Histogram Correlation Research Articles

On the instrument-dependent appearance of ion dissociation events in atom probe tomography mass spectra

The successful application of atom probe tomography (APT) relies on the accurate interpretation of the mass spectrum (i.e.m/z histogram) from a sample. Some materials yield mass spectra that are amenable to a straightforward peak assignment/ranging, however, there are many materials that produce mass spectra with features that defy simple interpretation. One such example is Ga2O3 which yields mass spectra containing several broad and difficult to interpret features. Herein, we study the GaO2+→ O1++ Ga1+ dissociation and we explain how this dissociation process gives rise to broad and previously unassigned features in the mass spectrum. Trajectory simulations are performed for the dissociation reaction utilizing realistic electrostatic models and compared to experiments using commercially available straight flight and reflectron based local electrode (LE) APT instruments. It is shown that the appearance of these features is strongly dependent on the specific design of the time-of-flight (ToF) mass analyzer. We explore how various experimental parameters can affect the appearance of the dissociation process in the one-dimensional (1D) mass spectrum and in the two-dimensional (2D) correlation histogram. While the focus of this work is on a particular dissociation process related to Ga2O3, the understanding gained in the course of these simulations and experiments should be applicable to the interpretation of dissociation processes in other materials.

Enhanced lightweight encryption algorithm based on chaotic systems

In order to improve security and efficiency, this study presents a novel lightweight encryption technique that makes use of chaotic systems. Our method creatively combines the new chaotic KLEIN_64 algorithm with the Keccak-256 hash function, offering a solid basis for producing initial values essential for causing chaotic maps during the encryption process. After a deep validation with rigorous NIST testing, our chaotic pseudo random generator, LAC, exhibits excellent reliability and cryptographic robustness. Furthermore, the complexity of the cryptographic round function is improved by incorporating a second chaotic pseudo random generator that combines chaotic LFSR and Skew Tent Maps, thereby fortifying security measures.Designed with resource-limited applications in mind, our approach ensures that the cryptosystem remains both lightweight and efficient, meeting the stringent constraints typical of such environments. The practical feasibility and performance of our approach are extensively evaluated through FPGA implementation on the Zybo 7Z010 platform. Our implementation achieves a remarkable throughput of 2.820 Gbps while maintaining optimal resource utilization and efficiency. Extensive experimental results confirm the superior security of our cryptosystem, with correlation tests, entropy measurement, and histogram analysis showcasing robustness against statistical attacks. Moreover, the cryptosystem shows little fluctuation in the Unified Average Changing Intensity (UACI) and Non-Linear Pixel Change Rate (NPCR), confirming its resistance to differential attacks. Overall, our technique advances lightweight cryptography by providing a robust and efficient solution to modern cybersecurity challenges. In particular, our approach is well-suited for applications with limited resources, ensuring that security is maintained without compromising on performance or efficiency, thus fulfilling the needs of modern, constrained environments.

Research on Tire Surface Damage Detection Method Based on Image Processing.

The performance of the tire has a very important impact on the safe driving of the car, and in the actual use of the tire, due to complex road conditions or use conditions, it will inevitably cause immeasurable wear, scratches and other damage. In order to effectively detect the damage existing in the key parts of the tire, a tire surface damage detection method based on image processing was proposed. In this method, the image of tire side is captured by camera first. Then, the collected images are preprocessed by optimizing the multi-scale bilateral filtering algorithm to enhance the detailed information of the damaged area, and the optimization effect is obvious. Thirdly, the image segmentation based on clustering algorithm is carried out. Finally, the Harris corner detection method is used to capture the "salt and pepper" corner of the target region, and the segmsegmed binary image is screened and matched based on histogram correlation, and the target region is finally obtained. The experimental results show that the similarity detection is accurate, and the damage area can meet the requirements of accurate identification.

Blockchain based medical image encryption using Arnold’s cat map in a cloud environment

Improved software for processing medical images has inspired tremendous interest in modern medicine in recent years. Modern healthcare equipment generates huge amounts of data, such as scanned medical images and computerized patient information, which must be secured for future use. Diversity in the healthcare industry, namely in the form of medical data, is one of the largest challenges for researchers. Cloud environment and the Block chain technology have both demonstrated their own use. The purpose of this study is to combine both technologies for safe and secure transaction. Storing or sending medical data through public clouds exposes information into potential eavesdropping, data breaches and unauthorized access. Encrypting data before transmission is crucial to mitigate these security risks. As a result, a Blockchain based Chaotic Arnold’s cat map Encryption Scheme (BCAES) is proposed in this paper. The BCAES first encrypts the image using Arnold’s cat map encryption scheme and then sends the encrypted image into Cloud Server and stores the signed document of plain image into blockchain. As blockchain is often considered more secure due to its distributed nature and consensus mechanism, data receiver will ensure data integrity and authenticity of image after decryption using signed document stored into the blockchain. Various analysis techniques have been used to examine the proposed scheme. The results of analysis like key sensitivity analysis, key space analysis, Information Entropy, histogram correlation of adjacent pixels, Number of Pixel Change Rate, Peak Signal Noise Ratio, Unified Average Changing Intensity, and similarity analysis like Mean Square Error, and Structural Similarity Index Measure illustrated that our proposed scheme is an efficient encryption scheme as compared to some recent literature. Our current achievements surpass all previous endeavors, setting a new standard of excellence.

A Visual Cortex-Attentive Deep Convolutional Neural Network for Digital Image Design

With the proliferation of advanced visualization techniques in visual communication, enhancing digital image quality remains a persistent challenge. This study presents a sophisticated Convolutional Neural Network (CNN) model to optimize image processing. The model incorporates a multi-stage architecture attentive to biological visual pathways. Inter-subnetwork connections enable integrated feature learning, guided by adaptive weighting of luminance, color, orientation, and edge maps. Spatial and channel attention modules further enrich feature interplay. When evaluated on the LIVE 3D Phase dataset, the approach demonstrates marked improvements, with saliency maps closely mirroring human visual perception. Pearson Correlation Coefficient and Histogram Intersection metrics exceed conventional models, at 0.6486 and 0.7074 respectively. Testing across distortion types reveals strong agreement with subjective rankings, confirming the model's effectiveness. By combining automated feature extraction with insights from visual cortex mechanisms, this bio-inspired CNN framework significantly enhances image optimization and quality. The scalable approach provides a foundation for next-generation computer vision and machine learning applications.

A new method of image encryption using advanced encryption Standard (AES) for network security

With the rapid increase in the use of technology, images have become a major source of sharing personal, confidential and official information and there is a dire need to protect this secret data. Image encryption plays major role in the security of images and there are many techniques developed for this purpose. Chaos based image encryption has now become most applicable and beneficial technique for image encryption. The purpose of this paper is to highlight a new method of image encryption with the use of advanced encryption standard (AES) and chaotic maps. This technique is composed of substitution and permutation phases. AES is found to be most secure cipher until now against different kinds of attacks. The round keys are generated by AES using key expansion algorithm. The sensitivity of this technique is that it is dependent on initial values and input image. S-boxes in AES introduce non-linearity, confusion, and an avalanche effect, enhancing security and resistance to cryptographic attacks by substituting bytes in the encryption process. The combination of AES and chaotic maps in encryption schemes provides a two-tiered approach to enhance security. AES offers a strong and well-established encryption method, while chaotic maps introduce randomness and complexity, making it more difficult for attackers to decipher encrypted data. This combination is often used to achieve a higher level of encryption security in various applications, including data transmission and storage. Different kinds of analysis and tests are performed on the technique which includes information entropy, number of pixel change rate (NPCR), and peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI) and histogram correlation of adjacent pixels. The results of these tests show that this technique is secure and resistant towards attacks.

PUBLIC HEALTH EXPENDITURE, ENVIROMENTAL POLLUTION AND HEALTH OUTCOMES IN NIGERIA

The study examines public health expenditure, environmental pollution and health outcomes- using infant mortality rate as proxy. Data was sourced from Central Bank of Nigeria and National Bureau of statistics from the period 1981 to 2021. A test of stationarity of the variables using- Unit Root Tests (ADF and P-P Tests) were examined coupled with a test of the existence of a long-run equilibrium relationship using the Johansen Co-integration Test. Afterward, an Error Correction Model (ECM) technique were applied in the study. Other diagnostic tests such as Residual Diagnostic Test, Serial Correlation LM Test and Histogram Normality Test were conducted to further validate the credibility of the model. The findings from the results showed that carbon (iv) oxide and gas flaring do not have significant effect on infant mortality rate as they were not statistically significant at 5% level. HIV showed negative impact on infant mortality which means the variables do not have direct effect on infant mortality. On the other hand, public health expenditure showed positive impact on infant mortality rate due to corrupt practices in the health sector. The study recommends that, expenditure on healthcare should be efficient and accountable. Also, government should create more or intensify public awareness program on pollution and HIV. Overall, the study identified a significant connections between health expenditure and health outcomes, and a weak relationships between environmental pollution and health outcomes in Nigeria.

An Enhanced Security in Medical Image Encryption Based on Multi-level Chaotic DNA Diffusion

A novel medical image encryption technique has been proposed based on the features of DNA encodingdecoding in combination with Logistic map approach. The approach is proven for encryption of highly sensitive medical images with 100 percent integrity or negligible data loss. Testing is done on both high and low-resolution images. Proposed encryption technique consists of two levels of diffusion using the actual structure of the DNA. In the first level of diffusion process, we have used DNA encoding and decoding operations to generate DNA sequence of each pixel. The originality of the work is to use a long DNA structure stored in a text file stored on both sender and receiver’s end to improve the performance of the proposed method. In this initial level of diffusion, DNA sequences are generated for each pixe-land in each of the DNA sequence. Index values are obtained by employing a search operation on the DNA structure. This index values are further modified and ready to be used for next diffusion process. In the second level diffusion, a highly chaotic logistic map is iterated to generate sequences and is employed to extract the chaotic values to form the cipher images. The correlation coefficient analysis, Histogram analysis, Entropy analysis, NPCR, and UACI exhibit significant results. Therefore; the proposed technique can play an important role in the security of low-resolution medical images as well as other visible highly sensitive images.

A comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction

Breast cancer is the most common life-threatening cancer in women and one of the leading causes of death. Early diagnosis is one of the best defenses against the spread of breast cancer. Machine learning (ML) tools are now available for cancer detection and prediction. This study presents a comparative assessment of machine learning models for diagnosing breast cancer based on various classification schemes. Our classification methodology is based on well-organized data collection, preparation, transformation, and exploratory analysis (including correlation matrix, histogram, and data distribution). All characteristics are compared with the results of applying the Least Absolute Shrinkage and Selection Operator (LASSO) approach, which selects the most important attributes. Logistic Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms have been applied in this study. We have achieved the maximum accuracy of 90.68% by RF compared with LASSO. Similarly, the recall in KNN was 98.80%, the precision in MLP was 92.50%, and the F1 score in RF was 94.60%.

Chaotic-DNA system for efficient image encryption

In order to prevent unwanted access to sensitive data by unauthorized individuals, color images are encoded. Because chaotic-deoxyribonucleic acid (chaotic-DNA) encoding can make information highly secure, it is often employed in image encryption. In this study, a new image encryption technique has been proposed based on a new 4D-chaotic system and DNA computing. The algorithm consists of two phases: in the first phase, the pixel positions are permuted by chaotic sequences. In the second phase, according to the concept of DNA cryptography, a set of operations (like DNA addition, DNA XOR, DNA subtraction, shift right, and shift left) are performed on the DNA encoding sequence. The performance of the suggested algorithm is evaluated through analyses like correlation coefficient, entropy, histogram, and key space. The results show that the encryption method that was exhibited has good encryption performance and high security. For encrypted images, the histogram is fairly uniform, the correlation values between adjacent pixels are very small and close to zero, and the entropy is near to the ideal value of eight. In addition, the proposed system has a very large key space that is equal to 2627 keys, which makes it resistant to brute-force, differential, and statistical attacks.

An Effective Color Image Encryption Based on Henon Map, Tent Chaotic Map, and Orthogonal Matrices.

In the last decade, the communication of images through the internet has increased. Due to the growing demands for data transfer through images, protection of data and safe communication is very important. For this purpose, many encryption techniques have been designed and developed. New and secured encryption schemes based on chaos theory have introduced methods for secure as well as fast communication. A modified image encryption process is proposed in this work with chaotic maps and orthogonal matrix in Hill cipher. Image encryption involves three phases. In the first phase, a chaotic Henon map is used for permuting the digital image. In the second phase, a Hill cipher is used whose encryption key is generated by an orthogonal matrix which further is produced from the equation of the plane. In the third phase, a sequence is generated by a chaotic tent map which is later XORed. Chaotic maps play an important role in the encryption process. To deal with the issues of fast and highly secured image processing, the prominent properties of non-periodical movement and non-convergence of chaotic theory play an important role. The proposed scheme is resistant to different attacks on the cipher image. Different tests have been applied to evaluate the proposed technique. The results of the tests such as key space analysis, key sensitivity analysis, and information entropy, histogram correlation of the adjacent pixels, number of pixel change rate (NPCR), peak signal to noise ratio (PSNR), and unified average changing intensity (UCAI) showed that our proposed scheme is an efficient encryption technique. The proposed approach is also compared with some state-of-the-art image encryption techniques. In the view of statistical analysis, we claim that our proposed encryption algorithm is secured.

Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network.

Dual respiratory-cardiac gating reduces respiratory and cardiac motion blur in myocardial perfusion single-photon emission computed tomography (MP-SPECT). However, image noise is increased as detected counts are reduced in each dual gate (DG). We aim to develop a denoising method for dual gating MP-SPECT images using a 3D conditional generative adversarial network (cGAN). Twenty extended cardiac-torso phantoms with various 99m Tc-sestamibi distributions, defect characteristics, and body and organ sizes were used in the simulation, modeling six respiratory and eight cardiac gates (CGs), that is, 48 DGs for ordered subset expectation maximization reconstruction. Twenty clinical 99m Tc-sestamibi SPECT/CT datasets were re-binned into 7 respiratory gates and 8 CGs, that is, 56 DGs for maximum likelihood expectation maximization reconstruction. We evaluated the use of (i) phantoms' own datasets (patient-specific denoising [PD]) or other phantoms' datasets (cross-patient denoising) for training; (ii) the CG or the static (non-gated [NG]) data as the training references for cGAN; and (iii) cGAN as compared to conventional 3D post-reconstruction filtering, cardiac gating methods, and convolutional neural network. Normalized mean squared error, noise as assessed by normalized standard deviation, spatial blurring measured as the full-width-at-half-maximum of left ventricular wall, ejection fraction, joint correlation histogram, and defect size were analyzed as metrics of image quality. Training using patients' own dataset is superior to conventional training based on other patients' data. Using CG image as training reference provides a better trade-off in terms of noise and image blur as compared to the use of NG. cGAN-CG-PD provides superior performance as compared to other denoising methods for all physical and diagnostic indices evaluated in both simulation and clinical studies. cGAN denoising is promising for dual gating MP-SPECT based on the metrics mentioned earlier.

Reference-free differential histogram-correlative detection of steganography: performance analysis

<span lang="EN-US">Recent <span>research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless </span> modified.</span>

Detection of Osteoarthritis Based on EHO Thresholding

Knee Osteoarthritis (OA) is a joint disease that is commonly observed in people around the world. Osteoarthritis commonly affects patients who are obese and those above the age of 60. A valid knee image was generated by Computed Tomography (CT). In this work, efficient segmentation of CT images using Elephant Herding Optimization (EHO) optimization is implemented. The initial stage employs, the CT image normalization and the normalized image is incited to image enhancement through histogram correlation. Consequently, the enhanced image is segmented by utilizing Niblack and Bernsen algorithm. The (EHO) optimized outcome is evaluated in two steps. The initial step includes image enhancement with the measure of Mean square error (MSE), Peak signal to noise ratio (PSNR) and Structural similarity index (SSIM). The following step includes the segmentation which includes the measure of Accuracy, Sensitivity and Specificity. The comparative analysis of EHO provides 95% of accuracy, 94% of specificity and 93% of sensitivity than that of Active contour and Otsu threshold.

Optical color image encryption scheme with a novel DNA encoding algorithm based on a chaotic circuit

An optical setup with a unique color image cryptography technique employing a chaotic system is suggested in this study. For this purpose, deoxyribonucleic acid (DNA) encoding is used as an encryption method. Rössler attractor is employed as a chaotic system and it consists of three differential equations. In order to implement the chaotic system by analog electronic components, a circuit design including operational transconductance amplifier (OTA) is also suggested. Circuit simulations have been performed utilizing LT spice program. In the proposed study, the number of DNA encoding rules has excessively been increased compared to previous studies on DNA encoding technique. Thus, the security of the encryption scheme is enhanced. Numerous security tests such as correlation coefficient, histogram, Shannon entropy, secret key and initial condition sensitivities, plaintext and ciphertext attacks and differential attack have been carried out to evaluate functioning of the introduced cryptography algorithm. The numerical test results prove that the suggested encryption algorithm can resist statistical and differential attacks. Moreover, the cryptology scheme indicates the behavior of sensitivity to initial condition and secret key. The presented algorithm has great performance from the point of encoding rules number and being robust against differential attack. Moreover, enhanced double random phase encoding (DRPE) technique is used in optical stage of the study. By modifying the conventional DRPE method, correlation value between R, G, B components for same pixel have extremely reduced. All numerical analyses regarding chaotic system and encryption scheme have been performed in MATLAB program.

Texture filtering based unsupervised registration methods and its application in liver computed tomography images

Image registration is of great clinical importance in computer aided diagnosis and surgical planning of liver diseases. Deep learning-based registration methods endow liver computed tomography (CT) image registration with characteristics of real-time and high accuracy. However, existing methods in registering images with large displacement and deformation are faced with the challenge of the texture information variation of the registered image, resulting in subsequent erroneous image processing and clinical diagnosis. To this end, a novel unsupervised registration method based on the texture filtering is proposed in this paper to realize liver CT image registration. Firstly, the texture filtering algorithm based on L0 gradient minimization eliminates the texture information of liver surface in CT images, so that the registration process can only refer to the spatial structure information of two images for registration, thus solving the problem of texture variation. Then, we adopt the cascaded network to register images with large displacement and large deformation, and progressively align the fixed image with the moving one in the spatial structure. In addition, a new registration metric, the histogram correlation coefficient, is proposed to measure the degree of texture variation after registration. Experimental results show that our proposed method achieves high registration accuracy, effectively solves the problem of texture variation in the cascaded network, and improves the registration performance in terms of spatial structure correspondence and anti-folding capability. Therefore, our method helps to improve the performance of medical image registration, and make the registration safely and reliably applied in the computer-aided diagnosis and surgical planning of liver diseases.

Pulmonary Toxicity After Total Body Irradiation - Critical Review of the Literature and Recommendations for Toxicity Reporting.

IntroductionTotal body irradiation is an effective conditioning regimen for allogeneic stem cell transplantation in pediatric and adult patients with high risk or relapsed/refractory leukemia. The most common adverse effect is pulmonary toxicity including idiopathic pneumonia syndrome (IPS). As centers adopt more advanced treatment planning techniques for TBI, total marrow irradiation (TMI), or total marrow and lymphoid irradiation (TMLI) there is a greater need to understand treatment-related risks for IPS for patients treated with conventional TBI. However, definitions of IPS as well as risk factors for IPS remain poorly characterized. In this study, we perform a critical review to further evaluate the literature describing pulmonary outcomes after TBI.Materials and MethodsA search of publications from 1960-2020 was undertaken in PubMed, Embase, and Cochrane Library. Search terms included “total body irradiation”, “whole body radiation”, “radiation pneumonias”, “interstitial pneumonia”, and “bone marrow transplantation”. Demographic and treatment-related data was abstracted and evidence quality supporting risk factors for pulmonary toxicity was evaluated.ResultsOf an initial 119,686 publications, 118 met inclusion criteria. Forty-six (39%) studies included a definition for pulmonary toxicity. A grading scale was provided in 20 studies (17%). In 42% of studies the lungs were shielded to a set mean dose of 800cGy. Fourteen (12%) reported toxicity outcomes by patient age. Reported pulmonary toxicity ranged from 0-71% of patients treated with TBI, and IPS ranged from 1-60%. The most common risk factors for IPS were receipt of a TBI containing regimen, increasing dose rate, and lack of pulmonary shielding. Four studies found an increasing risk of pulmonary toxicity with increasing age.ConclusionsDefinitions of IPS as well as demographic and treatment-related risk factors remain poorly characterized in the literature. We recommend routine adoption of the diagnostic workup and the definition of IPS proposed by the American Thoracic Society. Additional study is required to determine differences in clinical and treatment-related risk between pediatric and adult patients. Further study using 3D treatment planning is warranted to enhance dosimetric precision and correlation of dose volume histograms with toxicities.

Thermal Image Restoration Based on LWIR Sensor Statistics.

An imaging system has natural statistics that reflect its intrinsic characteristics. For example, the gradient histogram of a visible light image generally obeys a heavy-tailed distribution, and its restoration considers natural statistics. Thermal imaging cameras detect infrared radiation, and their signal processors are specialized according to the optical and sensor systems. Thermal images, also known as long wavelength infrared (LWIR) images, suffer from distinct degradations of LWIR sensors and residual nonuniformity (RNU). However, despite the existence of various studies on the statistics of thermal images, thermal image processing has seldom attempted to incorporate natural statistics. In this study, natural statistics of thermal imaging sensors are derived, and an optimization method for restoring thermal images is proposed. To verify our hypothesis about the thermal images, high-frequency components of thermal images from various datasets are analyzed with various measures (correlation coefficient, histogram intersection, chi-squared test, Bhattacharyya distance, and Kullback–Leibler divergence), and generalized properties are derived. Furthermore, cost functions accommodating the validated natural statistics are designed and minimized by a pixel-wise optimization method. The proposed algorithm has a specialized structure for thermal images and outperforms the conventional methods. Several image quality assessments are employed for quantitatively demonstrating the performance of the proposed method. Experiments with synthesized images and real-world images are conducted, and the results are quantified by reference image assessments (peak signal-to-noise ratio and structural similarity index measure) and no-reference image assessments (Roughness (Ro) and Effective Roughness (ERo) indices).

Analytic Study of a Novel Color Image Encryption Method Based on the Chaos System and Color Codes

Due to the growing of the use of Internet and communication media, image encryption is rapidly increased. Image sharing through unsafe open channels is vulnerable for attacking and stealing. For protecting the images from attacks, encryption techniques are required. Recently, new and efficient chaos-based techniques have been suggested to develop secure image encryption. This study presents a novel image encryption framework based on integrating the chaotic maps and color codes. Three phases are involved in the proposed image encryption technique. Piecewise chaotic linear map (PWLCM) is used in the first phase for permuting the digital image. In the second phase, substitution is done using Hill cipher which is the mixing of color codes with the permuted image. The third phase is implemented by XORing, a sequence generated by the chaotic logistic map (CLM). The proposed approach enhances the diffusion ability of the image encryption making the encrypted images resistant to the statistical differential attacks. The results of several analyses such as information entropy, histogram correlation of adjacent pixels, unified average changing intensity (UACI), number of pixel change rate (NPCR), and peak signal-to-noise ratio (PSNR) guarantee the security and robustness of the proposed algorithm. The measurements show that the proposed algorithm is a noble overall solution for image encryption. Thorough comparison with other image encryption algorithms is also carried out.

Magnetic Resonance Imaging of Orbital Solitary Fibrous Tumors: Radiological-Pathological Correlation Analysis.

Background:Solitary fibrous tumors (SFTs) are rare and can be misdiagnosed because of their various radiological appearances.Purpose:To clarify the characteristic MRI findings of SFTs by analyzing their radiological-pathological correlation.Material and Methods:Nine consecutive patients with SFT who underwent magnetic resonance imaging (MRI) prior to surgery were analyzed. Eight patients underwent contrast-enhanced MRI, and three underwent dynamic MRI. Radiological-pathological correlation analysis, co-occurrence matrix, run-length matrix, and histogram analysis were performed to assess the relationship between pathological findings T1- and T2-weighted images (T1-WI and T2-WI).Results:All nine lesions ranged in size from 20 to 36 mm. Seven lesions were located in the superior portion of the retrobulbar space found outside of the muscle cone, and two lesions in the inferior portion were located within it. No significant correlation was observed between the amount of collagenous tissue and the qualitative evaluation of the signal on T1-WI and T2-WI. Kurtosis on T2-WI was significantly correlated with the amount of collagenous tissue (ρ = –0.97, p < 0.0001) and endothelial cells (ρ = –0.49, p = 0.0479).Conclusion:Kurtosis in the histogram analysis on T2WI showed a strong correlation with the amount of collagenous tissue.