Journal of Imaging最新文献_第5页

SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting.

IF 2.7

Journal of Imaging Pub Date : 2025-02-10 DOI: 10.3390/jimaging11020052

Ahmed Zgaren, Wassim Bouachir, Nizar Bouguila

{"title":"SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting.","authors":"Ahmed Zgaren, Wassim Bouachir, Nizar Bouguila","doi":"10.3390/jimaging11020052","DOIUrl":"10.3390/jimaging11020052","url":null,"abstract":"Zero-shot counting is a subcategory of Generic Visual Object Counting, which aims to count objects from an arbitrary class in a given image. While few-shot counting relies on delivering exemplars to the model to count similar class objects, zero-shot counting automates the operation for faster processing. This paper proposes a fully automated zero-shot method outperforming both zero-shot and few-shot methods. By exploiting feature maps from a pre-trained detection-based backbone, we introduce a new Visual Embedding Module designed to generate semantic embeddings within object contextual information. These embeddings are then fed to a Self-Attention Matching Module to generate an encoded representation for the head counter. Our proposed method has outperformed recent zero-shot approaches, achieving the best Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) results of 8.89 and 35.83, respectively, on the FSC147 dataset. Additionally, our method demonstrates competitive performance compared to few-shot methods, advancing the capabilities of visual object counting in various industrial applications such as tree counting, wildlife animal counting, and medical applications like blood cell counting.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Differentiation of Atypical Lipomatous Tumors from Lipomas: Our Experience with Visual Analysis of Conventional Magnetic Resonance Imaging. 非典型脂肪瘤与脂肪瘤的鉴别：我们对传统磁共振成像进行视觉分析的经验。

IF 2.7

Journal of Imaging Pub Date : 2025-02-08 DOI: 10.3390/jimaging11020047

Luz Maria Moran, Chao Yuan Li Cai, Alberto Ramirez, Ana Royuela

{"title":"Differentiation of Atypical Lipomatous Tumors from Lipomas: Our Experience with Visual Analysis of Conventional Magnetic Resonance Imaging.","authors":"Luz Maria Moran, Chao Yuan Li Cai, Alberto Ramirez, Ana Royuela","doi":"10.3390/jimaging11020047","DOIUrl":"10.3390/jimaging11020047","url":null,"abstract":"Differentiating atypical lipomatous tumors (ALTs) from lipomas using imaging techniques is a challenge, and the biopsy with immunohistochemical determination of murine double minute 2 (MDM2) oncogene is the gold standard. We are looking for a management algorithm with the visual analysis of magnetic resonance images in these two fatty soft tissue tumors that allow us to avoid some biopsies. Two radiologists, blinded to the final diagnosis, independently assessed various features on conventional magnetic resonance imaging (MRI), in 79 patients with pathologically confirmed fatty tumors as either lipoma (MDM2 negative) or ALT (MDM2 positive). Results: The interobserver agreement for the most MRI features was moderate and the musculoskeletal radiologist accuracy for final diagnosis was 90% sensitivity and 66% specificity. Tumors with homogeneous fat signals and a maximum size < 8 cm were always lipomas (p < 0.001), and the tumors with septa thickness ≥ 2 mm, or more than one non-fat nodule, and a maximum size ≥ 12.8 cm were typically ALTs. While those tumors with septa < 2 mm or one non-fat nodule, independently of maximum size, the diagnosis of lipoma versus ALT is uncertain and a biopsy is required.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing U-Net Segmentation Accuracy Through Comprehensive Data Preprocessing.

IF 2.7

Journal of Imaging Pub Date : 2025-02-08 DOI: 10.3390/jimaging11020050

Talshyn Sarsembayeva, Madina Mansurova, Assel Abdildayeva, Stepan Serebryakov

引用次数: 0

Resampling Point Clouds Using Series of Local Triangulations.

IF 2.7

Journal of Imaging Pub Date : 2025-02-08 DOI: 10.3390/jimaging11020049

Vijai Kumar Suriyababu, Cornelis Vuik, Matthias Möller

引用次数: 0

Shaping the Optimal Timing for Treatment of Isolated Asymptomatic Severe Aortic Stenosis with Preserved Left Ventricular Ejection Fraction: The Role of Non-Invasive Diagnostics Focused on Strain Echocardiography and Future Perspectives.

IF 2.7

Journal of Imaging Pub Date : 2025-02-08 DOI: 10.3390/jimaging11020048

Luca Dell'Angela, Gian Luigi Nicolosi

{"title":"Shaping the Optimal Timing for Treatment of Isolated Asymptomatic Severe Aortic Stenosis with Preserved Left Ventricular Ejection Fraction: The Role of Non-Invasive Diagnostics Focused on Strain Echocardiography and Future Perspectives.","authors":"Luca Dell'Angela, Gian Luigi Nicolosi","doi":"10.3390/jimaging11020048","DOIUrl":"10.3390/jimaging11020048","url":null,"abstract":"The optimal timing for treatment of patients with isolated asymptomatic severe aortic stenosis and preserved left ventricular ejection fraction is still controversial and research is ongoing. Once a diagnosis has been performed and other cardiac comorbidities (e.g., concomitant significant valvulopathies or infiltrative cardiomyopathies) have reasonably been excluded, a hot topic is adequate myocardial characterization, which aims to prevent both myocardial dysfunction and subsequent adverse myocardial remodeling, and can potentially compromise the post-treatment outcomes. Another crucial subject of debate is the assessment of the real \"preserved\" left ventricular ejection fraction cut-off value in the presence of isolated asymptomatic severe aortic stenosis, in order to optimize the timing of aortic valve replacement as well. The aim of the present critical narrative review is highlighting the current role of non-invasive diagnostics in such a setting, focusing on strain echocardiography, and citing the main complementary cardiac imaging techniques, as well as suggesting potential implementation strategies in routine clinical practice in view of future developments.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Machine Learning and Generative Intelligence in Book Cover Development. 在书籍封面开发中使用机器学习和生成智能。

IF 2.7

Journal of Imaging Pub Date : 2025-02-07 DOI: 10.3390/jimaging11020046

Nonna Kulishova, Daiva Sajek

{"title":"Using Machine Learning and Generative Intelligence in Book Cover Development.","authors":"Nonna Kulishova, Daiva Sajek","doi":"10.3390/jimaging11020046","DOIUrl":"10.3390/jimaging11020046","url":null,"abstract":"The rapid development of machine learning and artificial intelligence approaches is finding ever wider application in various areas of life. This paper considers the problem of improving editorial and publishing processes, namely self-publishing, when designing book covers using machine learning and generative artificial intelligence (GAI) methods. When choosing a book, readers often have certain expectations regarding the design of the publication, including the color of the cover. These expectations can be called color preferences, and they can depend on the genre of the book, its target audience, and even personal associations. Cultural context can also influence color choice, as certain colors can symbolize different emotions or moods in different cultures. Cluster analysis of book cover images of the same genre allows us to identify color preferences inherent in the genre, which is proposed to be used when designing new covers. The capabilities of generative services for creating and improving cover designs are also investigated. An improved flow chart for using GAI in creating book covers in the process of self-publishing is proposed, which includes new stages, namely exploring, conditioning, and evolving. At these stages, the designer creates prompts for GAI and examines how they and GAI's issuances correspond to the task. Conditioning allows for even more precise adjustment of prompts to features of each book, and the evolving stage also includes post-processing of results already received from GAI. Post-processing, in turn, can be performed both in generative services and by a designer. The experiment allowed us to use the machine-learning method to determine which colors are most often found in book cover layouts of one of the genres and to check whether these colors correspond to harmonious color palettes. In accordance with the proposed scheme of the design process using generative artificial intelligence, versions of book cover layouts of a given genre were obtained.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

We Need to Talk About Lung Ultrasound Score: Prediction of Intensive Care Unit Admission with Machine Learning.

IF 2.7

Journal of Imaging Pub Date : 2025-02-07 DOI: 10.3390/jimaging11020045

Duarte Oliveira-Saraiva, João Leote, Filipe André Gonzalez, Nuno Cruz Garcia, Hugo Alexandre Ferreira

{"title":"We Need to Talk About Lung Ultrasound Score: Prediction of Intensive Care Unit Admission with Machine Learning.","authors":"Duarte Oliveira-Saraiva, João Leote, Filipe André Gonzalez, Nuno Cruz Garcia, Hugo Alexandre Ferreira","doi":"10.3390/jimaging11020045","DOIUrl":"10.3390/jimaging11020045","url":null,"abstract":"The admission of COVID-19 patients to the Intensive Care Unit (ICU) is largely dependent on illness severity, yet no standard criteria exist for this decision. Here, lung ultrasound (LU) data, blood gas analysis (BGA), and clinical parameters from venous blood tests (VBTs) were used, along with machine-learning (ML) models to predict the need for ICU admission. Data from fifty-one COVID-19 patients, including ICU admission status, were collected. The information from LU was gathered through the identification of LU findings (LUFs): B-lines, irregular pleura, subpleural, and lobar consolidations. LU scores (LUSs) were computed by summing predefined weights assigned to each LUF, as reported in previous studies. In addition, individual LUFs were analyzed without calculating a total LUS. Support vector machine models were built, combining the available clinical data to predict ICU admissions. The application of ML models to individual LUFs outperformed standard LUS approaches reported in previous studies. Moreover, combining LU data with results from other medical exams improved the area under the receiver operating characteristic curve (AUC). The model with the best overall performance used variables from all three exams (BGA, LU, VBT), achieving an AUC of 95.5%. Overall, the results demonstrate the significant role of ML models in improving the prediction of ICU admission. Additionally, applying ML specifically to LUFs provided better results compared to traditional approaches that rely on traditional LUSs. The results of this paper are deployed on a web app.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robot-Based Procedure for 3D Reconstruction of Abdominal Organs Using the Iterative Closest Point and Pose Graph Algorithms.

IF 2.7

Journal of Imaging Pub Date : 2025-02-05 DOI: 10.3390/jimaging11020044

Birthe Göbel, Jonas Huurdeman, Alexander Reiterer, Knut Möller

{"title":"Robot-Based Procedure for 3D Reconstruction of Abdominal Organs Using the Iterative Closest Point and Pose Graph Algorithms.","authors":"Birthe Göbel, Jonas Huurdeman, Alexander Reiterer, Knut Möller","doi":"10.3390/jimaging11020044","DOIUrl":"10.3390/jimaging11020044","url":null,"abstract":"Image-based 3D reconstruction enables robot-assisted interventions and image-guided navigation, which are emerging technologies in laparoscopy. When a robotic arm guides a laparoscope for image acquisition, hand-eye calibration is required to know the transformation between the camera and the robot flange. The calibration procedure is complex and must be conducted after each intervention (when the laparoscope is dismounted for cleaning). In the field, the surgeons and their assistants cannot be expected to do so. Thus, our approach is a procedure for a robot-based multi-view 3D reconstruction without hand-eye calibration, but with pose optimization algorithms instead. In this work, a robotic arm and a stereo laparoscope build the experimental setup. The procedure includes the stereo matching algorithm Semi Global Matching from OpenCV for depth measurement and the multiscale color iterative closest point algorithm from Open3D (v0.19), along with the multiway registration algorithm using a pose graph from Open3D (v0.19) for pose optimization. The procedure is evaluated quantitatively and qualitatively on ex vivo organs. The results are a low root mean squared error (1.1-3.37 mm) and dense point clouds. The proposed procedure leads to a plausible 3D model, and there is no need for complex hand-eye calibration, as this step can be compensated for by pose optimization algorithms.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-Model Synergy for Fingerprint Spoof Detection Using VGG16 and ResNet50. 使用 VGG16 和 ResNet50 进行指纹欺骗检测的双模型协同。

IF 2.7

Journal of Imaging Pub Date : 2025-02-04 DOI: 10.3390/jimaging11020042

Mohamed Cheniti, Zahid Akhtar, Praveen Kumar Chandaliya

{"title":"Dual-Model Synergy for Fingerprint Spoof Detection Using VGG16 and ResNet50.","authors":"Mohamed Cheniti, Zahid Akhtar, Praveen Kumar Chandaliya","doi":"10.3390/jimaging11020042","DOIUrl":"10.3390/jimaging11020042","url":null,"abstract":"In this paper, we address the challenge of fingerprint liveness detection by proposing a dual pre-trained model approach that combines VGG16 and ResNet50 architectures. While existing methods often rely on a single feature extraction model, they may struggle with generalization across diverse spoofing materials and sensor types. To overcome this limitation, our approach leverages the high-resolution feature extraction of VGG16 and the deep layer architecture of ResNet50 to capture a more comprehensive range of features for improved spoof detection. The proposed approach integrates these two models by concatenating their extracted features, which are then used to classify the captured fingerprint as live or spoofed. Evaluated on the Livedet2013 and Livedet2015 datasets, our method achieves state-of-the-art performance, with an accuracy of 99.72% on Livedet2013, surpassing existing methods like the Gram model (98.95%) and Pre-trained CNN (98.45%). On Livedet2015, our method achieves an average accuracy of 96.32%, outperforming several state-of-the-art models, including CNN (95.27%) and LivDet 2015 (95.39%). Error rate analysis reveals consistently low Bonafide Presentation Classification Error Rate (BPCER) scores with 0.28% on LivDet 2013 and 1.45% on LivDet 2015. Similarly, the Attack Presentation Classification Error Rate (APCER) remains low at 0.35% on LivDet 2013 and 3.68% on LivDet 2015. However, higher APCER values are observed for unknown spoof materials, particularly in the Crossmatch subset of Livedet2015, where the APCER rises to 8.12%. These findings highlight the robustness and adaptability of our simple dual-model framework while identifying areas for further optimization in handling unseen spoof materials.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856235/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Imaging and Image Processing Techniques for High-Resolution Visualization of Connective Tissue with MRI: Application to Fascia, Aponeurosis, and Tendon.

IF 2.7

Journal of Imaging Pub Date : 2025-02-04 DOI: 10.3390/jimaging11020043

Meeghage Randika Perera, Graeme M Bydder, Samantha J Holdsworth, Geoffrey G Handsfield

{"title":"Imaging and Image Processing Techniques for High-Resolution Visualization of Connective Tissue with MRI: Application to Fascia, Aponeurosis, and Tendon.","authors":"Meeghage Randika Perera, Graeme M Bydder, Samantha J Holdsworth, Geoffrey G Handsfield","doi":"10.3390/jimaging11020043","DOIUrl":"10.3390/jimaging11020043","url":null,"abstract":"Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T2* decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have proven useful in generating high-signal images of these tissues. To further these advances, we discuss the integration of UTE with Diffusion Tensor Imaging (DTI) and explore image processing techniques to enhance the localization, labeling, and modeling of connective tissues. These techniques are especially valuable for extracting features from thin tissues that may be difficult to distinguish. We present data from lower leg scans of 30 healthy subjects using a non-Cartesian MRI sequence to acquire axial 2D images to segment skeletal muscle and connective tissue. DTI helped differentiate aponeurosis from deep fascia by analyzing muscle fiber orientations. The dual echo imaging methods yielded high-resolution images of deep fascia, where in-plane spatial resolutions were between 0.3 × 0.3 mm to 0.5 × 0.5 mm with a slice thickness of 3-5 mm. Techniques such as K-Means clustering, FFT edge detection, and region-specific scaling were most effective in enhancing images of deep fascia, aponeurosis, and tendon to enable high-fidelity modeling of these tissues.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0