Regions Of Interest In Images Research Articles

A Rapid Head Organ Localization System Based on Clinically Realistic Images: A 3D Two Step Progressive Registration Method with CVH Anatomical Knowledge Mapping.

Rapid localization of ROI (Region of Interest) for tomographic medical images (TMIs) is an important foundation for efficient image reading, computer-aided education, and well-informed rights of patients. However, due to the multimodality of clinical TMIs, the complexity of anatomy, and the deformation of organs caused by diseases, it is difficult to have a universal and low-cost method for ROI organ localization. This article focuses on actual concerns of TMIs from medical students, engineers, interdisciplinary researchers, and patients, exploring a universal registration method between the clinical CT/MRI dataset and CVH (Chinese Visible Human) to locate the organ ROI in a low-cost and lightweight way. The proposed method is called Two-step Progressive Registration (TSPR), where the first registration adopts "eye-nose triangle" features to determine the spatial orientation, and the second registration adopts the circular contour to determine the spatial scale, ultimately achieving CVH anatomical knowledge automated mapping. Through experimentation with representative clinical TMIs, the registration results are capable of labeling the ROI in the images well and can adapt to the deformation problem of ROI, as well as local extremum problems that are prone to occur in inter-subject registration. Unlike the ideal requirements for TMIs' data quality in laboratory research, TSPR has good adaptability to incomplete and non-thin-layer quality in real clinical data in a low-cost and lightweight way. This helps medical students, engineers, and interdisciplinary researchers independently browse images, receive computer-aided education, and provide patients with better access to well-informed services, highlighting the potential of digital public health and medical education.

Early diagnosis of Cladosporium fulvum in greenhouse tomato plants based on visible/near-infrared (VIS/NIR) and near-infrared (NIR) data fusion

Plant diseases can inflict varying degrees of damage on agricultural production. Therefore, identifying a rapid, non-destructive early diagnostic method is crucial for safeguarding plants. Cladosporium fulvum (C. fulvum) is one of the major diseases in tomato growth. This work presents a method of data fusion using two hyperspectral imaging systems of visible/near-infrared (VIS/NIR) and near-infrared (NIR) spectroscopy for the early diagnosis of C. fulvum in greenhouse tomatoes. First, hyperspectral images of samples at health and different times of infection were collected. The average spectral data of the image regions of interest were extracted and preprocessed for subsequent spectral datasets. Then different classification models were established for VIS/NIR and NIR data, optimized through various variable selection and data fusion methods. The principal component analysis-radial basis function neural network (PCA-RBF) model established using low-level data fusion achieved optimal results, achieving accuracies of 100% and 99.3% for calibration and prediction, respectively. Moreover, both the macro-averaged F1 (Macro-F1) values reached 1, and the geometric mean (G-mean) values reached 1 and 1, respectively. The results indicated that it was feasible to establish a PCA-RBF model by using the hyperspectral technique with low-level data fusion for the early detection of C. fulvum in greenhouse tomatoes.

A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease

Recently, Alzheimer’s disease (AD) is one of the common neurodegenerative disorders, which primarily occurs in old age. Structural magnetic resonance imaging (sMRI) is an effective imaging technique used in clinical practice for determining the period of AD patients. An efficient deep learning framework is proposed in this paper for AD detection, which is inspired from clinical practice. The proposed deep learning framework significantly enhances the performance of AD classification by requiring less processing time. Initially, in the proposed framework, the sMRI images are acquired from a real-time dataset and two online datasets including Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL), and Alzheimer’s Disease Neuroimaging Initiative (ADNI). Next, a fuzzy-based superpixel-clustering algorithm is introduced to segment the region of interest (RoI) in sMRI images. Then, the informative deep features are extracted in segmented RoI images by integrating the probabilistic local ternary pattern (PLTP), ResNet-50, and Visual Geometry Group (VGG)-16. Furthermore, the dimensionality reduction is accomplished by through the modified gorilla troops optimizer (MGTO). This process not only enhances the classification performance but also diminishes the processing time of the capsule network (CapsNet), which is employed to classify the classes of AD. In the MGTO algorithm, a quasi-reflection-based learning (QRBL) process is introduced for generating silverback’s quasi-refraction position for further improving the optimal position’s quality. The proposed fuzzy based superpixel-clustering algorithm and MGTO-CapsNet model obtained a pixel accuracy of 0.96, 0.94, and 0.98 and a classification accuracy of 99.88%, 96.38%, and 99.94% on the ADNI, real-time, and AIBL datasets, respectively.

Active reinforcement learning based approach for localization of target ROI (region of interest) in cervical cell images

Active reinforcement learning based approach for localization of target ROI (region of interest) in cervical cell images

Assessing Residual Gastric Fluid Volume after Administering Diluted Oral Contrast until One Hour Prior to Anesthesia in Children: An Observational Cohort Study.

Background: Gastric fluid volume has been used as a surrogate marker for pulmonary aspiration risk in studies evaluating fasting protocol safety. This study measured residual gastric fluid volume in children using a protocol in which diluted oral contrast medium was administered up until one hour before anesthesia. Methods: This was a single-center prospective observational cohort trial of 70 children for elective abdominal/pelvic computed tomography (CT). Imaging was performed after diluted enteral contrast medium administration, beginning two hours before and ending at least one hour before induction. For each patient, gastric fluid volume was calculated using an image region of interest. The primary outcome measure was gastric fluid volume measured using the computed tomography image. Results: The median time from the end of contrast administration to imaging was 1.5 h (range: 1.1 to 2.2 h). Residual gastric volume, measured using CT was <0.4 mL/Kg in 33%; ≥0.4 mL/Kg in 67%; and ≥1.5 mL/Kg in 44% of patients. Residual gastric volumes measured using CT and aspiration were moderately correlated (Spearman's correlation coefficient = 0.41, p = 0.0003). However, the median residual gastric volume measured using CT (1.17, IQR: 0.22 to 2.38 mL/Kg) was higher than that of aspiration (0.51, IQR: 0 to 1.58 mL/Kg, p = 0.0008 on differences in paired measures). Three cases of vomiting were reported. No evidence of pulmonary aspiration was identified. Conclusions: Children who receive large quantities of clear fluid up to one hour before anesthesia can have a significant gastric residual volume.

Intelligent identification system of wild animals image based on deep learning in biodiversity conservation law

This study aims to overcome the impact of complex environmental backgrounds on the recognition of wildlife in monitoring images, thereby exploring the role of a deep learning-based intelligent wildlife recognition system in biodiversity conservation. The automatic identification of wildlife images is conducted based on convolutional neural networks (CNNs). Target detection technology, based on regression algorithms, is initially employed to extract Regions of Interest (ROI) containing wildlife from images. The wildlife regions in monitoring images are detected, segmented, and converted into ROI images. A dual-channel network model based on Visual Geometry Group 16 (VGG16) is implemented to extract features from sample images. Finally, these features are input into a classifier to achieve wildlife recognition. The proposed optimized model demonstrates superior recognition performance for five wildlife species, caribou, lynx, mule deer, badger, and antelope, compared to the dual-channel network model based on VGG16. The optimized model achieves a Mean Average Precision (MAP) of 0.714, with a maximum difference of 0.145 compared to the other three network structures, affirming its effectiveness in enhancing the accuracy of automatic wildlife recognition. The model effectively addresses the issue of low recognition accuracy caused by the complexity of background information in monitoring images, achieving high-precision recognition and holding significant implications for the implementation of biodiversity conservation laws.

Abstract 6245: Missed opportunity at the subcellular level: Enhancing the utility of cellular imaging modality in spatial transcriptomic profiling of tumor tissues

Abstract Mini-bulk or single-spot spatial transcriptomic technologies, such as GeoMX Digital Spatial Profiler (DSP), have revolutionized our ability to probe spatial heterogeneity and examine transcripts at a subcellular level. In cancer research, this level of granularity is crucial for uncovering the distinct gene expression signatures within specific compartments or structures of interest such as tumor, immune, stroma, and tertiary lymphoid structures. However, in most GeoMX DSP studies, the spatial information obtained from multiplex immunofluorescence imaging has been primarily used for identifying the regions of interest (ROI), rather than as an integral part of the downstream transcriptomic data interpretation. To fix this missed opportunity, we developed a machine learning based analytical framework to fully leverage the rich spatial context provided by the in situ imaging modality. The framework has two main functionalities. (1) ROI cropping and cell segmentation: our framework enables the automated cropping of ROI images, followed by a cell segmentation process utilizing a multi-color-optimized deep learning model pre-trained based on the TissueNet. (2) Cell typing and integrative spatial analysis: the pipeline extracts cell-level morphological features (e.g. cell size and circularity), and AI-driven characteristics. These features are subsequently employed for cell typing and the quantification of cell mixture. The framework is equipped to perform a series of advanced spatial analyses, such as spatial metrics calculation and consensus clustering analysis with the matched gene expression data. To validate the framework, we conducted GeoMX DSP analyses on samples obtained from bladder cancer and upper tract urothelial carcinoma, encompassing 56 ROIs annotated by pathologists. Our results demonstrated high accuracy, with the calculated tumor-immune proportions aligning precisely with the original annotations. We further compared the cell deconvolution results of our framework with those obtained using SpatialDecon. Consistency was noted across both methods, with a high correlation reaching 0.90. However, we observed that SpatialDecon tends to underestimate tumor purity in tumor core regions, with discrepancies as large as 0.30, and overestimating tumor purity in stromal regions. Additionally, analysis of ROIs in tumor margins revealed that SpatialDecon consistently overestimates the overall proportions of immune cells. In conclusion, our results underscore the importance of integrating in situ imaging with subcellular-level spatial transcriptomics for a more accurate and reliable analysis of tumor tissues. Our approach provides critical insights into the tumor microenvironment and cellular interactions, with significant implications for both research and clinical applications in oncology. Citation Format: Xiaofei Song, Xiaoqing Yu, Carlos M. Moran-Segura, G Daniel Grass, Roger Li, Xuefeng Wang. Missed opportunity at the subcellular level: Enhancing the utility of cellular imaging modality in spatial transcriptomic profiling of tumor tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6245.

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.

A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack

Palmprint recognition (PPR) has recently garnered attention due to its robustness and accuracy. Many PPR methods rely on preprocessing the region of interest (ROI). However, the emergence of ROI attacks capable of generating synthetic ROI images poses a significant threat to PPR systems. Despite this, ROI attacks are less practical since PPR systems typically take hand images as input rather than just the ROI. Therefore, there is a pressing need for a method that specifically targets the system by composing hand images. The intuitive approach involves embedding an ROI into a hand image, a comparatively simpler process requiring less data than generating entirely synthetic images. However, embedding faces challenges, as the composited hand image must maintain a consistent color and texture. To overcome these challenges, we propose a training-free, end-to-end hand image composition method incorporating ROI harmonization and palm blending. The ROI harmonization process iteratively adjusts the ROI to seamlessly integrate with the hand using a modified style transfer method. Simultaneously, palm blending employs a pretrained inpainting model to composite a hand image with a continuous transition. Our results demonstrate that the proposed method achieves a high attack performance on the IITD and Tongji datasets, with the composited hand images exhibiting realistic visual quality.

Low-cost single-shot complex optical field imaging with a simplified aperture

We report a new computational imaging methodology for complex optical field based on a single-frame diffraction intensity pattern acquisition with a simplified aperture and an extremely low-cost industrial machine vision camera. The current design of the apertures for the recovery of complex optical field was suffering from specific coding associated with the use of spatial light modulator or digital micromirror device, where it is difficult to make the optical implementations compact and cost-effective. In this work, we demonstrate that a simplified aperture without any pattern-like coding is efficient to establish the physical constraint for computational reconstruction. The aperture attached to the object defines the imaging region of interest (ROI) with coherent trans-illumination, and only one snapshot of the diffraction intensity pattern is required for recovering the ROI's complex field. Proof-of-concept numerical simulations and optical experiments have been carried out to validate the feasibility, noise robustness, resolution, and potential biological imaging applications of the proposed computational method.

LEACS: a learnable and efficient active contour model with space-frequency pooling for medical image segmentation.

Diseases can be diagnosed and monitored by extracting regions of interest (ROIs) from medical images. However, accurate and efficient delineation and segmentation of ROIs in medical images remain challenging due to unrefined boundaries, inhomogeneous intensity and limited image acquisition. To overcome these problems, we propose an end-to-end learnable and efficient active contour segmentation model, which integrates a global convex segmentation (GCS) module into a light-weighted encoder-decoder convolutional segmentation network with a multiscale attention module (ED-MSA). The GCS automatically obtains the initialization and corresponding parameters of the curve deformation according to the prediction map generated by the ED-MSA, while provides the refined object boundary prediction for ED-MSA optimization. To provide precise and reliable initial contour for the GCS, we design the space-frequency pooling operation layers in the encoder stage of ED-MSA, which can effectively reduce the number of iterations of the GCS. Beside, we construct ED-MSA using the depth-wise separable convolutional residual module to mitigate the overfitting of the model. The effectiveness of our method is validated on four challenging medical image datasets. Code is here:https://github.com/Yang-fashion/ED-MSA_GCS.

Crop Residue Cover Percentage Estimation from RGB Images Using Transfer Learning and Ensemble Ordinal Regression

Highlights A transfer learning strategy improved residue estimates from high-resolution RGB imagery. The best method used probabilistic estimates of expert classifiers to estimate residue cover. This research confirms the utility of RGB imagery to quantify residue cover in agricultural fields. Abstract. Plant residue on the soil surface increases the sustainability of food and fiber production in agricultural systems. Automated assessments of residue cover based on imagery have the potential to reduce labor and human bias associated with in-field measurements. We evaluate the capacity of a transfer learning strategy to improve the determination of residue level from high-resolution RGB images. The imagery for the study was collected from 88 field locations in 40 row crop fields in five Missouri counties between mid-April and early July in 2018 and 2019. At each field location, 50 contiguous 0.3 m × 0.2 m region of interest (ROI) images (ground sampling distance of 0.014 cm pixel--1) were extracted from imagery, resulting in a dataset of 4,400 ROI images; 3,000 were used for cross-validation and training (data collected in 2018) and 1,400 were used for testing (data collected in 2019). The percentage residue for each ROI image (ground truth) was determined by a bullseye grid method (n = 100). Features were extracted from ROI images using the VGGNet-16 CNN model, a pre-trained convolutional neural network model. We extracted 1,472 features per ROI using a global averaging and pooling strategy. The optimum feature set was identified using recursive feature elimination using a support vector machine (RFE-SVM). To estimate crop residue percentage using selected features, expert two-class SVMs were trained to separate adjacent levels of residue cover, where the rationale of the ensemble was to allow each of the two-class SVMs to find the hyperplanes that maximize the margin between the corresponding two consecutive classes. Based on the distance of the samples to these hyperplanes, probabilistic estimates of the data-point belonging to the class were computed. With the combined knowledge of probabilistic estimates from each expert classifier, the percentage crop residue cover of each ROI image was calculated. We tested our approach with 3-, 4-, 5-, and 8-class problems, achieving the best results with the 8-class problem with r2 = 0.93 at the ROI level, r2 = 0.97 at the field-location level, and minimal bias in residue estimates in low residue conditions. These results are superior to other reported estimates of percent residue derived from imagery. This research confirms the utility of high-resolution RGB imagery to quantify residue cover in agricultural systems. Keywords: Convolutional neural network, Soil erosion, Support vector machine, Transfer learning.

Object Detection and Regression Based Visible Spectrophotometric Analysis: A Demonstration Using Methylene Blue Solution

This study investigates the estimation of the concentration of methylene blue solutions to understand if visible spectrophotometry could be performed using a smartphone and machine learning. The presented procedure consists of taking photos, detecting test tubes and sampling region of interest (ROI) with YOLOv5, finding the hue, saturation, value (HSV) code of the dominant color in the ROI, and regression. 257 photos were taken for the procedure. The YOLOv5 object detection architecture was trained on 928 images and the highest mAP@05 values were detected as 0.915 in 300 epochs. For automatic ROI sampling, the YOLOv5 detect.py file was edited. The trained YOLOv5 detected 254 out of 257 test tubes and extracted ROIs. The HSV code of the dominant color in the exported ROI images was determined and stored in a csv file together with the concentration values. Subsequently, 25 different regression algorithms were applied to the generated data set. The extra trees regressor was the most generalizing model with 99.5% training and 99.4% validation R2 values. A hyperparameter tuning process was performed on the extra trees regressor and a mixed model was created using the best 3 regression algorithms to improve the R2 value. Finally, all three models were tested on unseen data and the lowest MSE value was found in the untuned extra trees regressor and blended model with values of 0.10564 and 0.16586, respectively. These results prove that visible spectrophotometric analysis can be performed using the presented procedure and that a mobile application can be developed for this purpose.

Color image ROI encryption algorithm based on a novel 4D hyperchaotic system

The significance of safeguarding the security of image information has escalated significantly, owing to the exponential proliferation of digital images containing sensitive information being disseminated on the Internet. In this paper, we first propose a novel 4D hyperchaotic system and design a new image encryption algorithm in conjunction with the hyperchaotic system. The algorithm uses a split random swap permutation method to permute the image and combines the S-box to diffuse the image. To improve the diffusivity of this encryption algorithm, a cross-random diffusion method is designed to diffuse the image again. Then, we propose a region of interest (ROI) encryption scheme for images. This scheme can automatically identify irregular privacy targets in images and encrypt them. To ensure the security of the region of interest location information during transmission, the scheme compresses the location information of the privacy target using a run-length encoding technique and then embeds the compressed data into the ciphertext image using reversible steganography based on histogram shift. The experimental results and security analysis unequivocally demonstrate that the image encryption algorithm proposed in this paper exhibits robust resistance against a wide array of attacks, thereby ensuring a high level of security. Additionally, the devised image ROI encryption scheme effectively safeguards diverse privacy targets.

Analysis of the Slanted-Edge Measurement Method for the Modulation Transfer Function of Remote Sensing Cameras

The modulation transfer function (MTF) serves as a crucial technical index for assessing the imaging quality of remote sensing cameras, which is integral throughout their entire operational cycle. Currently, the MTF evaluation of remote sensing cameras primarily relies on the slanted-edge method. The factors influencing the slanted-edge method’s effectiveness are broadly classified into two categories: algorithmic factors and image factors. This paper innovatively comprehensively analyzes the influencing factors of the slanted-edge method and proposes an improved slanted-edge method to calculate the MTF testing method of remote sensing cameras, which is applied to the MTF testing of remote sensing cameras. Since the traditional algorithm can only be applied in the small angle situation, this paper proposes a new method of slanted-edge method test calculation based on the optimal oversampling rate (OSR) adaptive model of the slanted edge and uses simulation experiments to verify the reliability of the algorithm model through the deviation of the slanted-edge angle calculation and MTF measurement, and the results show that the algorithm improves the accuracy of the MTF measurement compared with the ISO-cos and OMINI-sine methods. Then, the effects of the slanted-edge angle, image region of interest (ROI), as well as image contrast and signal-to-noise ratio (SNR) on the accuracy of the MTF calculation by the slanted-edge method were quantitatively analyzed as the constraints of the slanted-edge method test. Based on the laboratory target experiment, the algorithm flow and various influencing factors obtained in the simulation stage are verified, and the experimental results are more consistent with the various test results obtained in the simulation stage. Consequently, the slanted-edge method introduced in this paper is applicable for future remote sensing camera MTF testing. This approach offers a valuable reference for on-orbit focusing, satellite operational condition monitoring, lifespan estimation, and image restoration.

Region of interest (ROI) extraction and crack detection for UAV-based bridge inspection using point cloud segmentation and 3D-to-2D projection

For digital-image-based bridge inspection tasks, images captured by camera-carrying unmanned aircraft vehicles (UAVs) usually contain both the region of interest (ROI) and the background. However, accurately detecting cracks in concrete surface images containing background information is challenging. To improve UAV-based bridge inspection, an image extraction and crack detection methodology is presented in this paper. First, a deep-learning-based semantic segmentation network RandLA-BridgeNet for large-scale bridge point clouds, which can facilitate 3D ROI extraction, is trained and tested. Second, an image ROI extraction method based on 3D-to-2D projection is presented to generate images containing only the ROI. Finally, a data-driven deep learning convolutional neural network (CNN) called the grid-based classification and box-based detection fusion model (GCBD) is utilized to identify cracks in the processed images. An experiment is conducted on highway bridge images to validate the presented methodology. The overall semantic segmentation and image ROI extraction accuracies are 97.0% and 98.9%, respectively. After ROI extraction, 47.9% of the grid cells, which represent background misrecognition, are filtered, greatly improving the crack identification accuracy.

Computed tomography and guidelines-based human-machine fusion model for predicting resectability of the pancreatic cancer.

The study aims to develop a hybrid machine learning model for predicting resectability of the pancreatic cancer, which is based on computed tomography (CT) and National Comprehensive Cancer Network (NCCN) guidelines. We retrospectively studied 349 patients. One hundred seventy-one cases from Center 1 and 92 cases from Center 2 were used as the primary training cohort, and 66 cases from Center 3 and 20 cases from Center 4 were used as the independent test dataset. Semi-automatic module of ITK-SNAP software was used to assist CT image segmentation to obtain three-dimensional (3D) imaging region of interest (ROI). There were 788 handcrafted features extracted for 3D ROI using PyRadiomics. The optimal feature subset consists of three features screened by three feature selection methods as the input of the SVM to construct the conventional radiomics-based predictive model (cRad). 3D ROI was used to unify the resolution by 3D spline interpolation method for constructing the 3D tumor imaging tensor. Using 3D tumor image tensor as input, 3D kernelled support tensor machine-based predictive model (KSTM), and 3D ResNet-based deep learning predictive model (ResNet) were constructed. Multi-classifier fusion ML model is constructed by fusing cRad, KSTM, and ResNet using multi-classifier fusion strategy. Two experts with more than 10years of clinical experience were invited to reevaluate each patient based on their CECT following the NCCN guidelines to obtain resectable, unresectable, and borderline resectable diagnoses. The three results were converted into probability values of 0.25, 0.75, and 0.50, respectively, according to the traditional empirical method. Then it is used as an independent classifier and integrated with multi-classifier fusion machine learning (ML) model to obtain the human-machine fusion ML model (HMfML). Multi-classifier fusion ML model's area under receiver operating characteristic curve (AUC; 0.8610), predictive accuracy (ACC: 80.23%), sensitivity (SEN: 78.95%), and specificity (SPE: 80.60%) is better than cRad, KSTM, and ResNet-based single-classifier models and their two-classifier fusion models. This means that three different models have mined complementary CECT feature expression from different perspectives and can be integrated through CFS-ER, so that the fusion model has better performance. HMfML's AUC (0.8845), ACC (82.56%), SEN (84.21%), SPE (82.09%). This means that ML models might learn extra information from CECT that experts cannot distinguish, thus complementing expert experience and improving the performance of hybrid ML models. HMfML can predict PC resectability with high accuracy.

A Segmentation Method of Serialized Human Body Slices Based on Matting Strategy and Skeleton Extraction.

In this paper, a semiautomatic image segmentation method for the serialized body slices of the Visible Human Project (VHP) is proposed. In our method, we first verified the effectiveness of the shared matting method for the VHP slices and utilized it to segment a single image. Then, to meet the need for the automatic segmentation of serialized slice images, a method based on the parallel refinement method and flood-fill method was designed. The ROI (region of interest) image of the next slice can be extracted by using the skeleton image of the ROI in the current slice. Utilizing this strategy, the color slice images of the Visible Human body can be continuously and serially segmented. This method is not complex but is rapid and automatic with less manual participation. The experimental results show that the primary organs of the Visible Human body can be accurately extracted.

MODAN: Multifocal Object Detection Associative Network for Maritime Horizon Surveillance

In maritime surveillance systems, object detection plays a crucial role in ensuring the security of nearby waters by tracking the movement of various objects, such as ships and aircrafts, that are found at sea, detecting illegal activities and preemptively countering or predicting potential risks. Using vision sensors such as cameras to monitor the sea can help to identify the shape, size, and color of objects, enabling the precise analysis of maritime situations. Additionally, vision sensors can monitor or track small ships that may escape radar detection. However, objects located at considerable distances from vision sensors have low resolution and are small in size, rendering their detection difficult. This paper proposes a multifocal object detection associative network (MODAN) to overcome these vulnerabilities and provide stable maritime surveillance. First, it searches for the horizon using color quantization based on K-means; then, it selects and partitions the region of interest (ROI) around the horizon using the ROI selector. The original image and ROI image, converted to high resolution through the Super-Resolution Convolutional Neural Network (SRCNN), are then passed to the near-field and far-field detectors, respectively, for object detection. The weighted box fusion removes duplicate detected objects and estimates the optimal object. The proposed network is more stable and efficient in detecting distant objects than existing single-object detection models. Through performance evaluations, the proposed network exhibited an average precision surpassing that of the existing single-object detection models by more than 7%, and the false detection rate was reduced by 59% compared to similar multifocal-based state-of-the-art detection methods.

Projection mapping system for laser speckle contrast image: feasibility study for clinical application.

Laser speckle contrast images (LSCIs) have been utilized to monitor blood flow perfusion. However, they have conventionally been observed on monitor screens, resulting in potential spatial mismatching between the imaging region of interest (IROI) and monitor screen. This study proposes a projection mapping (PM) system for LSCIs (PMS_LSCI) that projects LSCIs to directly observe the blood flow perfusion in the IROI. The PMS_LSCI consists of a camera, imaging optics, a laser projector, and graphic user interface software. The spatial matching in the regions of interest was performed by adjusting the software screen of the LSCI in the IROI and evaluated by conducting in-vitro and in-vivo studies. An additional in-vivo study was performed to investigate the feasibility of real-time PM of the LSCI. The spatial mismatching in the regions of interest was ranged from 2.74% to 6.47% depending on the surface curvature. The PMS_LSCI could enable real-time PM of LSCI at four different blood flow states depending on blood pressure. The PMS_LSCI projects the LSCI in the IROI by interacting with a projector instead of the monitor screen. The PMS_LSCI presented clinical feasibility in the in-vitro and in-vivo studies.