective multi-label segmentation were used. This dataset was used for training the Pix2PixNIfTI model for generating synthetic MRI images of all the image contrasts. The segmentation model, namely DeepMedic, was trained in a five-fold cross-validation manner for brain tumor segmentation and tested using the original inputs as the gold standard. The inference of trained segmentation models was later applied to synthetic images replacing missing input, in combination with other original images to identify the efficacy of generated images in achieving multi-class segmentation. For the multi-class segmentation using synthetic data or lesser inputs, the dice scores were observed to be significantly reduced but remained similar in range for the whole tumor when compared with evaluated original image segmentation (e.g. mean dice of synthetic T2w prediction NC, 0.74 ± 0.30; ED, 0.81 ± 0.15; CET, 0.84 ± 0.21; WT, 0.90 ± 0.08). A standard paired t-tests with multiple comparison correction were performed to assess the difference between all regions (p < 0.05). The study concludes that the use of Pix2PixNIfTI allows us to segment brain tumors when one input image is missing.