Grayscale Values Research Articles

Quantitative Evaluation of Fundus Autofluorescence in Laser Photocoagulation Scars for Diabetic Retinopathy: Conventional vs. Short-Pulse Laser.

Short-pulse laser is popular for its advantages like less pain. However, its effectiveness is still debated. The aim of this study was to compare fundus autofluorescence (FAF) luminosity changes of laser photocoagulation scars between the conventional laser (0.2 s) and the short-pulse laser (0.02 s) for diabetic retinopathy. Conventional and short-pulse laser photocoagulations were performed in six and seven eyes, respectively. FAF images were captured at 1, 3, 6, 12, and 18 months after the treatments. To evaluate FAF, individual gray-scale values of the laser scars adjacent to the retinal arcade vessels were recorded; then, the mean gray values of the scars were divided by the luminosity of arcade vein. The average luminosity ratio of laser scars at 1, 3, 6, 12, and 18 months were 1.51 ± 0.17, 1.26 ± 0.07, 1.21 ± 0.03, 0.95 ± 0.11, and 0.89 ± 0.05 with conventional laser and 1.91 ± 0.13, 1.50 ± 0.15, 1.26 ± 0.08, 1.18 ± 0.06, and 0.97 ± 0.04 with short-pulse laser, respectively. Findings suggest the short-pulse laser displayed delayed hypoautofluorescence progression. This implies potential postponement in post-irradiation atrophic changes, as well as metabolic amelioration delay in the ischemic retina, when compared to conventional laser treatment.

Research and application of construction technology of digital mud cake formed by drilling fluid filtration

In drilling engineering, mud cake is formed when drilling fluid invades the near-wellbore zone, which can reduce drilling fluid leakage and reservoir pollution and maintain wellbore stability. To study the three-dimensional spatial characteristics of mud cakes, such as heterogeneity, particle accumulation, and porous media, CT scan images of cores and mud cakes before and after drilling fluid intrusion are used as the research object. The composite filtering and segmentation method for typical regions of images based on grayscale values and the batch segmentation method for large regions of images based on deep learning are investigated. On this basis, a visualized digital mud cake is constructed, and microscopic evaluation indexes such as the percentage, equivalent radius, throat length, sphericity, and coordination number of pores and particles in the mud cake are established. The application results show that the number of isolated pores in the outer mud cake formed by static filtration of drilling fluid is more than that of dynamic ones, the connectivity of pores is poor, and the particles have a better sealing effect on pores. The combined effect of the outer and inner mud cake results in different effects on core intrusion. The construction technique of digital mud cake can study the structure at a micron scale. This technique can provide more detailed guidance for engineering situations, such as optimization of drilling fluid systems in fractured formations, damage evaluation of reservoirs by drilling fluid, and influence of mud cake on cementing quality.

Quantification of carbon black tattoo ink hydrophobicity pre- and post-sonication

Abstract Despite growing academic interest for dermal inking in contemporary society, the reason why skin tattoos remain visible for life has predominantly been studied from a biological perspective. In preliminary physics studies of ink it has been presumed that the hydrophobicity of its main constituent prevents further dilution of pigment dispersion and therefore may be a contributing factor to aforementioned lifelongevity. According to these early studies, ultrasound might change the hydrophobicity of microparticles. The purpose of this study was to confirm or refute the presence of hydrophobic components in carbon black tattoo ink and to relate sonication to such presence. Cuvettes with n-octanol, distilled water, and a droplet of unsonicated or sonicated carbon black ink were shaken, allowed to settle and subsequently photographed. The sonicated ink had been subjected to ultrasound during 5 min at a centre frequency of 1 MHz, a pulse-repetition frequency of 1 kHz, and a 10% duty cycle. The greyscale values in both parts of the cuvettes were averaged. The resulting ratio of light intensities was an indicator for the ink hydrophobicity. The intensity partition coefficient of carbon black ink was measured to be greater than 103 before sonication and less than 103 after sonication. Carbon black tattoo ink was found to be very hydrophobic. However, sonication was found to make the dispersion less hydrophobic. Influencing the hydrophobicity of tattoo ink might change the permanence of a skin tattoo.

Analysis of Histogram and Grayscale on Chest X-Ray in Lung Cancer Using Image-J

Posteranterior (PA) chest radiographic examination is a support in screening for the diagnosis of lung cancer. Computed radiography (CR) modality can produce chest images quickly, optimally and can be processed as needed. However, so far radiologists interpret images only by visual assessment, so the results are very subjective. Therefore digital medical image processing can be done by looking at the histogram and gray scale values to increase the accuracy of enforcing patient diagnoses. This study aims to analyze the comparison of histograms and gray degree values on CR chest images between normal patients and lung cancer patients. The study was conducted using 30 chest images consisting of normal and lung cancer patient groups with 15 images each. All images are calculated grayscale and display histogram graphics with the Image-J application and statistically analyzed using the Independent T-Test. The results show that there is a difference in grayscale values between normal chest images and lung cancer (p<0.001). The grayscale and histogram values on lung cancer chest images ( 103.2908 + 6.119 ) are higher and tend to the right compared to the grayscale and histogram values on normal chest images ( 64.5848 + 3.28) . Histogram and grayscale values add objective image interpretation in diagnosing lung cancer.

Inkjet Printing of PrBa0.5Sr0.5Co1.5Fe0.5O5+ δ Cathode for High-Performance Proton Ceramic Fuel Cells

Ceramic fuel cells have been actively studied and are in the commercialization stage because of their economic and efficiency advantages, including the lack of precious metal catalysts, including Pt, fuel flexibility, and high efficiency. Solid oxide fuel cells (SOFCs) are the most typical type of ceramic fuel cell that conduct oxide ions. However, the operating temperature of SOFCs is high (>700 °C) owing to the high activation energy required for ion conduction. Prolonged operation at high temperatures can cause chemical and mechanical degradation in materials and peripheral parts, discouraging their use as long-time stationary generators, which is the main target market for SOFCs. For these reasons, expectations and interest in proton ceramic fuel cells (PCFCs) have recently increased owing to excellent power and durability exhibited at intermediate temperatures (<600 °C). According to recent reports, the durability of PCFC is superior to that of SOFC, even when methane fuel or city gas is used. The cathode reaction accounts for the highest resistance in PCFC operation. Therefore, the development of a high-performance cathode is the biggest factor for the success of PCFCs. To develop a high-performance cathode, the following factors must be optimized: 1) surface and interface shapes and components that can maximize the charge transfer reaction, and 2) porous microstructure that can effectively deliver oxygen gas to the reaction sites, and 3) electrolyte-electrode adhesion at the interface. The potential of inkjet printing (IJP) as a fabrication technology capable of optimizing these structures and components was recently confirmed by our group. IJP has attracted attention as a thin-film deposition technology that can produce complex and sophisticated microstructures, such as various multi-component ceramic materials, while controlling them at the nano-level. Recently, solution processes such as spin coating, tape casting, and screen printing have been frequently adopted in device manufacturing owing to their advantages such as simple accessibility and convenient management. However, fabricating films with nanoscale thickness is very difficult. Moreover, the precision with which a microstructure with the desired shape can be fabricated is limited. Conventional thin-film processes, such as physical vapor deposition and chemical vapor deposition, require complex and difficult deposition conditions, such as high-temperature or high-vacuum maintenance. In addition, equipment management is difficult, a large number of parts and a large system are required, and large-area deposition is highly challenging. Meanwhile, in the case of IJP technology, it is feasible to deposit high-quality nanoscale thin films relatively conveniently without requiring vacuum or temperature maintenance. Furthermore, they can be manufactured over large areas. IJP can be used to conveniently fabricate various patterns in the desired shape without masking or lithography. Moreover, it can precisely control the concentration and combination of printing inks to produce thin films with desired material compositions and microstructures. We successfully fabricated perovskite-type PBSCF cathodes for PCFCs using thermal inkjet printing (T-IJP) on a BaZrxCe0.8-xY0.1Yb0.1O3-δ electrolyte using an inexpensive desktop printer. Ceramic ink solutions were formulated using PBSCF nanopowder as a solid material and dispersed in anhydrous ethanol/propylene glycol methyl ether (PGME)-based solvents. The ink properties were improved by adding various polymers such as surfactants and dispersants. Printability was verified by analyzing the viscosity, surface tension, and particle size of the pigments. The shape and microstructure of the PBSCF cathode layer were successfully adjusted by controlling the number of printing cycles and grayscale value (color level) using commercial printing software. We verified the tendency of these specimens by observing the images using a scanning electron microscope (SEM) equipped with energy dispersive spectra. We also conducted performance evaluations and electrochemical impedance spectroscopy (EIS). Finally, we obtained an optimized PBSCF cathode (G-80) with adequate thickness and a favorable number of pores and cracks. The samples had identical thicknesses of 6–7 μm. This enabled a more reliable and accurate comparison and examination of the tendencies of the specimen microstructures by using grayscale values. We enhanced the performance compared to that of our previous T-IJP work. Among the four specimens measured in this study, G-80 attained a high power output of 728 mWcm-2 in the sub-IT regime (< 600 °C). This performance is higher than that achieved in previous similar studies.

An unsolvable pixel reduced authentication method for color images with grayscale invariance

This paper introduces an advanced grayscale-invariant authentication method for color images. Prior work directly utilizes the most significant bits (MSBs) of the red and blue channels for authentication code generation and embeds these codes into the least significant bits (LSBs). Concurrently, the green channel is adjusted to sustain consistent grayscale values. However, this direct utilization of MSBs often leads to a decline in image quality and results in a significant number of unsolvable pixels. To address these challenges, we present a new approach specifically designed to combat the deterioration of image quality and the prevalence of unsolvable pixels. In our method, we propose the MSB alteration (MSBA) technique, which modifies the MSBs of the red and blue channels to generate authentication codes with minimal distortion. This technique effectively reduces image distortion and the number of unsolvable pixels. Furthermore, we introduce the pixel substitution strategy (PSS) and the constraint-reduced strategy (CRS) to enhance the embedding efficiency. Experimental results demonstrate that our method outperforms prior work by converting more unsolvable pixels to solvable ones and achieving better detection results. Moreover, our method enhances image quality by approximately 1.5 dB, while maintaining the same embedding capacity.

Sensitive detection of 17β-estradiol at a picomolar level using an aptamer-assisted liquid crystal-based optical sensor.

A liquid crystal (LC)-based aptasensor was developed that can detect 17β-estradiol (E2) at the picomolar level. This aptasensor is based on competitive reactions of the aptamer that interacts with cetyl trimethyl ammonium bromide (CTAB) and E2 at the aqueous/LC interface. The long alkyl chain of CTAB anchored the 4-cyano-4'-pentylbiphenyl (5CB) to a homeotropic state and controls the local anchoring depending on the extent of electrostatic interaction with the aptamer. Upon addition of the aptamer solution to the CTAB-saturated LC layer, LCs change from dark to bright optical response. This is due to the perturbed orientation of 5CB at the aqueous/LC interface as a result of electrostatic attraction of the cationic group of CTAB and the phosphate group of the aptamer. The conformational change of the aptamer due to specific binding with E2 weakens the electrostatic attraction between CTAB and aptamer. When specific binding becomes relatively dominant, CTAB induces the orientation of LCs to the homeotropic state, resulting in a dark optical image observed. We also analyzed the change in the optical response of LCs according to the interfacial events and compared the grayscale values of the optical image for each concentration of E2 to determine the detection limit. Accordingly, the detection limit of the E2 sensor was found to be 3.1pM (0.8pg/ml) in Tris-buffered saline (TBS), and 6.8pM (1.9pg/ml) in human urine. The LC-based optical aptasensor was thus shown to be highly sensitive and selective with no requirement for complex analysis equipment.

Novel Three-Dimensional and Non-Invasive Diagnostic Approach for Distinction between Odontogenic Keratocysts and Ameloblastomas.

Aim of this study was to demonstrate the diagnostic ability to differentiate odontogenic keratocysts (OKCs) from ameloblastomas (AMs) based on computed tomography (CT) or cone beam computed tomography (CBCT) scans. Preoperative CT and CBCT scans from 2004 to 2019 of OKCs and AMs were analyzed in 51 participants. Lesions were three-dimensionally (3D) assessed and Hounsfield units (HU) as well as gray scale values (GSV) were quantified. Calculated HU spectra were compared within the same imaging modalities using unpaired t-tests and correlated with participants characteristics by calculating Pearsons correlation coefficients. Within the CT scans, AMs had highly significantly higher HU values compared to OKCs (43.52 HU and 19.79 HU, respectively; p < 0.0001). Analogous, within the CBCT scans, AMs had significantly higher GSV compared to OKCs (-413.76 HU and -564.76 HU, respectively; p = 0.0376). These findings were independent from participants' gender and age, anatomical site, and lesion size, indicating that the HU- and GSV-based difference reflects an individual configuration of the lesion. HU and GSV spectra calculated from CT and CBCT scans can be used to discriminate between OKCs and AMs. This diagnostic approach represents a faster and non-invasive option for preoperative diagnosis of such entities and has potential to facilitate therapeutic decision making.

Camera calibration approach using polarized light based on high-frequency component variance weighted entropy for pose measurement

Abstract The camera’s intrinsic parameters calibration directly affects monocular pose measurement accuracy. To improve the calibration accuracy of the camera’s intrinsic parameters, the polarizing device is used to eliminate the high-brightness areas in the images collected for camera calibration. However, an unsuitable polarizing angle will reduce the image’s contrast. This causes the texture information of the image to be lost or makes the extracted feature change in the direction of grayscale value decline, ultimately affecting the feature extraction. Aiming at this issue, this paper proposed a camera calibration approach using polarized light based on high-frequency component variance weighted entropy (HVE). We determined the suitable polarizing angle according to the two-dimensional homography matrix describing the mapping between the target plane and normalized imaging plane for each calibration location. Under the suitable polarizing angle, we determined the optimal exposure time according to the HVE. The images collected under suitable polarizing angles and the optimal exposure time were used to finish the camera calibration. The suitable polarizing angles and optimal exposure time can ensure the image’s contrast and texture information. The experimental findings demonstrate that our approach can increase the camera calibration accuracy and thus pose measurement accuracy. In the −45° to +45° range, the average pose measurement error is less than 0.05° and lowered by 0.08° (62%) compared to that performed using the intrinsic parameters calibrated with the images collected under no polarizing device. In the 0–20 mm range, the average pose measurement error is less than 0.028 mm and lowered by 0.05 mm (64%) compared to that performed using the intrinsic parameters calibrated with the images collected under no polarizing device.

Ability of artificial intelligence to identify self-reported race in chest x-ray using pixel intensity counts.

Prior studies show convolutional neural networks predicting self-reported race using x-rays of chest, hand and spine, chest computed tomography, and mammogram. We seek an understanding of the mechanism that reveals race within x-ray images, investigating the possibility that race is not predicted using the physical structure in x-ray images but is embedded in the grayscale pixel intensities. Retrospective full year 2021, 298,827 AP/PA chest x-ray images from 3 academic health centers across the United States and MIMIC-CXR, labeled by self-reported race, were used in this study. The image structure is removed by summing the number of each grayscale value and scaling to percent per image (PPI). The resulting data are tested using multivariate analysis of variance (MANOVA) with Bonferroni multiple-comparison adjustment and class-balanced MANOVA. Machine learning (ML) feed-forward networks (FFN) and decision trees were built to predict race (binary Black or White and binary Black or other) using only grayscale value counts. Stratified analysis by body mass index, age, sex, gender, patient type, make/model of scanner, exposure, and kilovoltage peak setting was run to study the impact of these factors on race prediction following the same methodology. MANOVA rejects the null hypothesis that classes are the same with 95% confidence ( 7.38, ) and balanced MANOVA ( 2.02, ). The best FFN performance is limited [area under the receiver operating characteristic (AUROC) of 69.18%]. Gradient boosted trees predict self-reported race using grayscale PPI (AUROC 77.24%). Within chest x-rays, pixel intensity value counts alone are statistically significant indicators and enough for ML classification tasks of patient self-reported race.

Grey image recognition-based mold growth assessment on the surface of typical building materials responding to dynamic thermal conditions

Mold growth on building materials poses a threat to both the building structural integrity and occupants’ well-being. However, it is generally studied under suitable conditions in laboratory; the assessment is based on visual inspection but lacks an objective criterion. This research explored the effects of simulated dynamic thermal conditions on mold growth on three typical building materials artificially contaminated with Aspergillus niger spores. Test specimens were assessed based on a developed digital image-based method, where image segmentation, processing, and greyscale recognition via the OpenCV visual library were introduced. The results showed that the high temperature-high humidity condition in a 24-h cyclic change facilitated mold growth on the surfaces of three materials, especially for gypsum board, with an identified area proportion of 1.13% on the 80th day. This was consistent with the changes of the counted number of mold colonies, and no significant differences were found among the gypsum board, latex paint, and wallpaper. The growth extents of mold spores were objectively evaluated by the mean greyscale values; the values decreased gradually with time, and the decrements were different compared high-temperature to low-temperature conditions. The mold growth models were developed, where the area proportion of mold growth was linearly related to the counted colonies and greyscale values under different material surfaces. This novel grey image recognition-based method provides an accurate means of evaluating mold growth abilities and extents, overcoming the inaccuracy of visual observation. The findings have significant implications for visual inspection, mold prediction, and building management.

A Study on Perception of Visual–Tactile and Color–Texture Features of Footwear Leather for Symmetric Shoes

The study applies Kansei engineering in analyzing the color and texture of leather footwear, utilizing neural network verification to mirror consumers’ visual and tactile imageries onto varieties of leather. This aids in the development of an advanced system for selecting leather footwear based on such impressions. Initially, representative word pairs denoting consumers’ visual and tactile perceptions of leather footwear were delineated. Post-evaluation of these perceptions through a sensibility assessment questionnaire was administered, using 54 samples of leather footwear provided by manufacturers, with each leather type codified in terms of visual and tactile sensibilities. Subsequently, a customized software algorithm was crafted to isolate the primary color and adhesiveness as color features from the leather sample images. Analyzing grayscale values of the images and using pixel neighborhood as a base, the associated calculation methods, such as LBP, SCOV, VAR, SAC, etc., were proposed to extract texture features from the images. The derived color and texture feature values were used as the input layer and the sensory vocabulary quantified values as the output layer. Backpropagation neural network training was conducted on 49 leather samples, with five leather samples used for testing, culminating in the verification of neural network training for three types and 17 combinations. The outcome was an optimal method for leather footwear Kansei engineering and neural network training, establishing a design process for leather footwear characteristics assisted by sensory vocabulary and a backpropagation neural network. Additionally, a computer-aided system for selecting leather footwear, based on these impressions, was designed and validated through footwear design. This study utilized symmetry in footwear design. By using the design of a single shoe to represent the imagery of a pair of symmetrical shoes, we verified whether the leather samples recommended by the leather imagery selection query system met the expected system input settings.

A novel feature extraction method for recognition of coal and gangue under wetting conditions

Aiming at the problem of low recognition accuracy of coal and gangue under wetting conditions, a new idea of feature extraction for recognition of coal and gangue was proposed based on grayscale distribution morphology. The surface imaging experiments under wetting conditions were carried out and three features such as zone brightness varying with location, bright irregular zones and bright curve zones were found in the obtained images. By studying the causes of these features, the distribution morphology of great grayscale value was selected as features for recognition of coal and gangue. The sub-image was sampled from the whole image by the algorithm of sliding window traversal and its occupied degree by great grayscale value was used to quantify distribution morphology. The maximum width of the sliding window, which was full of great grayscale value, was extracted as feature value of distribution morphology of great grayscale value, which was expressed as A. The recognition experiments of coal and gangue were carried out based on the statistical features of grayscale, the deep learning algorithm (Alex-Net) and A, respectively. The results showed that the maximum recognition accuracy was only 46.26% based on the statistical features of grayscale and the recognition accuracy was 83.0% based on the deep learning algorithm (Alex-Net), while the recognition accuracy reached 97.5% based on A. Such result demonstrates that the proposed feature extraction method can effectively improve the recognition accuracy of coal and gangue under wetting conditions.

A bit-plane encryption algorithm for RGB image based on modulo negabinary code and chaotic system

This paper proposes a new color image encryption algorithm based on modulo negabinary code (MNBC-IEA), which treats a color pixel as one 24-bit unsigned integer rather than three 8-bit unsigned integers, and it makes the 24-bit-planes dependent on each other. We first provide a new coding scheme of unsigned integers called modulo negabinary code (MNBC), and we construct its encoding and decoding algorithm. Next, we use MNBC to design the substitution algorithm for color image encryption. MNBC-IEA consists of the enhanced MNBC, a random cyclic shift (RCS), and a diffusion procedure. The RCS procedure is composed of the RCSs in three directions, and it treats a color image as a cuboid. Hence, the permutation process can cause the 24-bit-planes to affect each other in addition to scrambling the bit positions. The enhanced MNBC can change the grayscale values via a process that encodes a 28-bit unsigned integer consisting of a 24-bit pixel value and a 4-bit key into the MNBC. The diffusion procedure includes exclusive OR and modulo arithmetic such that plaintext pixels, ciphertext pixels, and keystreams are related to each other. The final experiments indicate that our algorithm can encrypt a color image into a random binary sequence and resist various statistical, differential, and brute-force attacks.

An anisotropically diffusible fuzzy clustering based image segmentation methodology for intracranial cyst detection

An Intracranial cyst is an abnormal growth of mass in the brain that affects functioning of the nervous system and so an early detection of the lesion enables to avoid adverse effects. The processing unit in the Magnetic Resonance Imaging (MRI) system performs reading the images followed by primary image enhancement to suppress distortions thereby enhancing the feature quality in terms of its intensity, augmenting the resolution by image segmentation, post-processing by thresholding based on grayscale values and performing several morphological operations. With the existing methodologies, extracting the Region Of Interest (ROI) with the overlapping intensity values lead to inaccurate results. A novel method in which the input image that is anisotropically diffused and blurred is converted into a sharp image. Further, fuzzy partitioning of pixels deployed on Global Thresholding –Clustering Methodology (GT-CM) based segmentation takes 4 clusters into account hence forth seperating the exterior portion of the skull, the border region of the skull, the ventricles which may include the lesion and the noise. Statistical results based on several metrics such as sensitivity, specificity, F measure, Jaccord Index, Dice Coefficient and precision show that the proposed method is far more effective. An accuracy of 99.26% is obtained in exactly locating and extracting the lesion along with its attributes.

Clinico-Radiographic Research Evaluating Crestal Bone Level and Bone Density Surrounding Dental Implants.

Planning the surgical operation, choosing the type of implant, and the effectiveness of the implant all heavily rely on the quantity and quality of the accessible bone. This study's goal was to determine how promptly inserted dental implants affected postoperative changes in bone density and crestal bone level by Cone bean computed tomography (CBCT) gray-scale value during a follow-up of three to six months. A prospective clinical-radiographic investigation was conducted. Twenty participants with a partly edentulous arch who were also systemically healthy were chosen from the OPD of the Department of Periodontics and Oral Implantology. Each patient's oral hygiene status was noted, and a preoperative CBCT was performed to assess bone density. The following clinical parameters were noted before surgical procedures: plaque index (PI) (Silness and Loe, 1964), gingival index (GI) (Loe and Sillness, 1964), probing pocket depth (PPD), and primary stability. A weak connection between Hounsfield unit (HU) in three months and six months was found. Moreover, a slender positive connection between baseline primary stability and three months was found. PI was shown to have a statistically significant difference between the means at several follow-ups (P value of 0.0001). PI and probing depth were not statistically different between baseline and six months (P values = 0.0001) or between three months and six months (P values = 0.050), respectively. The main outcome of this study shows that insertion torque is directly proportional to bone density and crestal bone level.

Reliability of Grayscale Value for Bone Density Determination in Oral Rehabilitation using Dental Implants.

Quality and quantity of jaw bones have been previously classified in literature using different methods. Imaging modalities such as computed tomography (CT), successfully determine bone density of jaws. This study aims to establish the role of cone-beam CT (CBCT) in determining the density of cortical and cancellous bones at different jaw sites. Eighty-three possible implant sites in healthy patients were evaluated using NewTomGiano CBCT machine. Cross-sections were obtained and cortical and cancellous bone densities on different aspects of the virtual implant in terms of Hounsfield unit (HU) were determined using New Net Technologies software version 6.1 and were classified according to software from D1 to D4. Data were entered into SPSS software (version 19.0) and were statistically analyzed. The mean HU showed the highest value for cortical and cancellous in the anterior mandible (mean HU 1874.01 and 1131.73, respectively) followed by the posterior mandible (mean HU 1789.20 and 872.95, respectively) and least in posterior maxilla (mean HU 1068.26 and 830.04, respectively). Maximum D1 bone type was found in cortical bone and D2 bone type was noted in cancellous bone area. Males showed very highly significant cortical bone thickness (P < 0.001) whereas females showed more cancellous bone thickness but the results were nonsignificant. A high degree of concordance between different regions of jaw bones with a strong correlation between the four bone types was obtained. Bone density plays a pivotal role in determining the prognosis of the implant. CBCT has proven to be beneficial in bone density analysis.

A Hybrid Domain Medical Image Encryption Scheme Using URUK and WAM Chaotic Maps with Wavelet – Fourier Transforms

Image encryption is one of the most important techniques to maintain data confidentiality against illegal access and fraudulent usage. In this study, a new medical image encryption technique was developed by combining the discrete wavelet transform, the fast Fourier transform, the Arnold transform, and two multidimensional chaotic systems. The medical image is subjected to a discrete wavelet transform before the magic square shuffles the image sub-bands. Confusion operations are performed on each scrambled subdomain using the Uruk 4D chaotic system. To increase randomness and unpredictability, a second stage of confusion is implemented in the domain of the Fast Fourier transform using the Arnold transform. The final encrypted image is obtained utilizing secret keys derived from the WAM 3D chaotic system. In particular, the initial conditions for chaotic systems are derived from grayscale values, thereby increasing the algorithm’s sensitivity to the input image. The results of the tests and the security analysis indicate that the proposed algorithm is exceptionally reliable and secure.

USE OF CONE BEAM COMPUTED TOMOGRAPHY GRAY SCALE VALUES FOR DIAGNOSING TUMORS AND CYSTS

The retrospective study was conducted in the Department of Radiology of the hospital from January 2022 to January 2023 to assess the ability of CBCT gray scale value to diagnose tumors and cysts and evaluate differences in bony changes of both. A total of 50 patients were included in the study. CBCT scanner with 68-1073μSv radiation dose was used for obtaining 3D images. Gray scale values were obtained on the tangential section. Radiological diagnosis was made after calculating mean gray values for benign lesions or cysts. Myxoma, odontogenic ghost cell tumor, and the adenomatoid odontogenic tumor had lower grayscale values, and multiple myeloma, cementoblastoma, acanthomatous ameloblastoma, residual cyst, and lateral periodontal cyst had higher values. The mean grayscale value for the tumor was 1468 ± 70.5, and for the cyst was 1292.1 ± 6.5. There was a significant correlation between both (P&lt;0.001). The bone density of normal and pathological maxillofacial regions was compared. The radiolucent area (e.g., axillary sinus) had the lowest, and the radiopaque region (e.g., enamel) had the highest gray scale value. CBCT gray value is a powerful diagnostic tool for tumors, cysts, and other orofacial lesions and has better outcomes than conventional 2-D imaging.

Computer-aided recognition and assessment of a porous bioelastomer in ultrasound images for regenerative medicine applications

It is difficult to use a single edge operator in image processing to extract continuous and accurate contours of a porous bioelastomer due to the fuzzy boundary and complex background in ultrasound images. To solve this problem, this paper proposes a joint algorithm for bioelastomer contour detection and a texture feature extraction method for monitoring the degradation performance of bioelastomers. First, the mean-shift clustering method is utilized to obtain the clustering feature information of bioelastomers and native tissue from manually segmented images, and this information is used as the initial information in the image binarization algorithm for image partitioning. Second, Otsu's thresholding method and mathematical morphology are applied in the process of image binarization. Finally, the Canny edge detector is employed to extract the complete bioelastomers contour from the binary image. To verify the robustness of the proposed joint algorithm, the results using the proposed joint algorithm, where mean-shift clustering is replaced with k-means clustering are also obtained. The proposed joint algorithm based on mean-shift clustering outperforms the joint algorithm based on k-means clustering, as well as algorithms that directly apply the Canny, Sobel and Laplacian methods. Texture feature extraction is based on the computer-aided recognition of bioelastomers. The region of interest (ROI) is set in the scaffold region, and the first-order statistical features and second-order statistical features of the greyscale values of the ROI are extracted and analysed. The proposed joint algorithm can not only extract ideal bioelastomers contours from ultrasound images but also provide valuable feedback on the degradation behaviour of bioelastomers at implant sites.