Image Rotation Research Articles

CSSF-BL360: Color Segmentation-Based Bolt Localization and 0~360° Full-Scale Loosening Angle Detection in Single-Frame Images

Introduction: Bolt loosening detection plays a crucial role in ensuring structural safety. Existing research using deep learning methods has the drawback of being applicable to limited scenarios and typically requires at least two frames of images for comparison in subsequent angle detection, with a limited range of detectable angles. Method: In this study, a color segmentation method was first used to separate the red square gasket placed between the bolt and the base, thereby locating the bolt area; then, the image was perspective-corrected using the four corners of the red square gasket; finally, the red mark bars on the bolt and base were separated using the same color segmentation method, and the geometric moments of the connected domains of the mark bars were processed, combined with vector processing techniques, to achieve quantified detection of bolt loosening angles within 0~360 degrees using a single frame image. In the verification experiments, 150 images were collected from different shooting angles and distances, and the bolt areas to be tested were located using the color segmentation method, all with an IOU (Intersection over Union) greater than 0.9317. Subsequent experiments set different variables, including different shooting distances, bolt loosening angles, perspective angles, lighting conditions, bolt types, and image rotation angles. Result: The results demonstrated that the method presented in this study could accurately detect bolt loosening angles in multiple scenarios. Conclusion: Hence, it is capable of measuring loosening angles within the range of 0~360 degrees using only a single frame image. result: In the verification experiments, 150 images were collected from different shooting angles and distances, and the bolt areas to be tested were located using the color segmentation method, all with an IOU(Intersection over Union) greater than 0.9317. Subsequent experiments set different variables, including different shooting distances, bolt loosening angles, perspective angles, lighting conditions, bolt types, and image rotation angles.

Multi-Temporal Snow-Covered Remote Sensing Image Matching via Image Transformation and Multi-Level Feature Extraction

To address the challenge of image matching posed by significant modal differences in remote sensing images influenced by snow cover, this paper proposes an innovative image transformation-based matching method. Initially, the Pix2Pix-GAN conversion network is employed to transform remote sensing images with snow cover into images without snow cover, reducing the feature disparity between the images. This conversion facilitates the extraction of more discernible features for matching by transforming the problem from snow-covered to snow-free images. Subsequently, a multi-level feature extraction network is utilized to extract multi-level feature descriptors from the transformed images. Keypoints are derived from these descriptors, enabling effective feature matching. Finally, the matching results are mapped back onto the original snow-covered remote sensing images. The proposed method was compared to well-established techniques such as SIFT, RIFT2, R2D2, and ReDFeat and demonstrated outstanding performance. In terms of NCM, MP, Rep, Recall, and F1-measure, our method outperformed the state of the art by 177, 0.29, 0.22, 0.21, and 0.25, respectively. In addition, the algorithm shows robustness over a range of image rotation angles from −40° to 40°. This innovative approach offers a new perspective on the task of matching multi-temporal snow-covered remote sensing images.

Deep learning-based binary classification of beta-amyloid plaques using 18F florapronol PET.

This study aimed to investigate a deep learning model to classify amyloid plaque deposition in the brain PET images of patients suspected of Alzheimer's disease. A retrospective study was conducted on patients who were suspected of having a mild cognitive impairment or dementia, and brain amyloid 18F florapronol PET/computed tomography images were obtained from 2019 to 2022. Brain PET images were visually assessed by two nuclear medicine specialists, who classified them as either positive or negative. Image rotation was applied for data augmentation. The dataset was split into training and testing sets at a ratio of 8 : 2. For the convolutional neural network (CNN) analysis, stratified k-fold (k = 5) cross-validation was applied using training set. Trained model was evaluated using testing set. A total of 175 patients were included in this study. The average age at the time of PET imaging was 70.4 ± 9.3 years and included 77 men and 98 women (44.0% and 56.0%, respectively). The visual assessment revealed positivity in 62 patients (35.4%) and negativity in 113 patients (64.6%). After stratified k-fold cross-validation, the CNN model showed an average accuracy of 0.917 ± 0.027. The model exhibited an accuracy of 0.914 and an area under the curve of 0.958 in the testing set. These findings affirm the model's high reliability in distinguishing between positive and negative cases. The study verifies the potential of the CNN model to classify amyloid positive and negative cases using brain PET images. This model may serve as a supplementary tool to enhance the accuracy of clinical diagnoses.

High Resolution Image Rotation Acceleration System Based on the ZYNQ Platform

High Resolution Image Rotation Acceleration System Based on the ZYNQ Platform

Wheat leaf localization and segmentation for yellow rust disease detection in complex natural backgrounds

Wheat yellow rust disease poses a significant threat to global wheat yield and grain quality. Early detection of this disease will help to minimize the loss caused by its effects. Existing models work well on images taken in a controlled environment, whereas a uniform background is placed behind the leaf, but these models fail to produce good results in natural settings. Previous research also involves manual interventions in the pipeline to achieve good classification results such as cropping the images, using uniform backgrounds, etc. These systems are not practical to use in natural environments where there will be a lot of background noise to the image and manual cropping becomes an extra step for the farmer. Moreover, the unavailability of the dataset in which images of leaves are taken in a natural setting became another challenge. In this research, a dataset is curated and leaves are annotated for object detection, object segmentation further the leaves are classified into 3 classes ie healthy, resistant, and susceptible. A novel unsupervised image rotation algorithm is proposed that takes input from YOLOv8 to align the leave in such a way that maximum background can be removed by a rectangular bounding box . Then the comparison between multiple state-of-the-art segmentation models ie. UNET, Segment-Anything (SAM), Segnet, LinkNet, PSPNet, FPN, Deep-Labv3+ (Xception), and DeepLabv3+ (Mo-bileNet) has shown that UNET has outperformed all the other segmentation models with an IOU score of 0.9563. Lastly for classification, the performance of multiple convolution neural networks ie. VGG16, Resnet 101(v2), Xception, Mo-bileNetV2, and Transformer-based models ie. Swin trans-former and MobileVit have been compared. Swin transformer has outperformed the state-of-the-art CNN models with an accuracy of 95.8%. This paper proposes a complete robust pipeline that can be deployed in natural environment and does not need any manual intervention to produce good results. This research shows that good localization of leaves and removal of unwanted background noise at the earliest stage of the pipeline will assist the segmentation model to effectively segment the leaf from the background which will enable classification models to achieve high classification accuracy, even when dealing with very small datasets.

Super-resolution based on frequency dependence of echo phase rotation in 3D ultrasound imaging by spatially encoded transmit/receive

Abstract Currently, we are investigating the ultrasound imaging of a single transmitter/receiver system for a puncture microscopy application. The proposed model, consisting of an encoding mask layer with irregular thickness attached to the surface of the transducer, was carried out by a finite element method simulator. To increase the number of measurements, the device was rotated for several rotations. The image for each rotation was constructed separately by numerically solving the linear equation of the image model. In this paper, we introduce a super resolution method (SCM) based on an eigenvalue analysis in order to improve the poor resolution. It was assigned as weights and multiplied by the current image. The simple averaging and coherence factor were applied to compound over a number of rotations to obtain the final image. The results show that the image has a better image resolution and also suppresses the unwanted signals as well.

PneumaOCT: Pneumatic optical coherence tomography endoscopy for targeted distortion-free imaging in tortuous and narrow internal lumens.

The complex anatomy of internal luminal organs, like bronchioles, poses challenges for endoscopic optical coherence tomography (OCT). These challenges include limited steerability for targeted imaging and nonuniform rotation distortion (NURD) with proximal scanning. Using rotary micromotors for distal scanning could address NURD but raises concerns about electrical safety and costs. We present pneumaOCT, the first pneumatic OCT endoscope, comprising a steerable catheter with a soft pneumatic actuator and an imaging probe with a miniature pneumatic turbine. With a diameter of 2.8 mm, pneumaOCT allows for a bending angle of up to 237°, facilitating navigation through narrow turns. The pneumatic turbine enables adjustable imaging speeds from 51 to 446 revolutions per second. We demonstrate the pneumaOCT in vivo imaging of mouse esophagus and colon, as well as targeted and distortion-free imaging of peripheral bronchioles in a bronchial phantom and a porcine lung. This advancement substantially improves endoscopic OCT for navigational imaging in curved and narrow lumens.

EpidermaQuant: Unsupervised Detection and Quantification of Epidermal Differentiation Markers on H-DAB-Stained Images of Reconstructed Human Epidermis.

The integrity of the reconstructed human epidermis generated in vitro can be assessed using histological analyses combined with immunohistochemical staining of keratinocyte differentiation markers. Technical differences during the preparation and capture of stained images may influence the outcome of computational methods. Due to the specific nature of the analyzed material, no annotated datasets or dedicated methods are publicly available. Using a dataset with 598 unannotated images showing cross-sections of in vitro reconstructed human epidermis stained with DAB-based immunohistochemistry reaction to visualize four different keratinocyte differentiation marker proteins (filaggrin, keratin 10, Ki67, HSPA2) and counterstained with hematoxylin, we developed an unsupervised method for the detection and quantification of immunohistochemical staining. The pipeline consists of the following steps: (i) color normalization; (ii) color deconvolution; (iii) morphological operations; (iv) automatic image rotation; and (v) clustering. The most effective combination of methods includes (i) Reinhard's normalization; (ii) Ruifrok and Johnston color-deconvolution method; (iii) proposed image-rotation method based on boundary distribution of image intensity; and (iv) k-means clustering. The results of the work should enhance the performance of quantitative analyses of protein markers in reconstructed human epidermis samples and enable the comparison of their spatial distribution between different experimental conditions.

Automatic reorientation to generate short-axis myocardial PET images

BackgroundAccurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images.MethodsA total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients.ResultsOn the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications.ConclusionOur algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference.

Terahertz optical pattern recognition with rotation and scaling enhanced by a 3D-printed diffractive deep neural network

Optical pattern recognition (OPR) has the potential to be a valuable tool in the field of terahertz (THz) imaging, with the advantage of being capable of image recognition with single-point detection, which reduces the overall system costs. However, this application is limited in the traditional OPR that rotation and scaling of the input image will bring about an offset of the recognition spot. Here we demonstrate a full-diffractive method to maintain the recognition spot at a fixed position, even when the input image is rotated or scaled, by using an all-optical diffractive deep neural network. The network is composed of two layers of diffractive optical elements (DOEs) without a 4f-system, and 3D-printed all-in-one. Experimental results show that our device can achieve a stable recognition of the input image regardless of its rotation (from 0° to 360°) or scaling (with a ratio from 1 to 1/1.9). This work is expected to provide enhanced functionality for compact THz systems in imaging and security applications.

Quality Grading of Dried Abalone Using an Optimized VGGNet

As living standards have improved, consumer demand for high-quality dried abalone has increased. Traditional abalone grading is achieved through slice analysis (sampling analysis) combined with human experience. However, this method has several issues, including non-uniform grading standards, low detection accuracy, inconsistency between internal and external quality, and high loss rate. Therefore, we propose a deep-learning-aided approach leveraging X-ray images that can achieve efficient and non-destructive internal quality grading of dried abalone. To the best of our knowledge, this is the first work to use X-ray to image the internal structure of dried abalone. The work was divided into three phases. First, a database of X-ray images of dried abalone was constructed, containing 644 samples, and the relationship between the X-ray images and the internal quality of the dried abalone was analyzed. Second, the database was augmented by image rotation, image mirroring, and noise superposition. Subsequently, a model selection evaluation process was carried out. The evaluation results showed that, in a comparison with models such as VGG-16, MobileNet (Version 1.0), AlexNet, and Xception, VGG-19 demonstrated the best performance in the quality grading of dried abalone. Finally, a modified VGG-19 network based on the CBAM was proposed to classify the quality of dried abalone. The results show that the proposed quality grading method for dried abalone was effective, achieving a score of 95.14%, and outperformed the competitors, i.e., VGG-19 alone and VGG-19 with the squeeze-and-excitation block (SE) attention mechanism.

Self-supervised class-balanced active learning with uncertainty-mastery fusion

Deep active learning (DeepAL) offers a viable solution for enhancing the predictive performance of models with limited annotation costs. Popular DeepAL pipelines fail to (1) adequately and simultaneously draw on unlabeled and labeled samples to learn accurate data representations, and (2) progressively balance the distribution of selected samples across classes during the query. In this paper, we introduce a self-supervised DeepAL (SSAL) framework as a solution to the aforementioned challenges. First, SSAL simultaneously utilizes both labeled data and unlabeled data to learn high-quality representations. The method learns the image rotation degree for unlabeled data in a self-supervised manner, which can be jointly processed with the supervised learning. Second, the sample information is interpreted by fusing the discriminant uncertainty and the presumed mastery ability of each sample. This approach prevents the selection of many nonrepresentative boundary samples based solely on uncertainty and avoids the selection of aggregated samples based solely on clustering. Third, we propose a new sample selection strategy named class rebalanced group sampling. This strategy rebalances classes by the optimal size of query samples for each class, which is induced from the approximation between the population and the model distribution. Experiments were conducted on five public image datasets, namely CIFAR-10/100, FashionMNIST, SVHN, and TinyImageNet, and the results demonstrated the effectiveness of our approach. The code is available at https://github.com/FanSmale/SSAL.

WH-DETR: An Efficient Network Architecture for Wheat Spike Detection in Complex Backgrounds

Wheat spike detection is crucial for estimating wheat yields and has a significant impact on the modernization of wheat cultivation and the advancement of precision agriculture. This study explores the application of the DETR (Detection Transformer) architecture in wheat spike detection, introducing a new perspective to this task. We propose a high-precision end-to-end network named WH-DETR, which is based on an enhanced RT-DETR architecture. Initially, we employ data augmentation techniques such as image rotation, scaling, and random occlusion on the GWHD2021 dataset to improve the model’s generalization across various scenarios. A lightweight feature pyramid, GS-BiFPN, is implemented in the network’s neck section to effectively extract the multi-scale features of wheat spikes in complex environments, such as those with occlusions, overlaps, and extreme lighting conditions. Additionally, the introduction of GSConv enhances the network precision while reducing the computational costs, thereby controlling the detection speed. Furthermore, the EIoU metric is integrated into the loss function, refined to better focus on partially occluded or overlapping spikes. The testing results on the dataset demonstrate that this method achieves an Average Precision (AP) of 95.7%, surpassing current state-of-the-art object detection methods in both precision and speed. These findings confirm that our approach more closely meets the practical requirements for wheat spike detection compared to existing methods.

Research on coal-rock identification method and data augmentation algorithm of comprehensive working face based on FL-Segformer

Coal-rock interface identification technology was pivotal in automatically adjusting the shearer’s cutting drum during coal mining. However, it also served as a technical bottleneck hindering the advancement of intelligent coal mining. This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces, coupled with the limited availability of coal-rock datasets. The loss function of the SegFormer model was enhanced, the model’s hyperparameters and learning rate were adjusted, and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer. Additionally, an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer, enabling the collection of coal-rock test image datasets. The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation, color dithering, and Gaussian noise injection so as to augment the diversity and applicability of the datasets. As a result, a coal-rock image dataset comprising 8424 samples was generated. The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union (MIoU) and mean pixel accuracy (MPA) of 97.72% and 98.83%, respectively. The FL-SegFormer model outperformed other models in terms of recognition accuracy, as evidenced by an MIoU exceeding 95.70% of the original image. Furthermore, the FL-SegFormer model using original coal-rock images was validated from No. 15205 working face of the Yulin test mine in northern Shaanxi. The calculated average error was only 1.77%, and the model operated at a rate of 46.96 frames per second, meeting the practical application and deployment requirements in underground settings. These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines.

The impact of cross-Kerr nonlinearity on rotary photon drag and temporal cloaking in Sagnac interferometry

We theoretically explore the impact of cross-Kerr nonlinearity (CKN) on rotary photon drag (RPD) and temporal cloaking in a Sagnac interferometer. With the activation of CKN, significant improvements in RPD and temporal cloaking are observed. Drag angles of ±1×10−3 and ±5×10−3 radians are attained, similarly, temporal gaps of 10μ s, 12μ s and 13μ s are observed. In addition to the observed electromagnetically induced transparency window, the group refractive index is drastically enhanced, time delay/advance are tuned from pico-radians to micro-radians, and reduced relativistic group velocities are achieved by activating CKN within the system. Additionally, the transmission pulses intensities are observed to be reduced with the dragging phenomenon. The findings may have potential applications in controlled image coding, rotation sensing and secure communication technologies.

Intelligent Detection of Tunnel Leakage Based on Improved Mask R-CNN

The instance segmentation model based on deep learning has addressed the challenges in intelligently detecting water leakage in shield tunneling. Due to the limited generalization ability of the baseline model, occurrences of missed detections, false detections, and repeated detections are encountered during the actual detection of tunnel water leakage. This paper adopts Mask R-CNN as the baseline model and introduces a mask cascade strategy to enhance the quality of positive samples. Additionally, the backbone network in the model is replaced with RegNetX to enlarge the model’s receptive field, and MDConv is introduced to enhance the model’s feature extraction capability in the edge receptive field region. Building upon these improvements, the proposed model is named Cascade-MRegNetX. The backbone network MRegNetX features a symmetrical block structure, which, when combined with deformable convolutions, greatly assists in extracting edge features from corresponding regions. During the dataset preprocessing stage, we augment the dataset through image rotation and classification, thereby improving both the quality and quantity of samples. Finally, by leveraging pre-trained models through transfer learning, we enhance the robustness of the target model. This model can effectively extract features from water leakage areas of different scales or deformations. Through instance segmentation experiments conducted on a dataset comprising 766 images of tunnel water leakage, the experimental results demonstrate that the improved model achieves higher precision in tunnel water leakage mask detection. Through these enhancements, the detection effectiveness, feature extraction capability, and generalization ability of the baseline model are improved. The improved Cascade-MRegNetX model achieves respective improvements of 7.7%, 2.8%, and 10.4% in terms of AP, AP0.5, and AP0.75 compared to the existing Cascade Mask R-CNN model.

Shift-insensitive perceptual feature of quadratic sum of gradient magnitude and LoG signals for image quality assessment and image classification

Most existing full-reference (FR) Image quality assessment (IQA) models work in the premise of that the two images should be well registered. Shifting an image would lead to an inaccurate evaluation of image quality, because small spatial shifts are far less noticeable than structural distortion for human observers. To this regard, we propose to study an IQA feature that is shift-insensitive to the basic primitive structure of images, i.e., image edge. According to previous studies, the image gradient magnitude (GM) and the Laplacian of Gaussian (LoG) operator that depict the edge profiles of natural images are highly efficient structural features in IQA tasks. In this paper, we find that the Quadratic sum of the normalized GM and the LoG signals (QGL) has excellent shift-insensitive property in representing image edges after theoretically solving the selection problem of a ratio parameter to balance the GM and LoG signals. Based on the proposed QGL feature, two FR-IQA models can be built directly by measuring the similarity map with mean and standard deviation pooling strategies, named mQGL and sQGL, respectively. Experimental results show that the proposed sQGL and mQGL work robustly on four benchmark IQA databases, and QGL-based models show great shift-insensitive property to spatial translation and image rotation while judging the image quality. In addition, we explore the feasibility of combining QGL feature with deep neural networks, and verify that it can help to promote image pattern recognition in texture classification tasks.

A New Approach of Steganography on Image Metadata

In this paper, we introduce a novel method, Steganography on Image Metadata (SIM), to tackle the problem of robustness modification in steganography. The SIM method works by embedding messages into the metadata storage space of digital media. Metadata is information embedded in a file that explains the file's content. The advantage of this method is that it does not alter the pixel values in the image, ensuring no degradation in media quality, and the secret message remains secure even when robustness manipulations are applied to the stego-image. To enhance data security, this paper also suggests using Fernet cryptography for message encryption during the embedding process into the cover-image. According to experimental evaluations, the SIM technique can attain a maximum PSNR value of 100 dB and an outstanding MSE value of 0. All robustness manipulation issues in steganography can be effectively addressed using the SIM method. Test results demonstrate that the SIM method can withstand symmetric and asymmetric cropping manipulations down to a pixel size of 1x1, and the message can still be extracted. Testing with image rotation manipulation also proves that the message can be successfully extracted even when the stego-image is rotated up to 180 degrees. Experiments with image resizing manipulation also confirm that the message can be recovered even when the stego-image undergoes up to 90% compression. Testing with color effects applied to the image also does not affect message extraction results.

A Robust Registration Method for Multi-view SAR Images based on Best Buddy Similarity

Abstract. Due to the influence of imaging angle and terrain undulation, multi-view synthetic aperture radar (SAR) images are difficult to be directly registered by traditional methods. Although feature matching solves the issue of image rotation and maintains scale invariance, these methods often lead to non-uniformity of interest points and may not achieve subpixel accuracy. The traditional template matching method makes it difficult to generate correct matches for multi-view SAR oblique images. In this paper, a multi-view SAR image template matching method based on Best Buddy Similarity (BBS) is proposed to solve the traditional methods' problem. Firstly, the initial correspondences between images are established according to the Range-Doppler model of SAR images. Secondly, a sliding window search is performed on the established correspondence, the BBS is calculated, and the subpixel locations of the peaks on the similarity map are estimated to achieve a fine match. In the calculation process of BBS, the SAR-ROEWA operator is used to suppress the speckle noise of SAR images. The experiment demonstrated that SAR-BBS can accurately match SAR images with large rotation angle. The peak value on the search window is significant. The registration accuracy of SAR-BBS outperforms the other state-of-the-art methods.

Radiomics analysis of the tissue content of the esophageal hiatus of normal subjects without esophageal symptoms (establishing control values)

Introduction: The opening in the diaphragm through which the esophagus enters from the thorax into the abdomen, i.e., esophageal hiatus is formed by the right crus of the diaphragm. During esophageal peristalsis, the lower end of the esophagus, i.e., lower esophageal sphincter (LES), slides from the abdomen into chest, which we suspect is due to the presence of fat in the hiatus that serves as a lubricant. A recent study from our laboratory found that in patients with achalasia esophagus, the LES and crus of diaphragm are firmly anchored together, which we suspect is due to the loss of fat from the hiatus. Gray scale intensity analysis of the radiological images can be used to determine the nature of tissue, i.e., fat, connective tissue, muscle etc. Goal: To establish gray scale values of the esophageal hiatus from the CT scan images in controls. Methods: 42 patients who had undergone an abdominal CT scan for symptoms unrelated to upper gastrointestinal pathology were selected for this analysis. These patients had no esophageal symptoms and were not taking any medications related to the esophagus and stomach, which included acid inhibition medications. The CT scan was visualized in the Visage (San Diego, CA), a software program that allows rotation of images in X, Y and Z planes to visualize the esophageal hiatus in a 2D image. A software (Segcrus) was built to perform the grayscale intensity analysis of the 3 regions of equal size in the image, 1) subcutaneous fat, 2) muscle, and 3) esophageal hiatus. The 3 regions of interest were demarcated manually in the 2D image. The inner margin of the hiatus was selected as the region of interest for the hiatus image. The software generated a statistical non-parametric method approximation using kernel density estimation (KDE) of the grey scale intensity distributions of the 3 regions of interest. The median values were computed to represent the central tendencies of the distributions, providing an insight into the typical greyscale values of the tissue types. Results: distinct patterns in the region intensity distributions for hiatal region, muscle, and fat were seen. For the hiatus, the values were (Median = 105), interquartile range (IQR = 80.3), and range (Min = 61.5, Max = 137.95), providing a comprehensive description of central tendency, variability, and data spread. For muscle, (Median = 139.92), interquartile range (IQR = 71.1), and range (Min = 119.67, Max = 157.17), and for fat, (Median = 48.24), interquartile range (IQR = 52.9), and range (Min = 23.99, Max = 67.79. Hiatus to fat tissue intensity overlap was not related to BMI (r=0.17, p=0.29), or Age (r=-0.04, p=0.82). There was a notable correlation between the overlap of Hiatal-Muscle tissue intensity and Age (r=0.38, p=0.012). However, such correlation was not observed with BMI (r=-0.3, p=0.06). Additionally, there was no significant correlation between the overlap of Hiatal-Fat tissue intensity and gender (r=-0.26, p=0.09), and similarly, the overlap of Hiatal-Muscle intensity showed no correlation with sex (r=0.09, p=0.58). The results highlight the variability and central tendencies within these tissue types across the normal subjects. Conclusion: These analysis in subjects with no esophageal symptoms may be used as control values to determine if there are difference in the tissue content of the esophageal hiatus between normal and patients with various esophageal motility disorders. R01 DK109376. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.