Region Of Interest Region Research Articles

An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow.

In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy. Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity. These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViTB- 16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95. This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.

Neural network-based reversible data hiding for medical image

Pixel prediction is an important issue in the field of reversible data hiding. Neural networks are gradually used to improve the accuracy of pixel prediction owing to their excellent performance. However, current neural network-based pixel predictors are designed for natural images and do not consider the characteristics of medical images. Therefore, in this paper, we propose a dual-branch neural network-based reversible data hiding scheme for medical images. Detailedly, considering the characteristics of medical images, in which complex and smooth regions are more clearly distinguished, we present a clustering method to classify pixels into three classes according to their complexities, and generate masks to assist pixel prediction. Then, in the prediction stage, a dual-branch neural network-based pixel predictor is designed to extract unique and shared features, and a convolutional block attention module is used to optimize the extracted features. Finally, in the embedding stage, considering the characteristics of region of interest (ROI) and region of non-interest (NROI) in medical images, we design a class-based embedding algorithm, which can prioritize embedding data into NROI with low complexity and then sequentially into low texture complexity region and high texture complexity region of ROI. Experimental results show that our scheme can achieve better performance of pixel prediction and data embedding than existing state-of-the-art works.

Digital versus manual pathology scoring of PD-L1 in patients with esophageal squamous cell carcinoma (ESCC).

e16023 Background: Immune checkpoint inhibitors (ICIs) are standard-of-care for patients with ESCC, but the association between PD-L1 and clinical outcomes of patients with ESCC receiving ICIs treatment is unclear. Currently, different PD-L1 antibody clones, scoring, and cutoffs were used for different ICIs development in ESCC. Thus, we develop a digital pathology (DP) algorithm to generate multiple scores at the same time with high reproducibility. We evaluate DP algorithm by: (1) comparing the digital and manual scores of SP263 and 22C3 assays; (2) evaluating the association with OS. Methods: Tumor tissue samples were collected from ESCC patients treated at National Taiwan University Hospital between 2017 and 2020. Slides were tested for PD-L1 using SP263 and 22C3 assays at a College of American Pathologists accredited central laboratory. H&E and PD-L1 slides were scanned for whole slide images (WSIs) using DP200 scanner at 20x. HALO (v3.5) by indica labs was used for DP development by deep learning neural network algorithms. WSIs of SP263 were annotated for region of interest (ROI), tumor/stroma area, and tumor, immune and fibroblast cells by board certified pathologists. After the algorithm is developed and run on SP263 WSIs, a board-certified pathologist reviewed the images and provided an agreement score of DP markups. The images were excluded if an agreement was less than 80%. This algorithm was then applied to 22C3 WSIs. Data of areas and cells were exported for subsequent statistical analysis using R. We calculated the DP scores based on the definition of manual readouts of TAP, CPS and s/iTILs in ROI and tumor and stroma regions in both clones’ WSIs. These DP scores were compared with manual scores using Spearman’s rank-order correlation. Association with OS were evaluated using cox proportional hazard regression. Results: Of 235 evaluable images, twelve (5.1%) were excluded due to the low agreement with the pathologist's judgment. DP generated scores showed a strong correlation with pathologists scoring (rho = 0.80 for SP263 and rho = 0.74 for 22C3). DP generated CPS-like and TAP-like scores in ROI, and tumor and stroma regions from SP263 and 22C3 also demonstrated a strong correlation between each other (rho: 0.77 - 0.86). However, s/iTILs correlation between DP and pathologist score is negligible (rho: 0.002 - 0.34). While examining the association of PD-L1 scores and s/iTILs with OS, no clear associations were found with either DP or pathologists’ scores, after adjusting for stage, ECOG, age and gender. Conclusions: DP algorithms developed in this study demonstrated the efficiency of generating multiple DP scores of CPS, TAP, and s/iTILs from one image at the same time, which performed comparable to manual scores. DP algorithm developed based on SP263 is generalizable to 22C3 and showed a good performance. Further algorithm adjustment will be made to overcome the misclassification in the 12 failed cases.

Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation

The high prevalence of cardiovascular diseases (CVDs) calls for accessible and cost-effective continuous cardiac monitoring tools. Despite Electrocardiography (ECG) being the gold standard, continuous monitoring remains a challenge, leading to the exploration of Photoplethysmography (PPG), a promising but more basic alternative available in consumer wearables. This notion has recently spurred interest in translating PPG to ECG signals. In this work, we introduce Region-Disentangled Diffusion Model (RDDM), a novel diffusion model designed to capture the complex temporal dynamics of ECG. Traditional Diffusion models like Denoising Diffusion Probabilistic Models (DDPM) face challenges in capturing such nuances due to the indiscriminate noise addition process across the entire signal. Our proposed RDDM overcomes such limitations by incorporating a novel forward process that selectively adds noise to specific regions of interest (ROI) such as QRS complex in ECG signals, and a reverse process that disentangles the denoising of ROI and non-ROI regions. Quantitative experiments demonstrate that RDDM can generate high-fidelity ECG from PPG in as few as 10 diffusion steps, making it highly effective and computationally efficient. Additionally, to rigorously validate the usefulness of the generated ECG signals, we introduce CardioBench, a comprehensive evaluation benchmark for a variety of cardiac-related tasks including heart rate and blood pressure estimation, stress classification, and the detection of atrial fibrillation and diabetes. Our thorough experiments show that RDDM achieves state-of-the-art performance on CardioBench. To the best of our knowledge, RDDM is the first diffusion model for cross-modal signal-to-signal translation in the bio-signal domain.

Application Exploration of Medical Image-aided Diagnosis of Breast Tumour Based on Deep Learning.

Nowadays, people attach increasing importance to accurate and timely disease diagnosis and personalized treatment. Because of the uncertainty and latency of the pathogenesis, it is difficult to detect breast tumour early. With higher resolution, magnetic resonance imaging (MRI) has become an important method for early detection of cancer in recent years. At present, DL technology can automatically study imaging features of different depths. This work aimed to use DL to study medical image-assisted diagnosis. The image data were collected from the patients. ROI (region of interest) containing the complete tumor area in the medical image was generated. The ROI image was extracted, and the extracted feature data were expanded. By constructing a three-dimensional (3D) CNN model, the evaluation indicators of breast tumour diagnosis results have been proposed. In the experiment part, 3D CNN model and other models have been used to diagnose the medical image of breast tumour. The 3D CNN model exhibited good ROI region extraction effect and breast tumor image diagnosis effect, and the average diagnostic accuracy of breast tumor image diagnosis was 0.736, which has been found to be much higher than other models and could be applied to breast tumor medical image-aided diagnosis. The 3D CNN model has been trained by combining the two-dimensional CNN training mode, and the evaluation index of diagnostic results has been established. The experimental part verified the medical image diagnosis effect of the 3D CNN model. The model had exhibited a high ROI region extraction effect and breast tumor image diagnosis effect.

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma from Multi-sequence Magnetic Resonance Imaging based on Deep Fusion Representation Learning.

Recent studies have identified microvascular invasion (MVI) as the most vital independent biomarker associated with early tumor recurrence. With advancements in medical technology, several computational methods have been developed to predict preoperative MVI using diverse medical images. These existing methods rely on human experience, attribute selection or clinical trial testing, which is often time-consuming and labor-intensive. Leveraging the advantages of deep learning, this study presents a novel end-to-end algorithm for predicting MVI prior to surgery. We devised a series of data preprocessing strategies to fully extract multi-view features from the data while preserving peritumoral information. Notably, a new multi-branch deep fused feature algorithm based on ResNet (DFFResNet) is introduced, which combines Magnetic Resonance Images (MRI) from different sequences to enhance information complementarity and integration. We conducted prediction experiments on a dataset from the Radiology Department of the First Hospital of Lanzhou University, comprising 117 individuals and seven MRI sequences. The model was trained on 80% of the data using 10-fold cross-validation, and the remaining 20% were used for testing. This evaluation was processed in two cases: CROI, containing samples with a complete region of interest (ROI), and PROI, containing samples with a partial ROI region. The robustness results from repeated experiments at both image and patient levels demonstrate the superior performance and improved generalization of the proposed method compared to alternative models. Our approach yields highly competitive prediction results even when the ROI region outline is incomplete, offering a novel and effective multi-sequence fused strategy for predicting preoperative MVI.

A method for standardized wear detection of masks based on key point detection

Since the outbreak of COVID-19, wearing masks outside has become a daily habit. In view of the current problems of low accuracy and lack of non-standard detection of mask wearing, a detection method for mask wearing based on key points is proposed. First, the YOLOv7_tiny algorithm is used to detect whether the face is wearing a mask, and the resulting ROI (Region of Interest) is scaled to a fixed size. Then, the key point detection algorithm was adopted to extract 68 key points of the face from the ROI region, and the image segmentation of the mask area is performed simultaneously. Finally, the correspondence between face landmarks and the mask area is used to assess whether the mask is worn correctly. The experimental results show that the average detection speed of this method in the natural environment is about 14FPS, the mAP (mean Average Precision) of whether to wear a mask is 66.34%, and the detection accuracy of whether to wear a mask is 96%, which can effectively meet the actual application requirements.

A multi-stage approach for high-precision measurement of cervical curvature in X-ray images

As people’s lifestyles change, cervical spondylosis affects people more often. Early diagnosis is thus crucial in the management of cervical spondylosis. Cervical curvature abnormalities may serve as an objective diagnostic standard in radiographs, commonly assessed by the Cobb angle measurement in clinical practice. The method involves the clinician marking four corners of each vertebra and then drawing lines to calculate the Cobb angle using specific rules. The whole process takes a long time and mainly relies on subjective experience. In this paper, we propose an automatic multi-stage method for measuring cervical spine Cobb angle, which is referred to as MSM-CSCA that can perform the precise measurement of cervical curvature. In Stage 1, the region of interest (ROI) of the cervical spine is carefully cropped automatically. In Stage 2, an improved CA-UNet is used to achieve detailed segmentation of the cervical spine region for the ROI region, which extracts features from the edges of the vertebrae and integrates them with the original ROI to highlight the vertebrae edges more distinctly. At Stage 3, the integrated features are input into a dual-branch landmark detection network, with segmentation and detection branches. Within this stage, the segmentation branch convolutional results are fed back into the detection branch after feature augmentation to provide a more accurate detection area. Finally, the accurately identified cervical landmarks from this network are used to calculate the Cobb angle. Experimental results show that the proposed method achieves SMAPE 15.05% and MAE 3.19 on X-ray images of the cervical spine, significantly better than the other benchmark methods. The proposed method can provide clinicians with an efficient, accurate, and reliable way for the estimation of cervical curvature.

TW3-based ROI detection and classification using a chaotic ANN and DNN-EVGO architecture for an automated bone age assessment on hand X-ray images

ABSTRACT Bone illnesses arise at a young age itself, so bone age assessment (BAA) is primarily utilized in paediatrics to identify their growth. Several BAA-related methods are employed to determine bone maturity; however, they do not give accuracy and the rate of error increases. To overcome this issue, in this manuscript, TW3-based Region of Interest (ROI) identification and classification with Chaotic ANN (CANN), as well as Deep Neural Network optimized with Evolved Gradient Direction optimizer (EVGO) architecture, is proposed for an Automated BAA classification. In this, Hand X-Ray images are taken from the Digital Hand Atlas (DHA) dataset and the Radiological Society of North America (RSNA) database. Here, Tanner-Whitehouse (TW3) and Shallow convolution neural network (SCNN) is utilized for extracting the ROI regions such as radius, ulna, and short bones (RUS) from the wrist region of the left hand. After that, the extracted ROI feature data are given to CANN for predicting BAA. Then, the predicted data are classified using the DNN-EVGO network. The bone age evaluation is then classified depending on maturity phases. The proposed approach is activated in MATLAB. Simulation outcomes of TW3- SCNN- CANN- DNN-EVGO- BAA methods for DHA database attain higher accuracy of 45.75%, 37.64%, 24.64% when analysed to the existing models, such as Faster Region-Convolutional Neural network oriented feature learning with optimal trained Recurrent Neural Network for BAA for paediatrics (TW3-F-R-CNN-AF-SFO), Multi-objective segmentation approach for BAA under parameter tuning- U-net architecture, and TW3-based fully automated bone age assessment system using deep neural networks (TW3-F-R-CNN-VGGNet-CNN-BAA).

Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect.

In women with biopsy-proven breast cancer, histologically normal areas of the parenchyma have shown molecular similarity to the tumor, supporting a potential cancer field effect. The purpose of this work was to investigate relationships of human-engineered radiomic and deep learning features between regions across the breast in mammographic parenchymal patterns and specimen radiographs. This study included mammograms from 74 patients with at least 1 identified malignant tumor, of whom 32 also possessed intraoperative radiographs of mastectomy specimens. Mammograms were acquired with a Hologic system and specimen radiographs were acquired with a Fujifilm imaging system. All images were retrospectively collected under an Institutional Review Board-approved protocol. Regions of interest (ROI) of were selected from three regions: within the identified tumor, near to the tumor, and far from the tumor. Radiographic texture analysis was used to extract 45 radiomic features and transfer learning was used to extract 20 deep learning features in each region. Kendall's Tau-b and Pearson correlation tests were performed to assess relationships between features in each region. Statistically significant correlations in select subgroups of features with tumor, near to the tumor, and far from the tumor ROI regions were identified in both mammograms and specimen radiographs. Intensity-based features were found to show significant correlations with ROI regions across both modalities. Results support our hypothesis of a potential cancer field effect, accessible radiographically, across tumor and non-tumor regions, thus indicating the potential for computerized analysis of mammographic parenchymal patterns to predict breast cancer risk.

Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm.

Intracranial hemorrhage detection in 3D Computed Tomography (CT) brain images has gained more attention in the research community. The major issue to deal with the 3D CT brain images is scarce and hard to obtain the labelled data with better recognition results. To overcome the aforementioned problem, a new model has been implemented in this research manuscript. After acquiring the images from the Radiological Society of North America (RSNA) 2019 database, the region of interest (RoI) was segmented by employing Otsu's thresholding method. Then, feature extraction was performed utilizing Tamura features: directionality, contrast, coarseness, and Gradient Local Ternary Pattern (GLTP) descriptors to extract vectors from the segmented RoI regions. The extracted vectors were dimensionally reduced by proposing a modified genetic algorithm, where the infinite feature selection technique was incorporated with the conventional genetic algorithm to further reduce the redundancy within the regularized vectors. The selected optimal vectors were finally fed to the Bi-directional Long Short Term Memory (Bi-LSTM) network to classify intracranial hemorrhage sub-types, such as subdural, intraparenchymal, subarachnoid, epidural, and intraventricular. The experimental investigation demonstrated that the Bi-LSTM based modified genetic algorithm obtained 99.40% sensitivity, 99.80% accuracy, and 99.48% specificity, which are higher compared to the existing machine learning models: Naïve Bayes, Random Forest, Support Vector Machine (SVM), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) network.

Dual-Channel Capsule Generative Adversarial Network Optimized with Golden Eagle Optimization for Pediatric Bone Age Assessment from Hand X-Ray Image

Bone age assessment (BAA) is mainly utilized for detecting the growth of pediatrics because a large number of bone diseases occur at young age. Several algorithms related to BAAs were used for detecting the maturity of bones, but it does not provide sufficient accuracy, and also increased the error rate. To deal with these problems, the dual-channel capsule generative adversarial network optimized with Golden eagle optimization (GEO) is proposed in this paper for pediatric BAA from hand X-ray image (DCCGAN-GEO-BAA-HX-ray). Initially, the input hand X-ray imageries are collected from the dataset of Radiological Society of North America (RSNA) pediatric bone age (BA). Then, region of interest (RoI) of input hand X-ray imageries is segmented based on Tsallis entropy-based multilevel 3D Otsu thresholding (TE-3D-Otsu). Here, TE-3D-Otsu method segments the RoI region of wrist, thumb, middle finger, little finger, which enhance the classification accuracy. Moreover, the segmented RoI is given to DCCGAN that predicts the BAA. Generally, the DCCGAN does not reveal any adoption of optimization methods to scale the optimum parameters to ensure accurate classification. Therefore, GEO is used for optimizing the weight parameters of DCCGAN. The proposed DCCGAN-GEO-BAA-HX-ray method is executed in MATLAB and its performance is examined under performance metrics such as accuracy, precision, sensitivity, F-scores, specificity, concordance correlation coefficient (CCC) and computational time. Finally, the proposed DCCGAN-GEO-BAA-HX-ray approach attains 14.68%, 7.142%, 9.23% and 4.65% higher accuracy, 38.18%, 12.02%, 11.56% and 7.59% lower computational time is compared with existing FRCNN-AF-SFO-BAA-HX-ray, DCNN-W-CTO-BAA-HX-ray, CNN-MLP-BAA-HX-ray and CNN-BAA-HX-ray methods.

AR-DIC: A Novel Automatic Optical Strategy for Displacement Measurement of the Curved-in-Place Pipe Under Dynamic Fracturing

Curved-In-Place Pipe (CIPP) technology is widely used in urban underground pipeline repair nowadays. However, the lack of automatic global displacement acquisition method makes it still quite challenging to effectively obtain the detailed displacement characteristics of the CIPP pipes under dynamic loads. Herein, we propose a cascade strategy named automatic speckle region extraction digital image correlation (AR-DIC) to achieve the dynamic, efficient and accurate displacement estimation of the CIPP pipelines, so as to determine the variation characters of the CIPP pipelines under dynamic loads. In the first stage, an improved automatic DeepLabv3+ neural network (IA-DeepLabv3+) is meticulously designed to extract the region of interest (ROI) of the CIPP pipeline speckle automatically. A dataset containing the cluttered background and the real experimental speckle are also generated to train the model parameters. Note that this is the first time an automated ROI extraction method has been proposed in CIPP, even in the DIC field. In the second stage, the digital image correlation based on scale-invariant feature transform (SIFT-DIC) algorithm is adopted to calculate the extracted ROI and get the displacement field. Finally, the proposed method is tested on both the simulated and experimental datasets. The experimental results confirm that our method can not only extract the speckle ROI region accurately, but also achieve high-precision calculation of the displacement field.

Test-retest reliability of a finger-tapping fMRI task in a healthy population.

Measuring brain activity during functional MRI (fMRI) tasks is one of the main tools to identify brain biomarkers of disease or neural substrates associated with specific symptoms. However, identifying correct biomarkers relies on reliable measures. Recently, poor reliability was reported for task-based fMRI measures. The present study aimed to demonstrate the reliability of a finger-tapping fMRI task across two sessions in healthy participants. Thirty-one right-handed healthy participants aged 18-60 years took part in two MRI sessions 3 weeks apart during which we acquired finger-tapping task-fMRI. We examined the overlap of activations between sessions using Dice similarity coefficients, assessing their location and extent. Then, we compared amplitudes calculating intraclass correlation coefficients (ICCs) in three sets of regions of interest (ROIs) in the motor network: literature-based ROIs (10-mm-radius spheres centred on peaks of an activation likelihood estimation), anatomical ROIs (regions as defined in an atlas) and ROIs based on conjunction analyses (superthreshold voxels in both sessions). Finger tapping consistently activated expected regions, for example, left primary sensorimotor cortices, premotor area and right cerebellum. We found good-to-excellent overlap of activations for most contrasts (Dice coefficients: .54-.82). Across time, ICCs showed large variability in all ROI sets (.04-.91). However, ICCs in most ROIs indicated fair-to-good reliability (mean = .52). The least specific contrast consistently yielded the best reliability. Overall, the finger-tapping task showed good spatial overlap and fair reliability of amplitudes on group level. Although caution is warranted in interpreting correlations of activations with other variables, identification of activated regions in response to a task and their between-group comparisons are still valid and important modes of analysis in neuroimaging to find population tendencies and differences.

Dynamic Characteristic Monitoring of Wind Turbine Structure Using Smartphone and Optical Flow Method

The dynamic characteristics of existing wind turbine structures are usually monitored using contact sensors, which is not only expensive but also time-consuming and laborious to install. Recently, computer vision technology has developed rapidly, and monitoring methods based on cameras and UAVs (unmanned aerial vehicles) have been widely used. However, the high cost of UAVs and cameras make it difficult to widely use them. To address this problem, a target-free dynamic characteristic monitoring method for wind turbine structures using portable smartphone and optical flow method is proposed by combining optical flow method with robust corner feature extraction in ROI (region of interest). Firstly, the ROI region clipping technology is introduced after the structural vibration video shooting, and the threshold value is set in the ROI to obtain robust corner features. The sub-pixel displacement monitoring is realized by combining the optical flow method. Secondly, through three common smartphone shooting state to monitor the structural displacement, the method of high pass filtering combined with adaptive scaling factor is used to effectively eliminate the displacement drift caused by the two shooting states of standing and slightly walking, which can meet the requirements of structural dynamic characteristics monitoring. After that, the structural displacement is monitored by assembling the telephoto lens on the smartphone. The accuracy of displacement monitored by assembling the telephoto lens on the smartphone is investigated. Finally, the proposed monitoring method is verified by the shaking table test of the wind turbine structure. The results show that the optical flow method, combined with smartphones, can accurately identify the dynamic characteristics of the wind turbine structure, and the smartphone equipped with a telephoto lens is more conducive to achieving low-cost wind turbine structure dynamic characteristics monitoring. This research can provide a reference for evaluating the condition of wind turbine structures.

Reversible data hiding with enhancing contrast and preserving brightness in medical image

With the development of telemedicine and medical sharing, private information which stored in the cloud may be disclosed. In order to protect the private information of patients and improve the quality of medical image further, there are some researches of reversible data hiding (RDH) based on contrast enhancement in medical image. However, the existing methods may cause the embedded medical image to be over-enhanced when increasing the embedding capacity. With the intention of protecting the medical image stored on a semi-trusted server, a novel reversible data hiding with enhancing contrast and preserving brightness is proposed in this paper. The proposed algorithm separates medical image into regions of interest (ROI) and regions of non-interest (NROI) firstly. Then through pre-processing the bins in the histogram of ROI regions to get better contrast stretching effect. Thereafter, stretch the contrast of ROI region and embed secret message with brightness preservation. Experimental results show that the proposed method can not only enhance the ROI region’s contrast but also preserve the brightness of the embedded images when compared with traditional methods.

Resource optimized Region Based Image Encryption Using Chaotic Maps

Securing medical images becomes a major concern to avoid leaking confidential data. This problem motivated the development of many low computational complexity methods to encrypt these medical images. In this research work, block cipher-based region of interest medical image encryption with multiple maps is proposed. Primarily, region of interest (ROI) regions and region of background (ROB) are extracted with the help of Laplacian edge detection operator. Further, important ROI regions are permuted in a circular fashion with the help of Arnold cat map and angle value. Then permuted ROI part is encrypted using the duffling system and unimportant regions are unchanged. The advantage of the work is that it encrypts only selected/important parts and that will achieve fast execution speed and reduction in computation complexity. The approach presented here enables the storage and transmission of medical image data within an open network. These results show that the security in the proposed method is much better than many chaotic encryption algorithms proposed in the recent times.

KUALITAS CITRA SIDIK TULANG DENGAN PEMBERIAN DOSIS RADIOFARMAKA 99mTc-MDP BERDASARKAN BERAT BADAN DI INSTALASI KEDOKTERAN NUKLIR RS HASAN SADIKIN BANDUNG

A bone scan procedure using radiopharmaceutical 99mTc-MDP (Methylenediphosphonate) is an examination to help establish diagnostics in detecting metastases (spread) of cancer cells to bone. The dose given in this examination was according to the instructions from the Europe Association of Nuclear Medicine (EANM), the average dose given was 500MBq and also based on the guidelines in the attachment to the Regulation of the Head of Bapeten No. 17 of 2012 was 700 MBq. Justification for giving this dose will undoubtedly affect the acceptance of the exposure dose by the patient himself. On the other hand, giving a minimal dose sometimes creates doubts and concerns about the final result or poor image quality. Fulfillment of this image quality can be seen or calculated using ratio assessment. The results of this ratio assessment will show good image quality if it meets the target to background ratio results, which is the accumulation in the primary and background objects > from 5:1. The result of the intensity ratio is obtained from the ROI (Region of Interest) in the lumbar spine divided by ROI (Region of Interest) in the non-bone (abdominal area), which is considered the background. Giving a low dose is expected so that the exposure dose is small, but the picture results are still fulfilled. Carried out the research by providing a dose of Bone scan examination based on the patient's body weight of 10 MBq/kg, then calculating the Target Background to Ratio on the results of scans in the lumbar region 1-5. Development of the research obtained the number of patients as many as 51 patients with three people removed from the study so that the number of patients who took data was 48 patients consisting of 38 women and ten men. The results of the calculation included 33 patients who met the ratio results (>5), while 15 patients still did not meet the image quality criteria (<5). The Conclusion of the ratio that meets the value >5 is 69%, and those who do not meet the requirements are 31%. Keywords: Bone Scan, Ratio Results, 99mTc MDP

A Combined Approach for Accurate and Accelerated Teeth Detection on Cone Beam CT Images.

Teeth detection and tooth segmentation are essential for processing Cone Beam Computed Tomography (CBCT) images. The accuracy decides the credibility of the subsequent applications, such as diagnosis, treatment plans in clinical practice or other research that is dependent on automatic dental identification. The main problems are complex noises and metal artefacts which would affect the accuracy of teeth detection and segmentation with traditional algorithms. In this study, we proposed a teeth-detection method to avoid the problems above and to accelerate the operation speed. In our method, (1) a Convolutional Neural Network (CNN) was employed to classify layer classes; (2) images were chosen to perform Region of Interest (ROI) cropping; (3) in ROI regions, we used a YOLO v3 and multi-level combined teeth detection method to locate each tooth bounding box; (4) we obtained tooth bounding boxes on all layers. We compared our method with a Faster R-CNN method which was commonly used in previous studies. The training and prediction time were shortened by 80% and 62% in our method, respectively. The Object Inclusion Ratio (OIR) metric of our method was 96.27%, while for the Faster R-CNN method, it was 91.40%. When testing images with severe noise or with different missing teeth, our method promises a stable result. In conclusion, our method of teeth detection on dental CBCT is practical and reliable for its high prediction speed and robust detection.

Welding Defects Detection in Radiographic Images Using an Improved Denoising Technique Combined with an Enhanced Chan-Vese Model

The detection of welding defects is becoming an important operation in the industry and the field of non-destructive testing. Among the most used techniques in the detection of weld defects, it is radiography. The radiographic images acquired are generally of low contrast, poor quality, and uneven lighting. Therefore, the detection of welding defects becomes a difficult task. In this work, a new hybrid approach based on the combination of several techniques is proposed. It consists of three stages: firstly, we define the region of interest (ROI). Secondly, a preprocessing operation based on an improved version of denoising by soft thresholding of wavelet coefficients and an optimized threshold is applied to improve the image quality (noise reduction, contrast enhancement). Thirdly, an enhanced Chan-Vese model is proposed to segment the denoised ROI region. This enhanced model is based on the choice of a cluster obtained by the Fuzzy C-Mean algorithm (FCM) as the initial contour. The proposed approach is applied to the various radiographic welding images from the GDxray database to extract the characteristics of the welding defects. The results obtained clearly show the effectiveness of the proposed approach compared to conventional techniques.