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Journal articles on the topic 'Hu-histogram'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Romanov, Andrej, Michael Bach, Shan Yang, et al. "Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds." Diagnostics 11, no. 5 (2021): 738. http://dx.doi.org/10.3390/diagnostics11050738.

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CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After de

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Szász, Paulína, Petr Kučera, Filip Čtvrtlík, Kateřina Langová, Igor Hartmann, and Zbyněk Tüdös. "Diagnostic Value of Unenhanced CT Attenuation and CT Histogram Analysis in Differential Diagnosis of Adrenal Tumors." Medicina 56, no. 11 (2020): 597. http://dx.doi.org/10.3390/medicina56110597.

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Background and Objectives: Our aim was to verify the optimal cut-off value for unenhanced CT attenuation and the percentage of negative voxels in the volume CT histogram analysis of adrenal masses. Materials and Methods: We retrospectively analyzed the CT data of patients who underwent an adrenalectomy in the period 2002–2019. In total, 413 adrenalectomies were performed. Out of these, 233 histologically verified masses (123 adenomas, 58 pheochromocytomas, 18 carcinomas, and 34 metastases) fulfilled the inclusion criteria and were selected for analysis. The mean unenhanced attenuation in Houns

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NALBANT, Mustafa Orhan, and Ercan İNCİ. "The efficacy of volumetric computed tomography histogram analysis in adrenal masses." Journal of Health Sciences and Medicine 6, no. 4 (2023): 730–36. http://dx.doi.org/10.32322/jhsm.1279667.

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Aims: The rate of adrenal mass detection has increased due to the development of imaging modalities. It is vital to differentiate benign adrenal adenomas from other adrenal masses in order to establish whether an active management strategy is essential. Volumetric CT histogram analysis calculates the percentage of covered pixels in the negative attenuation region. The goal of this research was to evaluate the diagnostic utility of volume histogram analysis for adrenal tumors confirmed histopathologically as well as the ideal slice thickness for CT histogram analysis to differentiate between be

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Park, Sun-Young, Hong Il Ha, Sang Min Lee, In Jae Lee, and Hyun Kyung Lim. "Comparison of diagnostic accuracy of 2D and 3D measurements to determine opportunistic screening of osteoporosis using the proximal femur on abdomen-pelvic CT." PLOS ONE 17, no. 1 (2022): e0262025. http://dx.doi.org/10.1371/journal.pone.0262025.

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Objectives To compare the osteoporosis-predicting ability of computed tomography (CT) indexes in abdomen-pelvic CT using the proximal femur and the reliability of measurements in two- and three-dimensional analyses. Methods Four hundred thirty female patients (age range, 50–96 years) who underwent dual-energy X-ray absorptiometry and abdominal-pelvic CT within 1 month were retrospectively selected. The volumes of interest (VOIs) from the femoral head to the lesser trochanter and the femoral neck were expressed as 3DFemur. Round regions of interest (ROIs) of image plane drawn over the femoral n

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HU, CHIN-KUN, JAU-ANN CHEN, and F. Y. WU. "CRITICAL POINT OF THE KAGOME POTTS MODEL: A HISTOGRAM MONTE CARLO RENORMALIZATION GROUP DETERMINATION." Modern Physics Letters B 08, no. 07 (1994): 455–59. http://dx.doi.org/10.1142/s0217984994000480.

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The histogram Monte Carlo renormalization group method proposed by Hu is used to determine the critical point of the q-state Potts model on the Kagome lattice. Our results are compared with the predictions of conjectures by Wu and Tsallis.

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Wildah, Siti Khotimatul, and Abdul Latif. "Kombinasi Metode Fitur Ekstraksi untuk Indentifikasi Penyakit pada Daun Teh." Jurnal Sistem dan Teknologi Informasi (JustIN) 11, no. 3 (2023): 447. http://dx.doi.org/10.26418/justin.v11i3.65172.

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Teh merupakan salah satu minuman yang paling banyak dikonsumsi di dunia, namun produksi teh seringkali terhambat dan mengalami penurunan oleh berbagai penyakit yang mempengaruhi pertumbuhan dan kualitas daun teh. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi penyakit daun teh dengan memanfaatkan teknologi Image Classification dan menerapkan metode kombinasi analisis tekstur Haralick, Color Histogram, Hu Moment dan pengklasifikasian objek menggunakan Random Forest classifier. Dataset yang digunakan dalam penelitian ini dikumpulkan dari perkebunan teh Johnstone Boiyon di Koiwa,

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Stefano, Alessandro, Mauro Gioè, Giorgio Russo, et al. "Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT." Diagnostics 10, no. 5 (2020): 306. http://dx.doi.org/10.3390/diagnostics10050306.

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Background: Our study assesses the diagnostic value of different features extracted from high resolution computed tomography (HRCT) images of patients with idiopathic pulmonary fibrosis. These features are investigated over a range of HRCT lung volume measurements (in Hounsfield Units) for which no prior study has yet been published. In particular, we provide a comparison of their diagnostic value at different Hounsfield Unit (HU) thresholds, including corresponding pulmonary functional tests. Methods: We consider thirty-two patients retrospectively for whom both HRCT examinations and spiromet

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Kwon, Heejin, Eunju Kang, Sanghyun Kim, Yanghyun Baeck, Ilcheol Bark, and Jinhan Cho. "Predicting prognosis prior to the combination of atezolizumab and bevacizumab on unresectable HCC: Analysis and comparison of tumor heterogeneity at CT and Gd-EOB-DTPA hepatobiliary MR imaging." Medicine 103, no. 49 (2024): e40769. https://doi.org/10.1097/md.0000000000040769.

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Since 2007, the combination of atezolizumab and bevacizumab, comprising an immune checkpoint inhibitor and a molecularly targeted agent, has become the first-line treatment for advanced hepatocellular carcinoma (HCC). Predicting prognosis prior to systemic chemotherapy remains a critical concern. This study included 84 advanced HCC patients who underwent enhanced computed tomography (CT) and Gd-EOB-DTPA magnetic resonance imaging (MRI) before the systemic therapy were included. In CT, the 2 radiologists measured mean CT Hounsfield unit (CTHU) value by drawing region of interest at the largest

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huaijantug, somkiat, paranee yatmark, Wutthiwong Theerapan, Tanawalee Mantiantipan, Itsari Atsawarat, and Duangthip Chatchaisak. "Quantitative measurement of regional brain imaging using computed tomography in canine with suspected stroke." Journal of Research in Veterinary Sciences 5, no. 1 (2025): 21. https://doi.org/10.5455/jrvs.20241220022945.

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Background and Aim: Brain stroke is increasingly being recognized in veterinary medicine and difficult to diagnosis. Computed tomography (CT) scan is a tool that can help the diagnosis more easily and rapidly. The objective of this study was to determine the clinical characteristics and quantitated CT images in terms of volume and histogram analysis of the ROIs (rostral cerebral artery, middle cerebral artery, caudal cerebral artery, and caudal cerebellar artery) in dogs with suspected stroke. Materials and Methods: Six dogs with suspected stroke and 3 healthy dogs were evaluated. General clin

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Katsuta, Yoshiyuki, Noriyuki Kadoya, Shina Mouri, et al. "Prediction of radiation pneumonitis with machine learning using 4D-CT based dose-function features." Journal of Radiation Research 63, no. 1 (2021): 71–79. http://dx.doi.org/10.1093/jrr/rrab097.

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Abstract In this article, we highlight the fundamental importance of the simultaneous use of dose-volume histogram (DVH) and dose-function histogram (DFH) features based on functional images calculated from 4-dimensional computed tomography (4D-CT) and deformable image registration (DIR) in developing a multivariate radiation pneumonitis (RP) prediction model. The patient characteristics, DVH features and DFH features were calculated from functional images by Hounsfield unit (HU) and Jacobian metrics, for an RP grade ≥ 2 multivariate prediction models were computed from 85 non-small cell lung

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Kurniawan, Hans Christian, Kevin Suryajaya Soemarto, and Bernardo Nugroho Yahya. "Evaluasi Metode Ekstraksi Fitur Hu Moment Invariants untuk Pengenalan Aktivitas Manusia." Jurnal Telematika 15, no. 2 (2021): 107–14. http://dx.doi.org/10.61769/telematika.v15i2.367.

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Vision-based Human Activity Recognition has been widely used due to a bunch of video data availability in the present days through CCTV and another mechanism which contains some human activities. This data is going to be very useful to improve and automate decision-making in several fields including security surveillance. In this field, it is important to achieve a good performance (i.e., accuracy) inefficient computational time. While there are many approaches in this field, most complex approaches require high computational time. In this work, we are evaluating Hu Moments performance, as wel

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Parrella, Giovanni, Alessandro Vai, Anestis Nakas, et al. "Synthetic CT in Carbon Ion Radiotherapy of the Abdominal Site." Bioengineering 10, no. 2 (2023): 250. http://dx.doi.org/10.3390/bioengineering10020250.

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The generation of synthetic CT for carbon ion radiotherapy (CIRT) applications is challenging, since high accuracy is required in treatment planning and delivery, especially in an anatomical site as complex as the abdomen. Thirty-nine abdominal MRI-CT volume pairs were collected and a three-channel cGAN (accounting for air, bones, soft tissues) was used to generate sCTs. The network was tested on five held-out MRI volumes for two scenarios: (i) a CT-based segmentation of the MRI channels, to assess the quality of sCTs and (ii) an MRI manual segmentation, to simulate an MRI-only treatment scena

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Nandanwar, Pratiksha D., and Dr Somnath B. Dhonde. "A Novel Approach to Cervical Cancer Detection Using Hybrid Stacked Ensemble Models and Feature Selection." International Journal of Electrical and Electronics Research 11, no. 2 (2023): 582–89. http://dx.doi.org/10.37391/ijeer.110246.

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Around the world, millions of women are diagnosed with cervical cancer each year. Early detection is very important to produce a better overall quality of life for those diagnosed with the disease and reduce the burden on the healthcare system. In recent years, the field of machine learning (ML) has been developing methods that can improve the accuracy of detecting cervical cancer. This paper presents a new approach to this problem by using a combination of image segmentation and feature extraction techniques. The proposed approach is divided into three phases. The first stage involves image s

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Liu, Yingzi, Yang Lei, Tonghe Wang, et al. "MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method." British Journal of Radiology 92, no. 1100 (2019): 20190067. http://dx.doi.org/10.1259/bjr.20190067.

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Objective: The purpose of this work is to develop and validate a learning-based method to derive electron density from routine anatomical MRI for potential MRI-based SBRT treatment planning. Methods: We proposed to integrate dense block into cycle generative adversarial network (GAN) to effectively capture the relationship between the CT and MRI for CT synthesis. A cohort of 21 patients with co-registered CT and MR pairs were used to evaluate our proposed method by the leave-one-out cross-validation. Mean absolute error, peak signal-to-noise ratio and normalized cross-correlation were used to

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Nurbaity, Sabri, Shafekah Kassim Nurul, Ibrahim Shafaf, Roslan Rosniza, Nabilah Abu Mangshor Nur, and Ibrahim Zaidah. "Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing." International Journal of Artificial Intelligence (IJ-AI) 9, no. 2 (2020): 304–9. https://doi.org/10.11591/ijai.v9.i2.pp304-309.

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Maize is one of the world's leading food supplies. Therefore, the crop's production must continue to reproduce to fulfill the market demand. Maize is an active feeder, therefore, it need to be adequately supplied with nutrients. The healthy plants will be in deep green color to indicate it consist of adequate nutrient. Current practice to identify the nutrient deficiency on maize leaf is throught a laboratory test. It is time consuming and required agriculture knowledge. Therefore, an image processing approach has been done to improve the laboratory test and eliminate a human error in

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Yu, Xiaoyang, Shuang Liu, Ming Pang, Jixun Zhang, and Shuchun Yu. "Novel SGH Recognition Algorithm Based Robot Binocular Vision System for Sorting Process." Journal of Sensors 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/5479152.

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To achieve automatic sorting on commodity trademarks, a binocular vision system has been constructed in this paper. By adjusting camera pose, this system can obtain greater shooting perspective. In order to improve sorting accuracy, a now SGH recognition method is proposed. SGH consists of spatial color histogram (Sfeature), gray level cooccurrence matrix (Gfeature), and Hu moments (H) feature, which represent color feature, texture feature, and shaper feature, respectively. Similarity judgment function is built by using SGH. The experimental results show that SGH algorithm has a higher visual

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Wuryani, Nanik, and Sarifah Agustiani. "Random Forest Classifier untuk Deteksi Penderita COVID-19 berbasis Citra CT Scan." Jurnal Teknik Komputer 7, no. 2 (2021): 187–93. http://dx.doi.org/10.31294/jtk.v7i2.10468.

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Covid-19 merupakan virus yang menyebar dan meluas sehingga berubah menjadi suatu pandemi. Virus Covid-19 menyerang melalui organ vital manusia yaitu paru-patu, oleh karena itu peneliti lebih berfokus untuk mengidentifikasi Covid-19 pada paru-paru. Penelitian ini dilakukan dengan menggunakan citra CT Scan paru-paru dan bertujuan untuk mendeteksi ada tidaknya virus dengan cara mengklasifikasikan citra Covid-19 ke dalam tiga kelas menggunakan algoritma Random Forest serta mengkombinasikannya dengan menyertakan beberapa ekstraksi fitur yaitu Haralick, Color Histogram, dan Hu-Moments. Penelitian di

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Qiu, Richard L. J., Yang Lei, Joseph Shelton, et al. "Deep learning-based thoracic CBCT correction with histogram matching." Biomedical Physics & Engineering Express 7, no. 6 (2021): 065040. http://dx.doi.org/10.1088/2057-1976/ac3055.

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Abstract Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield unit (HU) values. Here, we propose a new deep learning-based method to generate synthetic CTs (sCT) from thoracic CBCTs. A deep-learning model which integrates histogram matching (HM) into a c

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Shafai-Erfani, Ghazal, Yang Lei, Yingzi Liu, et al. "MRI-Based Proton Treatment Planning for Base of Skull Tumors." International Journal of Particle Therapy 6, no. 2 (2019): 12–25. http://dx.doi.org/10.14338/ijpt-19-00062.1.

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Abstract Purpose: To introduce a novel, deep-learning method to generate synthetic computed tomography (SCT) scans for proton treatment planning and evaluate its efficacy. Materials and Methods: 50 Patients with base of skull tumors were divided into 2 nonoverlapping training and study cohorts. Computed tomography and magnetic resonance imaging pairs for patients in the training cohort were used for training our novel 3-dimensional generative adversarial network (cycleGAN) algorithm. Upon completion of the training phase, SCT scans for patients in the study cohort were predicted based on their

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LIM, S. N., X. Chen, E. M. Gore, and A. Li. "Early Treatment Response Assessment Based on Computed Tomography HU Histogram Feature in the Periphery of Lung Tumors." International Journal of Radiation Oncology*Biology*Physics 96, no. 2 (2016): E647—E648. http://dx.doi.org/10.1016/j.ijrobp.2016.06.2250.

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Lestari, Fauzia Puspa, Choirul Anam, Yati Hardiyanti, and Freddy Haryanto. "Automated Universal Image Quality Index Measurement vs. Automated Noise Measurement: Which Method is Better to Define CT Image Quality?" Jurnal Penelitian Fisika dan Aplikasinya (JPFA) 9, no. 2 (2019): 132. http://dx.doi.org/10.26740/jpfa.v9n2.p132-139.

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Automatitation method in defining the quality of CT image is needed to optimize CT Scan treatment planning. So, the optimization of treatment planning can also be done automatically. There are various methods proposed to define the quality of an image. The purpose of this study was to find the simple and precision method to define CT image. We compared the performance of Automated Noise Measurement (ANM) and Automated Universal Image Quality Index (UIQI). We also compared them with the Manual noise measurement method based on the level of convergence in homogeneous images. The first step of Au

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Sakboonyarat, Boonnatee, and Pinyo Taeprasartsit. "Discriminative Image Enhancement for Robust Cascaded Segmentation of CT Images." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 15, no. 2 (2021): 150–65. http://dx.doi.org/10.37936/ecti-cit.2021152.240112.

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Objective: Cascaded/attention-based neural network has become common in image segmentation. This work proposes to improve its robustness by adding discriminative image enhancement to its attention mechanism. Unlike prior work, this image enhancement can also be applied as data augmentation and easily adapted for existing models. Its generalization can improve accuracy across multiple segmentation tasks and datasets. Methods: The method first localizes a target organ in a 2D fashion to obtain a tight neighborhood of the organ in each slice. Next, the method computes an HU histogram of a region

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Xue, Xudong, Yi Ding, Jun Shi, et al. "Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy." Technology in Cancer Research & Treatment 20 (January 2021): 153303382110624. http://dx.doi.org/10.1177/15330338211062415.

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Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the CycleGAN, Pix2pix and U-Net models were used to generate the sCT images. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were used to quantify the accuracy of the proposed models in a testing cohort of 34 patients. Radiation dose were calculated on

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Nadiyah Hidayati and Mawadatul Maulidah. "EKSTRAKSI FITUR DENGAN COLOR HISTOGRAM DAN CLASSIFIER RANDOM FOREST PADA CITRA KUPU-KUPU." JAMI: Jurnal Ahli Muda Indonesia 4, no. 2 (2023): 148–57. http://dx.doi.org/10.46510/jami.v4i2.172.

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Objektif. Penelitian dalam pengolahan citra banyak dikembangkan dalam berbagai bidang, misalnya kesehatan, pertanian, kesenian, aneka ragam hayati dll. Salah satu penelitian yang berkembang adalah pengklasifikasian jenis serangga yaitu kupu-kupu. Kupu-kupu merupakan salah satu serangga yang menguntungkan bagi manusia, namun populasi spesies kupu-kupu di Indonesia banyak yang menurun atau terancam punah. Dengan banyaknya jenis kupu-kupu dalam berbagai bentuk, corak yang berbeda, dan keunikan diperlukan suatu teknik yang memfasilitasi pembelajaran dengan lebih efisien. Kupu-kupu dijadikan datase

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Sabri, Nurbaity, Nurul Shafekah Kassim, Shafaf Ibrahim, Rosniza Roslan, Nur Nabilah Abu Mangshor, and Zaidah Ibrahim. "Nutrient deficiency detection in Maize (Zea mays L.) leaves using image processing." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 2 (2020): 304. http://dx.doi.org/10.11591/ijai.v9.i2.pp304-309.

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<span lang="EN-US">Maize is one of the world's leading food supplies. Therefore, the crop's production must continue to reproduce to fulfill the market demand. Maize is an active feeder, therefore, it need to be adequately supplied with nutrients. The healthy plants will be in deep green color to indicate it consist of adequate nutrient. Current practice to identify the nutrient deficiency on maize leaf is throught a laboratory test. It is time consuming and required agriculture knowledge. Therefore, an image processing approach has been done to improve the laboratory test and eliminate

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Ley-Zaporozhan, Julia, Athanasios Giannakis, Tobias Norajitra, et al. "Fully Automated Segmentation of Pulmonary Fibrosis Using Different Software Tools." Respiration 100, no. 7 (2021): 580–87. http://dx.doi.org/10.1159/000515182.

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<b><i>Objective:</i></b> Evaluation of software tools for segmentation, quantification, and characterization of fibrotic pulmonary parenchyma changes will strengthen the role of CT as biomarkers of disease extent, evolution, and response to therapy in idiopathic pulmonary fibrosis (IPF) patients. <b><i>Methods:</i></b> 418 nonenhanced thin-section MDCTs of 127 IPF patients and 78 MDCTs of 78 healthy individuals were analyzed through 3 fully automated, completely different software tools: YACTA, LUFIT, and IMBIO. The agreement between YACTA and LU

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Park, Sun-Young, Hong Il Ha, Injae Lee, and Hyun Kyung Lim. "Comparison of HU histogram analysis and BMD for proximal femoral fragility fracture assessment: a retrospective single-center case–control study." European Radiology 32, no. 3 (2021): 1448–55. http://dx.doi.org/10.1007/s00330-021-08281-2.

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Gong, Hanshun, Bo Liu, Gaolong Zhang, et al. "Evaluation of Dose Calculation Based on Cone-Beam CT Using Different Measuring Correction Methods for Head and Neck Cancer Patients." Technology in Cancer Research & Treatment 22 (January 2023): 153303382211483. http://dx.doi.org/10.1177/15330338221148317.

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Purpose: To investigate and compare 2 cone-beam computed tomography (CBCT) correction methods for CBCT-based dose calculation. Materials and Methods: Routine CBCT image sets of 12 head and neck cancer patients who received volumetric modulated arc therapy (VMAT) treatment were retrospectively analyzed. The CBCT images obtained using an on-board imager (OBI) at the first treatment fraction were firstly deformable registered and padded with the kVCT images to provide enough anatomical information about the tissues for dose calculation. Then, 2 CBCT correction methods were developed and applied t

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Vellini, Luca, Sergio Zucca, Jacopo Lenkowicz, et al. "A Deep Learning Approach for the Fast Generation of Synthetic Computed Tomography from Low-Dose Cone Beam Computed Tomography Images on a Linear Accelerator Equipped with Artificial Intelligence." Applied Sciences 14, no. 11 (2024): 4844. http://dx.doi.org/10.3390/app14114844.

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Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images from low-dose Cone Beam CT (CBCT) acquired on a modern linear accelerator integrating AI. Methods: A total of 53 patients treated in the pelvic region were enrolled and split into training (30), validation (9), and testing (14). A Generative Adversarial Network (GAN) was trained for 200 epochs. The image accuracy was eva

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Arya, Devrat, and Jaimala Jha. "GLOBAL AND LOCAL DESCRIPTOR FOR CBIR AND IMAGE ENHANCEMENT USING MULTI-FEATURE FUSION METHOD." International Journal of Research -GRANTHAALAYAH 4, no. 6 (2016): 170–82. http://dx.doi.org/10.29121/granthaalayah.v4.i6.2016.2651.

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The research is ongoing in CBIR it is getting much popular. In this retrieval of image is done using a technique that searches the necessary features of image. The main work of CBIR is to get retrieve efficient, perfect and fast results.In this algorithm, fused multi-feature for color, texture and figure features. A global and local descriptor (GLD) is proposed in this paper, called Global Correlation Descriptor (GCD) and Discrete Wavelet Transform (DWT), to excerpt color and surface feature respectively so that these features have the same effect in CBIR. In addition, Global Correlation Vecto

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Safitri, Eli, Ronasip Heppy Ria Sibarani, Yusiva SM Sidabutar, and Dedy Kiswanto. "KLASIFIKASI PENYAKIT DAUN ANGGUR BERBASIS CITRA MENGGUNAKAN METODE K-NEAREST NEIGHBORS (KNN)." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 6 (2024): 12633–42. https://doi.org/10.36040/jati.v8i6.12004.

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Penurunan produktivitas dan kualitas tanaman anggur sering kali disebabkan oleh serangan penyakit pada daunnya, yang sulit dideteksi secara manual di area perkebunan yang luas. Untuk mengatasi tantangan ini, penelitian ini mengembangkan sistem deteksi penyakit daun anggur berbasis citra menggunakan metode K-Nearest Neighbors (KNN) yang bertujuan meningkatkan akurasi deteksi dan efisiensi klasifikasi penyakit. Dalam penelitian ini, gambar daun anggur yang sehat dan terinfeksi penyakit dikumpulkan dan dianalisis dengan metode ekstraksi fitur yang meliputi Local Binary Pattern (LBP) untuk tekstur

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Xin-ye, Ni, Gao Liugang, Fang Mingming, and Lin Tao. "Application of Metal Implant 16-Bit Imaging: New Technique in Radiotherapy." Technology in Cancer Research & Treatment 16, no. 2 (2016): 188–94. http://dx.doi.org/10.1177/1533034616649530.

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Objective: This study aimed to evaluate the computed tomography number and the variation of dose distribution based on 12-bit, 16-bit, and revised 16-bit images while the metal bars were inserted. Methods: The phantoms containing stainless steel, titanium alloy, and aluminum bar were scanned with computed tomography. These images were reconstructed with 12-bit and 16-bit imaging technologies. The “cupping artifacts” computed tomography value of the metal object revised by Matlab software was called the revised 16-bit image. The computed tomography values of these metal materials were analyzed.

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Mouad M.H. Ali, Arafat S.M. Qaed, and Abdulrazzaq H. A.Al-ahdal. "Score and Feature Level Fusion Approaches for Evaluation of Multi-Features of Fingerprint Modality for Person Recognition System." Abhath Journal of Basic and Applied Sciences 1, no. 2 (2022): 27–36. http://dx.doi.org/10.59846/abhathjournalofbasicandappliedsciences.v1i2.442.

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In the biometrics, the technologies grow day by day and the security also increased related to that technologies. The fingerprint was the most intensively researched in the field of biometrics system due to permanence and uniqueness features which made varies of different peoples. The paper addressing many stages, in addition to the primary stages of any biometrics system the fusion of unimodal system was used in order to improve the performance of the system. The double enhancement techniques were used to make the images very clear by Histogram Equalization and Fast Fourier Transformation (FF

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Bliznakova, Kristina, Tihomir Georgiev, Antonio Sarno, et al. "A comparison of two low-cost 3D printing techniques for constructing phantoms from MRI breast images." International Journal of Radiation Research 22, no. 4 (2024): 883–90. https://doi.org/10.61186/ijrr.22.4.883.

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<strong>Abstract</strong> Background: This study aimed to test the possibility of using Magnetic Resonance (MR) images to create anthropomorphic breast phantoms for X-ray imaging and to compare the performance of fused deposition modeling (FDM) and 2D inkjet printing with radiopaque inks. Materials and Methods: Two physical phantoms were produced using either an inkjet printer on paper or an FDM technique, both based on clinical MR data. The paper phantom was printed with 1.2 g of KI dissolved in 20 ml of water. For the FDM phantom, the extrusion rate was adjusted according to clinical Hounsfi

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Wang, Junhui, Meng Zhao, Li Zou, Yi Hu, Xuezhen Cheng, and Xiaofeng Liu. "Fish Tracking Based on Improved TLD Algorithm in Real-World Underwater Environment." Marine Technology Society Journal 53, no. 3 (2019): 80–89. http://dx.doi.org/10.4031/mtsj.53.3.8.

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AbstractFish tracking in the natural underwater environment is of great value for many applications, such as fish behavior analysis and the estimation of fish population density. Due to the variety of swimming postures, uneven illumination, and complicated background in the real-world underwater environment, most existing object tracking algorithms are not ideal for fish tracking. In this paper, a tracking algorithm based on TLD (Tracking-Learning-Detection) is proposed, in which the shape and color features of fish are fully utilized to achieve accurate and rapid tracking. The proposed algori

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Smt., Rohini Vijay Daund. "Shivalinga (Pind) Detection Using Machine Learning Techniques." International Journal of Advance and Applied Research S6, no. 22 (2025): 1058–61. https://doi.org/10.5281/zenodo.15534534.

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<em>Shivalinga, a sacred symbol of Lord Shiva, holds immense cultural and religious significance in Hinduism. Recognizing its image using automated image processing techniques presents unique challenges due to variations in material, lighting, occlusions, and similar-looking artifacts. This research proposes a deep learning-based approach to efficiently recognize Shivalinga images from diverse sources, including temple photographs, sculptures, and digital repositories. The study integrates traditional image processing techniques such as edge detection and contour analysis with advanced deep le

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Kumar, Alok, and Manmohan Singh. "Ant Colony Optimization Algorithm for Disease Detection in Maize Leaf using Machine Learning Techniques." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, no. 01 (2022): 31–37. http://dx.doi.org/10.18090/samriddhi.v14i01.5.

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Plant diseases have affected the productivity of food in recent years. Because of this productivity loss, humans and animals are also affected, but the whole biodiversity would be affected. So, we should take preventive measures to stop this food destruction. Both humans and animals largely consume the maize crop. Due to some factors, Maize leaf is easily affected by some fungal or other diseases. Farmers could not find out the leaf diseases at the early stages. They need some advanced methods to detect these types of diseases. Early detection of leaf disease helps farmers to increase the Maiz

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Murat, Miraemiliana, Siow-Wee Chang, Arpah Abu, Hwa Jen Yap, and Kien-Thai Yong. "Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach." PeerJ 5 (September 12, 2017): e3792. http://dx.doi.org/10.7717/peerj.3792.

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Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that c

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Cui, Xiaoyue. "Research on Clothing Image Database Retrieval Algorithm Based on Wavelet Transform." Journal of Mathematics 2022 (January 7, 2022): 1–8. http://dx.doi.org/10.1155/2022/6332592.

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Aiming at the problems of low image data retrieval accuracy and slow retrieval speed in the existing image database retrieval algorithms, this paper designs a clothing image database retrieval algorithm based on wavelet transform. Firstly, it represents the color consistency vector of clothing image, reflects the composition and distribution of image color through color histogram, quantifies the visual features of clothing image, aggregates them into a fixed size representation vector, and uses the Fair Value (FV) model to complete the collection of clothing image data. Then, the size of the c

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Szilveszter, B., S. Newlander, Z. Jokkel, et al. "Ground Truth Accuracy And Histogram Analysis Of Lipid Rich Low-attenuation Versus Noncalcified Plaque Volume Quantification Across Different Fixed Versus Adaptive HU Thresholds - A Photon Counting Phantom Study." Journal of Cardiovascular Computed Tomography 19, no. 4 (2025): S82—S83. https://doi.org/10.1016/j.jcct.2025.05.194.

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Vishnoi, Vibhor Kumar, Krishan Kumar, Brajesh Kumar, and Rakesh Bhutiani. "A stacking ensemble machine learning based approach for classification of plant diseases through leaf images." Environment Conservation Journal 25, no. 3 (2024): 767–78. http://dx.doi.org/10.36953/ecj.28742840.

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Diseases and pests in plants/crops are major causes of significant agricultural losses with economic, social and ecological impacts. Therefore, there is a need for early identification of plant diseases and pests through automated systems. Recently, machine learning-based methods have become popular in solving agricultural problems such as plant diseases faced by technically-noob farmers. This work proposes a novel method based on stacking ensemble machine learning to detect plant diseases in Uradbean precisely. Two classifiers: support vector machine (SVM), random forest (RF) are trained on a

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Zhu, Libing, Nathan Y. Yu, Riley C. Tegtmeier, et al. "Deep Learning-Based Synthetic CT for Personalized Treatment Modality Selection Between Proton and Photon Therapy in Thoracic Cancer." Cancers 17, no. 9 (2025): 1553. https://doi.org/10.3390/cancers17091553.

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Objectives: Identifying patients’ advantageous radiotherapy modalities prior to CT simulation is challenging. This study aimed to develop a workflow using deep learning (DL)-predicted synthetic CT (sCT) for treatment modality comparison based solely on a diagnostic CT (dCT). Methods: A DL network, U-Net, was trained utilizing 46 thoracic cases from a public database to generate sCT images predicting planning CT (pCT) scans based on the latest dCT, and tested on 15 institutional patients. The sCT accuracy was evaluated against the corresponding pCT and a commercial algorithm deformed CT (MdCT)

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Bai, Han, Wenhui Li, Yaoxiong Xia, Lan Li, Jingyan Gao, and Xuhong Liu. "Preliminary Study on the Effect of 4DCT-Ventilation-Weighted Dose on the Radiation Induced Pneumonia Probability (RIPP)." Dose-Response 19, no. 3 (2021): 155932582110177. http://dx.doi.org/10.1177/15593258211017753.

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Purpose: The purpose of the present study was to evaluate the feasibility of using 4-dimensional computed tomography (4DCT)-ventilation-weighted dose analysis to predict radiation-induced pneumonia probability (RIPP). Methods and Materials: The study population for this retrospective analysis included 16 patients with stage III lung cancer. Each patient’s 4DCT images, including end-inhale and end-exhale sequences, were used for the deformable image registration, and the Hounsfield units (HU) density-change was used to calculate the ventilation. A previously established equation was used to con

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Nurhadi, Nurhadi, Eko Arip Winanto, Rahaini Mohd Said, Jasmir Jasmir, and Lasmedi Afuan. "PATTERN CLASSIFICATION SIGN LANGUAGE USING FEATURES DESCRIPTORS AND MACHINE LEARNING." Jurnal Teknik Informatika (Jutif) 5, no. 2 (2024): 349–56. https://doi.org/10.52436/1.jutif.2024.5.2.1228.

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Sign language is way of communication for the deaf and speech impaired. In Indonesia, the utilization of a standardized language involves the incorporation of American Sign Language (ASL). ASL is employed for various communication needs, ranging from basic alphanumeric fingerspelling (A-Z and numbers) to the more complex SIBI form (comprising gesture vocabulary) in everyday interactions as well as formal contexts. This surge in the digitization of sign language underscores the ongoing advancements in research and development. The challenge in this research lies in the ability to recognize Amer

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Birnbacher, Lorenz, Margarita Braunagel, Marian Willner, et al. "Quantitative differentiation of minimal-fat angiomyolipomas from renal cell carcinomas using grating-based x-ray phase-contrast computed tomography: An ex vivo study." PLOS ONE 18, no. 4 (2023): e0279323. http://dx.doi.org/10.1371/journal.pone.0279323.

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Background The differentiation of minimal-fat—or low-fat—angiomyolipomas from other renal lesions is clinically challenging in conventional computed tomography. In this work, we have assessed the potential of grating-based x-ray phase-contrast computed tomography (GBPC-CT) for visualization and quantitative differentiation of minimal-fat angiomyolipomas (mfAMLs) and oncocytomas from renal cell carcinomas (RCCs) on ex vivo renal samples. Materials and methods Laboratory GBPC-CT was performed at 40 kVp on 28 ex vivo kidney specimens including five angiomyolipomas with three minimal-fat (mfAMLs)

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Chen, Yajun, Zhangnan Wu, Bo Zhao, Caixia Fan, and Shuwei Shi. "Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine." Sensors 21, no. 1 (2020): 212. http://dx.doi.org/10.3390/s21010212.

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Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and

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Kiran, S. M., and D. N. Chandrappa. "Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms." Journal of Robotics and Control (JRC) 4, no. 6 (2023): 840–48. http://dx.doi.org/10.18196/jrc.v4i6.20342.

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India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in c

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A. Jain, Sajan, N. Shobha Rani, and N. Chandan. "Image Enhancement of Complex Document Images Using Histogram of Gradient Features." International Journal of Engineering & Technology 7, no. 4.36 (2018): 780. http://dx.doi.org/10.14419/ijet.v7i4.36.24244.

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Enhancement of document images is an interesting research challenge in the process of character recognition. It is quite significant to have a document with uniform illumination gradient to achieve higher recognition accuracies through a document processing system like Optical Character Recognition (OCR). Complex document images are one of the varied image categories that are difficult to process compared to other types of images. It is the quality of document that decides the precision of a character recognition system. Hence transforming the complex document images to a uniform illumination

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Asnafi, Solmaz, Benson S. Chen, Valérie Biousse, Nancy J. Newman, and Amit M. Saindane. "Intracranial computed tomography histogram analysis detects changes in the setting of elevated intracranial pressure and normal imaging." Neuroradiology Journal, May 4, 2022, 197140092210968. http://dx.doi.org/10.1177/19714009221096832.

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Background: Patients with idiopathic intracranial hypertension (IIH) have elevated intracranial pressure (ICP) of unclear etiology. This study evaluated the ability of quantitative intracranial Hounsfield unit (HU) histogram analysis to detect pathophysiological changes from elevated ICP in the setting of a normal head CT. Methods: Retrospective analysis of non-contrast-enhanced head CT images of IIH patients and matched controls. Following skull stripping, total intracranial CT voxels within the range of 0-70 HU were divided into seven 10 HU bins. A measurement of total intracranial HU was al

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Ryoo, Chang Hyun, Jee Won Chai, Sung Hwan Hong, Ja-Young Choi, Hye Jin Yoo, and Hee Dong Chae. "CT hounsfield unit and histogram analysis for differentiation of recent versus remote vertebral compression fractures." British Journal of Radiology, September 19, 2021, 20210941. http://dx.doi.org/10.1259/bjr.20210941.

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Objectives: The purpose of this study was to analyze the intraosseous tissue changes in recent vertebral compression fractures (VCFs) and to differentiate recent from remote VCFs using CT Hounsfield unit histogram analysis (HUHA). Methods: Sixty-five patients with T11 to L3 VCFs were included. HUHA of 2 vertebral bodies (VBs)— a fractured VB and the closest lower-level unaffected VB—was done. The mean Hounsfield unit (HU) value and HU proportions of 5 ranges (HU ≤ 0, 0 < HU≤50, 50 < HU≤100, 100 < HU≤150, and HU > 150) were obtained. Then, ΔHU value and ΔHU proportion were calculate

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