Grayscale Levels Research Articles

High-Throughput Preparation and High-Throughput Detection of Polymer-Dispersed Liquid Crystals Based on Ink-Jet Printing and Grayscale Value Analysis.

In this paper, based on high-throughput technology, polymer dispersed liquid crystals (PDLC) composed of pentaerythritol tetra (2-mercaptoacetic acid) (PETMP), trimethylolpropane triacrylate (TMPTA), and polyethylene glycol diacrylate (PEGD 600) were investigated in detail. A total of 125 PDLC samples with different ratios were quickly prepared using ink-jet printing. Based on the method of machine vision to identify the grayscale level of samples, as far as we know, it is the first time to realize high-throughput detection of the electro-optical performance of PDLC samples, which can quickly screen out the lowest saturation voltage of batch samples. Additionally, we compared the electro-optical test results of manual and high-throughput preparation PDLC samples and discovered that they had very similar electro-optical characteristics and morphologies. This demonstrated the viability of PDLC sample high-throughput preparation and detection, as well as promising application prospects, and significantly increased the efficiency of PDLC sample preparation and detection. The results of this study will contribute to the research and application of PDLC composites in the future.

Optically heated blackbody emitter for dynamic infrared scene generation

The infrared image conversion chip based on optical heating technique has advantages of wide spectral range and high spatial resolution, which is appropriate as an infrared target simulator. At present, the dynamic infrared image, which is physically generated by infrared target simulator, can achieve 8-bit grayscale level with 100 Hz refresh rate. In this paper, we demonstrate an optically heated blackbody emitter (OHBE) for dynamic infrared scene generation to further improve the grayscale level of generated infrared image. The OHBE utilizes the thermal inertness of radiation layer material to integrate the visible light energy, and generates infrared images with 10-bit grayscale value within 1.25 ms. And the infrared images can be collected in a holding time of 3.75 ms in each refresh cycle. The temperature range of the generated infrared image is 108 K, and the temperature resolution corresponds to a unit grayscale value is 0.1 K. The OHBE proposed has been successfully applied to an infrared target simulator.

The area-reconstruction h-dome technique and its efficient Python implementation for improved particle size image analysis.

The traditional watershed segmentation methods usually suffer from over segmentation for irregularly shaped particles. This is because the distance map of an irregularly shaped particle contains multiple local maxima, and over segmentation would happen if these local maxima were used as seeds for watershed segmentation. In this work, several methods based on morphological reconstruction, including h-dome transform, h-maxima, and area-reconstruction h-dome transform, are introduced to merge, or erase redundant local maxima, and the performance of these methods in avoiding over segmentation is compared. The results show that the area-reconstruction h-dome transform is the most effective method in controlling over segmentation among the evaluated methods. However, the area-reconstruction h-dome transform is achieved by superposition of binary reconstructions at each grayscale level, which is extremely time-consuming and impractical for batch processing. A hybrid pixel-queue algorithm is applied to accelerate the area-reconstruction h-dome transform, and the algorithm is implemented in Cython to further improve the computational efficiency. For a 2592 × 1944 pixel image, on a PC with an Intel Core i5 2.4GHz processor and 8GB RAM, the processing time of the area-reconstruction h-dome transform after acceleration is about 549 ms, which is 249 times faster than the unaccelerated algorithm and 4 times faster than the reconstruction function in the Scikit-image library (an open-source image processing library for the Python programming language) which performs reconstruction by dilation. The accelerated area-reconstruction h-dome transform algorithm was successfully applied to the segmentation of rubber particles in a thermoplastic polyolefin (TPO) compound. RESEARCH HIGHLIGHTS: Techniques for segmenting particles with irregular shapes based on morphological reconstruction are reviewed. A fast algorithm for area-reconstruction h-dome transform is introduced based on Vincent's first approach combined with the pixel queue algorithm and Cython acceleration. The accelerated reconstruction algorithm is 249 times faster than the unaccelerated algorithm. The fast area-reconstruction h-dome transform algorithm is successfully applied to rubber particle segmentation of a thermal plastic polyolefin.

Reversible Data Hiding in Palette Images

Reversible data hiding (RDH) has been investigated for over two decades. Depending on the application scenario, it can be divided into RDH and reversible data hiding in encrypted images (RDHEI). Interestingly, almost all studies on RDH/RDHEI have been conducted on gray-scale images, and relatively few studies have been conducted on color images compared to those on gray-scale images. Moreover, very few studies have been undertaken on palette images as a widely used image format. For palette images in which pixels are not gray-scale levels but color table indexes, it is difficult to apply traditional RDH/RDHEI methods directly. Therefore, we propose a new framework for RDH/RDHEI specifically for palette images. This framework adds a route selection algorithm before the conventional RDH/RDHEI approach. To be specific, we propose a method named the shortest route with correlation and frequency selection to reorder the color table, followed by a correlation reconstruction of the remapped index image according to this color table. The experimental results show the improvement brought by our proposed route selection to the existing RDH/RDHEI methods in palette images.

Hybrid reflected-ultrasound computed tomography versus B-mode-ultrasound for muscle scoring in spinal muscular atrophy.

Novel light- and sound-based technologies like multispectral optoacoustic tomography (MSOT) with co-registered reflected-ultrasound computed tomography (RUCT) could add additional value to conventional ultrasound (US) for disease phenotyping in pediatric spinal muscular atrophy (SMA). The aim of this study was to investigate the quality of RUCT compared to US for qualitative and quantitative assessment of imaging neuromuscular disorders. Subanalyzing the MSOT SMA study, 288 RUCT and 276 US images from 10 SMA patients (mean age 9.0±3.7) and 10 gender- and age-matched healthy volunteers (HV; mean age 8.7±4.3) were analyzed for quantitative (grayscale levels [GSLs]) and qualitative (echogenicity, distribution pattern, Heckmatt scale, and muscle texture) muscle changes. RUCT and US measures were further correlated with clinical standard motor outcomes. Quantitative agreement using GSLs revealed significantly higher GSLs in muscles of SMA patients compared to healthy muscles in both techniques (US mean GSL [SD] SMA vs. HV: 110.70 [27.8] vs. 68.85 [19.2], p<.0001; RUCT mean GSL [SD] SMA vs. HV: 91.81 [21.8] vs. 59.86 [8.2], p<.0001) with good correlation with motor outcome tests, respectively. Qualitative agreement between methods for muscle composition was excellent for differentiation of pathological versus healthy muscles, echogenicity, and distribution pattern, moderate for Heckmatt scale, and poor for muscle texture. The data suggest that RUCT may allow the assessment of basic qualitative and quantitative measures for muscular diseases with comparable results to conventional US.

Sinus augmentation with poly(ε)caprolactone-β tricalcium phosphate scaffolds, mesenchymal stem cells and platelet rich plasma for one-stage dental implantation in minipigs.

This study evaluated the efficacy of a tube-shaped poly(ε) caprolactone - β tricalcium phosphate (PCL-TCP) scaffold with the incorporation of human umbilical cord-derived mesenchymal stem cells (hUCMSCs) and platelet-rich plasma (PRP) for bone regeneration in the procedure of single-stage sinus augmentation and dental implantation in minipigs. Implants were placed in the bilateral sides of the maxillary sinuses of 5 minipigs and allocated to a PCL-TCP+hUCMSCs+PRP group (n=5), a PCL-TCP+PRP group (n=5), and a PCL-TCP-only group (n=6). After 12 weeks, bone regeneration was evaluated with soft X-rays, micro-computed tomography, fluorescence microscopy, and histomorphometric analysis. Four implants failed (2 each in the PCL-TCP+hUCMSCs+PRP and PCL-TCP+hUCMSC groups). An analysis of the grayscale levels and bone-implant contact ratio showed significantly higher mean values in the PCL-TCP+hUCMSCs+PRP than in the PCL-TCP group (P=0.045 and P=0.016, respectively). In fluoromicroscopic images, new bone formation around the outer surfaces of the scaffolds was observed in the PCL-TCP+hUCMSCs+PRP group, suggesting a tenting effect of the specially designed scaffolds. Bone regeneration at the scaffold-implant interfaces was observed in all 3 groups. Using a tube-shaped, honeycombed PCL-TCP scaffold with hUCMSCs and PRP may serve to enhance bone formation and dental implants' osseointegration in the procedure of simultaneous sinus lifting and dental implantation.

Helium radiation blistering mechanisms in tungsten: Ion channeling effects

Understanding the radiation damage of low-energy and high-fluence helium ions on tungsten surface is of great interest and significance for developing advanced plasma-facing materials. Here, we investigate the effect of ion channeling on helium radiation blistering using a Helium Ion Microscope. 30 keV helium ions were implanted into individual tungsten grains to a fluence of 1×1018 ions/cm2 at room temperature. The grains of interest were selected based on the grayscale levels of ion channeling contrast image. It has been found that the channeling and non-channeling grains exhibit distinct blistering behaviors in both surface morphology and subsurface microstructure. In contrast with the channeling blister, the non-channeling one is smaller, shallower in irradiated bubble region, and composed of more and larger cracks beneath the surface. The underlying mechanisms were discussed based on the Molecular Dynamic simulations, which might have implications for designing and fabricating plasma-resistant materials through texture and anisotropy tailoring.

Intensification of mixing in an ultrasonic flow reactor

The aim of the project including this study is to provide a methodology facilitating the scaling up of ultrasonic flow reactors. To achieve this goal, the philosophy consists in developing lab size ultrasonic flow reactors, in which chemical process could be developed, stackable in parallel to reach industrial production volumes. The first step of this research project is to characterize the flow and the mixing within the 30 kHz vibrating walls of a millimetric-sized channel prototype specifically developed for this project. Mixing performances of this channel are measured by imaging the mixing between distilled water and brilliant blue dye solution thanks to a camera. Images grey scale levels are linked to concentration field of brilliant blue in water thanks to a calibration. Tests are done with and without ultrasonic waves for Reynolds number ranging from 5.2 to 130.6 and electrical power of the ultrasonic transducers ranging from 4 to 64 W. The study has shown mixing enhancement thanks to ultrasound. The mixing quality decreases as flow rate increases while asymptotically increases as the electrical power increase. The optimal electrical power divided by the flow rate has been found equal to 260 J/ml for the best reached mixing efficiency equal to 0.9 (maximum theoretical mixing efficiency is 1).

Detection and classification of large bowel obstruction from X‐ray images using machine learning algorithms

AbstractLarge bowel obstruction (LBO) occurs when there is a blockage or twisting in the large bowel that prevents wastes and gas from passing through. If left untreated, the blockage cuts off blood supply to the colon, causing sections of it to die which results in high rates of morbidity and fatality. The examination of clinical symptoms of LBO involves careful inspection of the cecum and colon. Radiologists use X‐rays to inspect the clinical signs. Some research has been done to automate the detection of related abdominal and intestinal diseases. However, all these studies concentrate only on detecting Crohn's, ulcerative colitis, Acute Appendicitis, colorectal cancer, celiac diseases, liver diseases, and chronic kidney diseases. Automatic detection and classification of LBO has not been given due attention so far to the best of the authors knowledge. To address this challenge, we have designed a model for the detection and classification of LBO. The models development comprises of stages such as preprocessing, detection, segmentation, feature extraction, and classification. We used YOLOv3 for detection and used a gray scale level co‐occurrence matrix (GLCM), and a convolutional neural network for feature extraction, while support vector machine (SVM) and softmax were used for classification. The proposed model achieved a diagnostic accuracy of 89% when feature extraction methods such as CNN and median filter with softmax classifier were used. CNN and Gaussian filter with soft max classifier achieved 91%, while CNN and anisotropic filter with soft max classifier achieved 92%. GLCM with threshold segmentation and Gaussian filter with SVM classifier achieved 87%, while CNN with watershed segmentation and Gaussian filter with SVM classifier achieved 97% and CNN‐GLCM with watershed segmentation and anisotropic diffusion filter with SVM classifier achieved 98% for detection and classification of LBO. Finally, this paper presented a performance analysis of various machine learning approaches for detection and classification of LBO. Hence, our model is designed to assist human experts (Radiologists) in diagnosing LBO.

Are There Differences in the Homogeneity of the Parts of Tablets Obtained after Subdivision?-A Preliminary Assessment Using an X-ray Microtomography.

Aim: The study aimed to analyze the weight and homogeneity of the parts of tablets containing carbamazepine and tablets with trazodone hydrochloride, obtained after subdivision with a kitchen knife. X-ray microtomography was used for homogeneity analysis. Methods: 30 tablets with carbamazepine and 30 tablets with trazodone hydrochloride were analyzed in terms of weight uniformity after subdivision. Then, seven tablets of each type were analyzed using an X-ray microtomography (Phoenix vǀtomeǀx, General Electric). The absorption of X-rays by an object is proportional to its density. In turn, measurement of the density of the analyzed object in a microtomographic image is the grayscale level. Based on the correlation between the grayscale value and the reference density, from the calibration phantom, we were able to determine the density of any area of the tablet’s scan. Results: During the subdivision, the weight loss exceeded 3% for two carbamazepine tablets, while for trazodone tablets, none lost more than 3%, which is the limit recommended by Food and Drug Administration (FDA). As to the density of the tablets resulting from the microtomographic analysis, two of the whole tablets containing trazodone hydrochloride had a significantly higher density than the remainder (p < 0.001). Similarly, some differences in density were observed in the analysis of the density of tablets of carbamazepine (p = 0.008). Parts of one of the analyzed tablets with trazodone obtained after subdivision differed in terms of pixel brightness, thus density. On the other hand, the uniform density was observed for parts of the split tablets containing carbamazepine. Conclusions: Parts of the trazodone hydrochloride tablets obtained after subdivision differed in terms of homogeneity and weight. Microtomographic methods may be an interesting and useful method for evaluating the uniformity of compounds in solid dosage forms.

Radiographic image enhancement based on a triple constraint U-Net network

Radiographic testing (RT) images of complex components are affected by several factors, including low greyscale levels, low contrast and blur. These factors can significantly restrict the accuracy and effectiveness of defect recognition. To address this issue, this paper proposes a radiographic image enhancement method based on a triple constraint U-Net network. Firstly, a radiographic image preprocessing target dataset is constructed based on conventional image preprocessing technology and previous experience. The U-Net model is then used to design a model loss function, including the parameters of image consistency, texture consistency and structural similarity, in order to achieve structure preservation and noise removal in the images. Finally, radiographic images of actual complex components are used to illustrate and verify the effectiveness of the proposed method. The results show that the proposed method can effectively convert an original image to a target image, enhance the details of the defect area and improve the accuracy of defect recognition by 5.2%.

Segmentation Methods for MRI Human Spine Images using Thresholding Approaches

Computer-Aided Diagnosis (CAD) in MRI image processing can assist experts in detecting abnormality in human spine image efficiently. The manual process of detecting abnormality is tedious, hence the use of CAD in this field is helpful to increase the diagnosis efficiency. The segmentation method is an important and critical process in CAD that could affect the accuracy of the MRI spine image’s overall diagnosis. There are various segmentation methods commonly used in CAD. One of the methods is segmentation using thresholding. Thresholding approaches divide the area of interest by identifying the threshold values that can separate the image into desired grayscale levels based on its pixel’s intensity. This study focuses on investigating the optimal approach in segmenting lumbar vertebrae on the MRI images. The steps involved in this study include pre-processing (normalization), segmentation using local and global thresholding, neural network classification, and performance measurement. 20 images are used to evaluate and compare the segmentation methods. The effectiveness of the segmentation method is measured based on the performance measurement technique. This preliminary study shows that local thresholding outperforms the global thresholding approach with an accuracy of 91.4% and 87.7%.

A quantum segmentation algorithm based on local adaptive threshold for NEQR image

The classical image segmentation algorithm based on local adaptive threshold can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem gradually emerges. In this paper, a quantum segmentation algorithm based on local adaptive threshold for NEQR image is proposed, which can use quantum mechanism to simultaneously compute local thresholds for all pixels in a gray-scale image and quickly segment the image into a binary image. In addition, several quantum circuit units, including median calculation, quantum binarization, etc. are designed in detail, and then a complete quantum circuit is designed to segment NEQR images by using fewer qubits and quantum gates. For a [Formula: see text] image with [Formula: see text] gray-scale levels, the complexity of our algorithm can be reduced to O[Formula: see text], which is an exponential speedup compared to the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.

Online circular contrast perimetry via a web-application: optimising parameters and establishing a normative database.

PurposeTo establish a normative database and optimise parameters for personal-computer based perimetry via a web-application using circular contrast targets.MethodsOnline 24-degree 52-loci perimetry was delivered through a web-application using circular flickering contrast targets. Embedding contrast differentials within targets allows calculation of relative decibel (rdB) per 256-bit greyscale level differential. Target light-band maximum brightness colour was fixed, while the dark-band varied to achieve the desired rdB level. A staircase system was used with two reversals ranging from 0 to 36 rdB levels. Blind spot localisation at the start of the test was used to optimise viewing distance and subsequently count fixation losses. Gaze was maintained on a spinning golden star which moves mid-test to maximise sampling area. Patients performed the test to each eye separately using a computer in a darkened room at 40–45 cm.Results158 eyes of 101 patients completed the Online Circular Contrast Perimetry (OCCP) test. Mean age was 62.9 ± 14.3 years old. Mean sensitivity reduced with age, at 1.0 relative decibel per decade. Mean sensitivity per locus correlated with standard automated perimetry (SAP) in a physiological hill of vision, with an average difference of 4.02 decibels (95% confidence interval (CI) = 3.77–4.27, p < 0.001) and good agreement between tests.ConclusionsOnline circular contract perimetry provides accurate perimetric testing with comparable results to standard automated perimetry.

The diagnosis of COVID-19 in CT images using hybrid machine learning approaches (CNN &amp; SVM)

The new coronavirus disease (2019) has spread quickly as an acute respiratory distress syndrome (ARDS) among millions of individuals worldwide. Furthermore, the number of COVID-19 checking obtainable in hospitals is very limited as compared to the rising number of infections every day. As an outcome, an automatic detection system must be implemented as a quick diagnostic tool for preventing or reducing the spread of COVID-19 among humans. The present paper aims to propose an automated system by means of a hybrid Deep Learning ("convolutional neural network"(CNN)) and "support vector machine (SVM)" approach for identifying COVID-19 pneumonia-infected patients on the basis of chest computed tomography (746 CT images of "COVID-19" and "non-COVID-19"). The proposed system is composed of three phases. The first, pre-processing phase begins with converting CT images into greyscale level CT images of equal size (256×256). The "contrast limited adaptive histogram equalization" technology is adopted to enhance the intensity levels, and demonstrate the feature of lung tissue. It is also necessary to normalize the division of the image elements by 255 to make the values between 0 and 1, as this will speed up the processing process. The second phase, the CNN (SimpNet model), was applied as a deep feature extraction technique to identify CT samples. The SVM classifier and SoftMax function are employed in the third phase to classify COVID-19 pneumonia-infected patients. Specificity, Sensitivity, "F-score", Accuracy, and "area under curve" are used as criteria to estimate the efficiency of the classification. The results showed a high accuracy rate of COVID-19 classification which reached (98%) and (99.1%) for CNN-SoftMax and CNN-SVM classifier, respectively in the tested dataset (225 CT images).

An analysis of the meso-structural damage evolution of coal using X-ray CT and a gray-scale level co-occurrence matrix method

To study the meso-damage evolution in coal under loading conditions, the industrial CT scanning tests under the uniaxial compression condition were carried out using an industrial CT scanner equipped with a loading control system. The gray level co-occurrence matrix (GLCM) theory was applied to quantitatively analyze the meso-damage evolution and the fracturing characteristics using the acquired CT images at each scanning stage. Four statistical features (i.e., contrast, energy, correlation, and homogeneity) of the CT images were extracted to evaluate the internal damage evolution in coal. The results show that the contrast generally took a changing trend of first decreasing and then increasing with the closure, initiation, and expansion of the fractures, that is, it decreased slowly at the compaction stage, slightly increased at the elasticity stage, and drastically increased at the post-peak stage. On the contrary, the energy, correlation, and homogeneity showed a changing trend opposite to that of the contrast. The changing tendency of mean values of the four statistical features could be depicted by the Boltzmann function, and in addition, the statistical features followed Gaussian distribution during the complete stress-strain process. The maximum damage cross-section of the coal sample could be transferred with the increase of deformation in the coal sample, indicating that the final failure location may not be the maximum initial damage location. The fracture ratio measured by the binarization and K-Means clustering algorithm methods typical showed three changing stages (i.e., a slow reduction process, a slight increase process, and a dramatic increase process) and could be used to reflect the meso-damage development process in coal at different deformation stages.

Model Design of The Image Recognition of Lung CT Scan for COVID-19 Detection Using Artificial Neural Network

COVID-19 has become a pandemic and is a big problem that needs to be checked out immediately. CT scan images can explain the lung conditions of COVID-19 patients and have the potential to be a clinical diagnostic tool. In this research, we classify COVID-19 by recognizing images on a computer tomography scan (CT scan) of the lungs using digital image processing and GLCM feature extraction techniques to obtain grayscale level values in CT images, followed by the creation of an artificial neural network model. So that the model can classify CT scan images, the results in this research obtained the most optimal model for COVID-19 classification performance with 90% accuracy, 88% precision, 91% recall, and 90% F1 score. This research can be a useful tool for clinical practitioners and radiologists to assist them in the diagnosis, quantification, and follow-up of COVID-19 cases.

Influence of Different Fabrication Techniques on the Accuracy of Radiographic Scan Templates in Cases of Full-Arch Computer-Guided Implant Placement: An In Vitro Study.

To evaluate the influence of using different fabrication techniques, including intraoral scans, CBCT scans of patients' existing dentures, or denture duplicates, on the accuracy of radiographic scan templates. The influence of selecting different segmentation threshold values during reconstruction of CBCT data was also evaluated. A reference model was obtained by scanning five pairs of maxillary and mandibular acrylic complete dentures using a desktop laboratory laser scanner (DWOS 7Series, Dental Wings). Test scans were obtained from intraoral scans of dentures, CBCT scans of dentures and denture duplicates reconstructed at different grayscale segmentation thresholds, and a laboratory scan of denture duplicates. The resultant STL scan files were imported to an open source and cloud storage software (Medit Link) for the accuracy measurements by calculating root mean square estimate (RMSE) between reference and test scans. Collected data were then analyzed. Qualitative analysis was also performed using 3D color maps. The lowest RMSE (352.7) was found with intraoral denture scans. The highest RMSE (1,336.3) was found with the CBCT scans of the denture duplicates at segmentation threshold of -700 grayscale level. Qualitative analysis revealed that the intraoral denture scans exhibited the most homogenous deviation pattern relative to reference lab scans. Within the limitations of this study, the intraoral scans of the patients' existing dentures resulted in the fabrication of the most accurate radiographic scan templates. The improved accuracy of scan templates fabricated using the intraoral scanners can eliminate the possible laboratory errors associated with the conventional technical procedures as well as reduce the inaccuracies resulting from the image processing and segmentation of CBCT data.

Quantum Image Segmentation Based on Grayscale Morphology

The classical image segmentation algorithm based on grayscale morphology can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem will emerge. In order to solve this problem, a quantum image segmentation algorithm is proposed in this paper, which can use quantum mechanism to simultaneously perform morphological operations on all pixels in a grayscale image, and then quickly segment the image into a binary image. In addition, several quantum circuit units, including dilation, erosion, bottom hat transformation, top hat transformation, etc., are designed in detail, and then they are combined together to construct the complete quantum circuits for segmenting the NEQR images. For a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{n} \times 2^{n}$</tex-math></inline-formula> image with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> grayscale levels, the complexity of our algorithm can be reduced to O <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(n^{2}+q)$</tex-math></inline-formula> , which is an exponential speedup than the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.

3D Relief Cutting Process Using 2D Image Luminosity Grayscaling on The G-Weike WK1212 CNC Router Machine

This study implements the image litophane processing method to convert 2D images into 3D designs using image luminosity grayscaling techniques, so that the design process as input data for CNC router machining operations can be carried out quickly to support rapid prototyping technology. Furthermore, this research will process the 3D design into a 3D engraving object with a relief cutting process using a G-Weike WK1212 CNC router machine. Using the image luminosity grascaling technique, the color composition value is obtained to change the grayscale color image by 33% red, 59% green and 11% blue with a luminosity value of 35% or 89 from the range of luminosity grayscale levels 0 - 255. These results are further increased by 50% to 172 or 67% of the range of grayscale luminosity levels L[0, 255] so that it will be at the ideal luminosity value for the relief cutting process. Using a luminosity value of 172 from a grayscale image, the actual depth value is 1.82mm from a total depth of 3mm, which of course the relief pattern will look good.