International Journal of Imaging Systems and Technology最新文献

{"title":"Automated, Reproducible, and Reconfigurable Human Head Phantom for Experimental Testing of Microwave Systems for Stroke Classification","authors":"Tomas Pokorny,&nbsp;Tomas Drizdal,&nbsp;Marek Novak,&nbsp;Jan Vrba","doi":"10.1002/ima.23200","DOIUrl":"https://doi.org/10.1002/ima.23200","url":null,"abstract":"<p>Microwave systems for prehospital stroke classification are currently being developed. In the future, these systems should enable rapid recognition of the type of stroke, shorten the time to start treatment, and thus significantly improve the prognosis of patients. In this study, we realized a realistic and reconfigurable 3D human head phantom for the development, testing, and validation of these newly developed diagnostic methods. The phantom enables automated and reproducible measurements for different positions of the stroke model. The stroke model itself is also interchangeable, so measurements can be made for different types, sizes, and shapes of strokes. Furthermore, an extensive series of measurements was performed at a frequency of 1 GHz, and an SVM classification algorithm was deployed, which successfully identified ischemic stroke in 80% of the corresponding measured data. If similar classification accuracy could be achieved in patients, it would lead to a dramatic reduction in the consequences of strokes.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Segmentation Evaluation With the Dice Index: Methodological Issues","authors":"Mohamed L. Seghier","doi":"10.1002/ima.23203","DOIUrl":"https://doi.org/10.1002/ima.23203","url":null,"abstract":"&lt;p&gt;In this editorial, I call for more clarity and transparency when calculating and reporting the Dice index to evaluate the performance of biomedical image segmentation methods. Despite many existing guidelines for best practices for assessing and reporting the performance of automated methods [&lt;span&gt;1, 2&lt;/span&gt;], there is still a lack of clarity on why and how performance metrics were selected and assessed. I have seen articles where, for instance, Dice indices (i) were erroneously reported as smaller than intersection-over-union values, (ii) oddly increased from moderate to excellent values after including images with no actual positive instances, (iii) were drastically affected by image cropping or zero-padding, (iv) did not make sense in the light of the reported precision and sensitivity values, (v) showed opposite trends to F1 scores, (vi) were wrongly interpreted as accuracy measures, (vii) used as a measure of detection success rather than segmentation success, (viii) were used to rank methods that varied considerably in terms of the number of false positives and false negatives, (ix) were averaged across segmented structures of interest with highly variable sizes, and (x) were directly compared to other Dice indices from previous studies despite being tested on completely different datasets. It is important to remind our authors what one can (or cannot) do with the Dice index for biomedical image segmentation.&lt;/p&gt;&lt;p&gt;As the Dice index is one of the preferred metrics to assess segmentation performance and is widely used in many challenges and benchmarks to rank models [&lt;span&gt;3&lt;/span&gt;], it is paramount that authors calculate it correctly and report it clearly and transparently. Below, I discuss conceptual and methodological issues about the Dice index before providing a list of 10 simple rules for optimal and transparent reporting of the Dice index. By improving transparency and clarity, I believe readers will draw the right conclusions about methods evaluation, which will ultimately help improve interpretability and replicability in biomedical data processing.&lt;/p&gt;&lt;p&gt;The discussion below applies to any image segmentation problem, imaging modality, 2D (slices) or 3D (volumes) inputs, and segmentation tasks (e.g., segmenting abnormalities or typical structures and organs). Examples will be taken from the automated segmentation of stroke lesions in brain scans.&lt;/p&gt;&lt;p&gt;Put another way, the Dice index codes how the positives declared by an automated method match the actual positives of the ground truth. We have &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mtext&gt;Dice&lt;/mtext&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;A&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;A&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal Connectivity-Guided Glioma Segmentation From Magnetic Resonance Images via Cascaded 3D Residual U-Net","authors":"Xiaoyan Sun,&nbsp;Chuhan Hu,&nbsp;Wenhan He,&nbsp;Zhenming Yuan,&nbsp;Jian Zhang","doi":"10.1002/ima.23206","DOIUrl":"https://doi.org/10.1002/ima.23206","url":null,"abstract":"<div>\u0000 \u0000 <p>Glioma is a type of brain tumor with a high mortality rate. Magnetic resonance imaging (MRI) is commonly used for examination, and the accurate segmentation of tumor regions from MR images is essential to computer-aided diagnosis. However, due to the intrinsic heterogeneity of brain glioma, precise segmentation is very challenging, especially for tumor subregions. This article proposed a two-stage cascaded method for brain tumor segmentation that considers the hierarchical structure of the target tumor subregions. The first stage aims to identify the whole tumor (WT) from the background area; and the second stage aims to achieve fine-grained segmentation of the subregions, including enhanced tumor (ET) region and tumor core (TC) region. Both stages apply a deep neural network structure combining modified 3D U-Net with a residual connection scheme to tumor region and subregion segmentation. Moreover, in the training phase, the 3D masks generation of subregions with potential incomplete connectivity are guided by the completely connected regions. Experiments were performed to evaluate the performance of the methods on both area and boundary accuracy. The average dice score of the WT, TC, and ET regions on BraTS 2020 dataset is 0.9168, 0.0.8992, 0.8489, and the Hausdorff distance is 6.021, 9.203, 12.171, respectively. The proposed method outperforms current works, especially in segmenting fine-grained tumor subregions.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recognition of Diabetic Retinopathy Grades Based on Data Augmentation and Attention Mechanisms","authors":"Xueri Li,&nbsp;Li Wen,&nbsp;Fanyu Du,&nbsp;Lei Yang,&nbsp;Jianfang Wu","doi":"10.1002/ima.23201","DOIUrl":"https://doi.org/10.1002/ima.23201","url":null,"abstract":"<div>\u0000 \u0000 <p>Diabetic retinopathy is a complication of diabetes and one of the leading causes of vision loss. Early detection and treatment are essential to prevent vision loss. Deep learning has been making great strides in the field of medical image processing and can be used as an aid for medical practitioners. However, unbalanced datasets, sparse focal areas, small differences between adjacent disease grades, and varied manifestations of the same grade disease challenge deep learning model training. Generalization performance and robustness are inadequate. To address the problem of unbalanced sample numbers between classes in the dataset, this work proposes using VQ-VAE for reconstructing affine transformed images to enrich and balance the dataset. Test results show the model's average reconstruction error is 0.0001, and the mean structural similarity between reconstructed and original images is 0.967. This proves reconstructed images differ from originals yet belong to the same category, expanding and diversifying the dataset. Addressing the issues of focal area sparsity and disease grade disparity, this work utilizes ResNeXt50 as the backbone network and constructs diverse attention networks by modifying the network structure and embedding different attention modules. Experiments demonstrate that the convolutional attention network outperforms ResNeXt50 in terms of Precision, Sensitivity, Specificity, F1 Score, Quadratic Weighted Kappa Coefficient, Accuracy, and robustness against Salt and Pepper noise, Gaussian noise, and gradient perturbation. Finally, the heat maps of each model recognizing the fundus image were plotted using the Grad-CAM method. The heat maps show that the attentional network is more effective than the non-attentional network ResNeXt50 at attending to the fundus image.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FDT-Net: Frequency-Aware Dual-Branch Transformer-Based Optic Cup and Optic Disk Segmentation With Parallel Contour Information Mining and Uncertainty-Guided Refinement","authors":"Jierui Gan,&nbsp;Hongqing Zhu,&nbsp;Tianwei Qian,&nbsp;Jiahao Liu,&nbsp;Ning Chen,&nbsp;Ziying Wang","doi":"10.1002/ima.23199","DOIUrl":"https://doi.org/10.1002/ima.23199","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate segmentation of the optic cup and disc in fundus images is crucial for the prevention and diagnosis of glaucoma. However, challenges arise due to factors such as blood vessels, and mainstream networks often demonstrate limited capacity in extracting contour information. In this paper, we propose a segmentation framework named FDT-Net, which is based on a frequency-aware dual-branch Transformer (FDBT) architecture with parallel contour information mining and uncertainty-guided refinement. Specifically, we design a FDBT that operates in the frequency domain. This module leverages the inherent contextual awareness of Transformers and utilizes Discrete Cosine Transform (DCT) transformation to mitigate the impact of certain interference factors on segmentation. The FDBT comprises global and local branches that independently extract global and local information, thereby enhancing segmentation results. Moreover, to further mine additional contour information, this study develops the parallel contour information mining (PCIM) module to operate in parallel. These modules effectively capture more details from the edges of the optic cup and disc while avoiding mutual interference, thus optimizing segmentation performance in contour regions. Furthermore, we propose an uncertainty-guided refinement (UGR) module, which generates and quantifies uncertainty mass and leverages it to enhance model performance based on subjective logic theory. The experimental results on two publicly available datasets demonstrate the superior performance and competitive advantages of our proposed FDT-Net. The code for this project is available at https://github.com/Rookie49144/FDT-Net.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"M-Net: A Skin Cancer Classification With Improved Convolutional Neural Network Based on the Enhanced Gray Wolf Optimization Algorithm","authors":"Zhinan Xu,&nbsp;Xiaoxia Zhang,&nbsp;Luzhou Liu","doi":"10.1002/ima.23202","DOIUrl":"https://doi.org/10.1002/ima.23202","url":null,"abstract":"<div>\u0000 \u0000 <p>Skin cancer is a common malignant tumor causing tens of thousands of deaths each year, making early detection essential for better treatment outcomes. However, the similar visual characteristics of skin lesions make it challenging to accurately differentiate between lesion types. With advancements in deep learning, researchers have increasingly turned to convolutional neural networks for skin cancer detection and classification. In this article, an improved skin cancer classification model M-Net is proposed, and the enhanced gray wolf optimization algorithm is combined to improve the classification performance. The gray wolf optimization algorithm guides the wolf pack to prey through a multileader structure and gradually converges through the encirclement and pursuit mechanism, so as to perform a more detailed search in the later stage. To further improve the performance of the gray wolf optimization, this study introduces the simulated annealing algorithm to avoid falling into the local optimal state and expands the search range by improving the search mechanism, thus enhancing the global optimization ability of the algorithm. The M-Net model significantly improves the accuracy of classification by extracting features of skin lesions and optimizing parameters with the enhanced gray wolf optimization algorithm. The experimental results based on the ISIC 2018 dataset show that compared with the baseline model, the feature extraction network of the model has achieved a significant improvement in accuracy. The classification performance of M-Net is excellent in multiple indicators, with accuracy, precision, recall, and F1 score reaching 0.891, 0.857, 0.895, and 0.872, respectively. In addition, the modular design of M-Net enables it to flexibly adjust feature extraction and classification modules to adapt to different classification tasks, showing great scalability and applicability. In general, the model proposed in this article performs well in the classification of skin lesions, has broad clinical application prospects, and provides strong support for promoting the diagnosis of skin diseases.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Image Fusion for Multiple Diseases Features Enhancement","authors":"Sajid Ullah Khan,&nbsp;Meshal Alharbi,&nbsp;Sajid Shah,&nbsp;Mohammed ELAffendi","doi":"10.1002/ima.23197","DOIUrl":"https://doi.org/10.1002/ima.23197","url":null,"abstract":"<div>\u0000 \u0000 <p>Throughout the past 20 years, medical imaging has found extensive application in clinical diagnosis. Doctors may find it difficult to diagnose diseases using only one imaging modality. The main objective of multimodal medical image fusion (MMIF) is to improve both the accuracy and quality of clinical assessments by extracting structural and spectral information from source images. This study proposes a novel MMIF method to assist doctors and postoperations such as image segmentation, classification, and further surgical procedures. Initially, the intensity-hue-saturation (IHS) model is utilized to decompose the positron emission tomography (PET)/single photon emission computed tomography (SPECT) image, followed by a hue-angle mapping method to discriminate high- and low-activity regions in the PET images. Then, a proposed structure feature adjustment (SFA) mechanism is used as a fusion strategy for high- and low-activity regions to obtain structural and anatomical details with minimum color distortion. In the second step, a new multi-discriminator generative adversarial network (MDcGAN) approach is proposed for obtaining the final fused image. The qualitative and quantitative results demonstrate that the proposed method is superior to existing MMIF methods in preserving the structural, anatomical, and functional details of the PET/SPECT images. Through our assessment, involving visual analysis and subsequent verification using statistical metrics, it becomes evident that color changes contribute substantial visual information to the fusion of PET and MR images. The quantitative outcomes demonstrate that, in the majority of cases, the proposed algorithm consistently outperformed other methods. Yet, in a few instances, it achieved the second-highest results. The validity of the proposed method was confirmed using diverse modalities, encompassing a total of 1012 image pairs.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization and Application Analysis of Phase Correction Method Based on Improved Image Registration in Ultrasonic Image Detection","authors":"Nannan Lu,&nbsp;Hongyan Shu","doi":"10.1002/ima.23185","DOIUrl":"https://doi.org/10.1002/ima.23185","url":null,"abstract":"<p>In order to prevent and detect a wide range of disorders, including those of the brain, thoracic, digestive, urogenital, and cardiovascular systems, ultrasound technology is essential for assessing physiological data and tissue morphology. Its capacity to deliver real-time, high-frequency scans makes it a handy and non-invasive diagnostic tool. However, issues like patient movements and probe jitter from human error can provide a large amount of interference, resulting in inaccurate test findings. Techniques for image registration can assist in locating and eliminating unwanted interference while maintaining crucial data. Even though there has been research on improving these techniques in Matlab, there are no specialized systems for interference removal, and the procedure is still time-consuming, particularly when working with huge quantities of ultrasound images. The phase correlation technique, which converts images into the frequency domain and makes noise suppression easier, is one of the most efficient algorithms now in use since it can tolerate noise with resilience. Nevertheless, little research has been done on using this technique to identify displacement in blood vessel wall ultrasound images. To address these gaps, this work presents an image registration system that uses the phase correlation algorithm. The system provides rotation, zoom registration, picture translation, and displacement detection of the vessel wall in addition to interference removal. Furthermore, batch processing is included to increase the effectiveness of registering multiple ultrasound pictures. Through efficient interference management and streamlined registration, this method offers a workable way to improve the precision and efficacy of ultrasonic diagnostics.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142429964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Pyramid Network Based Spatial Attention and Cross-Level Semantic Similarity for Diseases Segmentation From Capsule Endoscopy Images","authors":"Said Charfi,&nbsp;Mohamed EL Ansari,&nbsp;Lahcen Koutti,&nbsp;Ilyas ELjaafari,&nbsp;Ayoub ELLahyani","doi":"10.1002/ima.23194","DOIUrl":"https://doi.org/10.1002/ima.23194","url":null,"abstract":"<div>\u0000 \u0000 <p>As an emerging technology that uses a pill-sized camera to visualize images of the digestive tract. Wireless capsule endoscopy (WCE) presents several advantages, since it is far less invasive, does not need sedation and has less possible complications compared to standard endoscopy. Hence, it might be exploited as alternative to the standard procedure. WCE is used to diagnosis a variety of gastro-intestinal diseases such as polyps, ulcers, crohns disease, and hemorrhages. Nevertheless, WCE videos produced after a test may consist of thousands of frames per patient that must be viewed by medical specialists, besides, the capsule free mobility and technological limits cause production of a low quality images. Hence, development of an automatic tool based on artificial intelligence might be very helpful. Moreover, most state-of-the-art works aim at images classification (normal/abnormal) while ignoring diseases segmentation. Therefore, in this work a novel method based on Feature Pyramid Network model is presented. This approach aims at diseases segmentation from WCE images. In this model, modules to optimize and combine features were employed. Specifically, semantic and spatial features were mutually compensated by spatial attention and cross-level global feature fusion modules. The proposed method testing F1-score and mean intersection over union are 94.149% and 89.414%, respectively, in the MICCAI 2017 dataset. In the KID Atlas dataset, the method achieved a testing F1-score and mean intersection over union of 94.557% and 90.416%, respectively. Through the performance analysis, the mean intersection over union in the MICCAI 2017 dataset is 20.414%, 18.484%, 11.444%, 8.794% more than existing approaches. Moreover, the proposed scheme surpassed the methods used for comparison by 29.986% and 9.416% in terms of mean intersection over union in KID Atlas dataset. These results indicate that the proposed approach is promising in diseases segmentation from WCE images.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142429356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multispectral Blood Smear Background Images Reconstruction for Malaria Unstained Images Normalization","authors":"Solange Doumun OULAI,&nbsp;Sophie Dabo-Niang,&nbsp;Jérémie Zoueu","doi":"10.1002/ima.23182","DOIUrl":"https://doi.org/10.1002/ima.23182","url":null,"abstract":"<p>Multispectral and multimodal unstained blood smear images are obtained and evaluated to offer computer-assisted automated diagnostic evidence for malaria. However, these images suffer from uneven lighting, contrast variability, and local luminosity due to the acquisition system. This limitation significantly impacts the diagnostic process and its overall outcomes. To overcome this limitation, it is crucial to perform normalization on the acquired multispectral images as a preprocessing step for malaria parasite detection. In this study, we propose a novel method for achieving this normalization, aiming to improve the accuracy and reliability of the diagnostic process. This method is based on estimating the Bright reference image, which captures the luminosity, and the contrast variability function from the background region of the image. This is achieved through two distinct resampling methodologies, namely Gaussian random field simulation by variogram analysis and Bootstrap resampling. A method for handling the intensity saturation issue of certain pixels is also proposed, which involves outlier imputation. Both of these proposed approaches for image normalization are demonstrated to outperform existing methods for multispectral and multimodal unstained blood smear images, as measured by the Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), Zero mean Sum of Absolute Differences (ZSAD), Peak Signal to Noise Ratio (PSNR), and Absolute Mean Brightness Error (AMBE). These methods not only improve the image contrast but also preserve its spectral footprint and natural appearance more accurately. The normalization technique employing Bootstrap resampling significantly reduces the acquisition time for multimodal and multispectral images by 66%. Moreover, the processing time for Bootstrap resampling is less than 4% of the processing time required for Gaussian random field simulation.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142429469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}