Computed Tomography Image Processing Research Articles

Application of Different Image Processing Methods for Measuring Rock Fracture Structures under Various Confining Stresses

Fractures within granite may become channels for fluid flow and have a significant impact on the safety of waste storage. However, internal aperture variation under coupled conditions are usually difficult to grasp, and the inevitable differences between the measured data and the real fracture structure will lead to erroneous permeability predictions. In this study, two different CT (Computed Tomography) image processing methods are adopted to grasp internal fractures. Several CT images are extracted from different positions of a rock sample under different confining stresses. Two critical factors, i.e., aperture and the contact area ratio value within a single granite fracture sample, are investigated. Results show that aperture difference occurs under these two image processing methods. The contact area ratio value under two image processing methods has less than 1% difference without confining stress. However, there is larger than ten times difference when the confining stress increases to 3.0 MPa. Moreover, the edge detection method has the capability to obtain a relatively accurate internal fracture structure when confining pressure is applied to the rock sample. The analysis results provide a better approach to understanding practical rock fracture variations under various conditions.

ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing

ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing

Mechanism of asphalt concrete reinforced with industrially recycled steel slag from the perspectives of adhesion and skeleton

This research investigated the reinforcement mechanism of steel slag recycling in asphalt concrete from the perspective of adhesion and skeleton. Bonding performance between steel slag and bitumen was assessed by the calculation of coating area and peeling area, as well as the area detection by Scanning Electron Microscope (SEM) and Energy Dispersive Spectrometer (EDS). Computed Tomography (CT) scanning was used to determine the bitumen absorption characteristics of steel slag. Mechanical characteristics of steel slag mixture during compaction period and service period were evaluated by continuous compression test with Universal Testing Machine (UTM-25). A combination of CT scanning and image processing technology were used to quantify skeleton contact characteristics. The results indicated that bonding performance of bitumen with steel slag was obviously better than basalt after long-term erosion. The surface pores of steel slag absorbed excessive bitumen as anchoring and embedding effect, accounting for 20.24% of the total steel slag bitumen absorption in maximum. Steel slag skeleton of SMA gradation was found easily to be compacted during construction period and more resistant to loading during service period. In addition, there were more contact points in steel slag asphalt concrete, resulting that steel slag asphalt concrete had better stability. The contact points were different in different particle size ranges, and it was suggested that steel slag with particle size of 9.5–16.0 mm and 2.36–4.75 mm should be preferred.

Preoperative 3-dimensional computed tomography bone density measures provide objective bone quality classifications for stemless anatomic total shoulder arthroplasty

Reproducible methods for determining adequate bone densities for stemless anatomic total shoulder arthroplasty (aTSA) are currently lacking. The purpose of this study was to evaluate the utility of preoperative computed tomography (CT) imaging for assessing the bone density of the proximal humerus for supportive differentiation in the decision making for stemless humeral component implantation. It was hypothesized that preoperative 3-dimensional (3-D) CT bone density measures provide objective classifications of the bone quality for stemless aTSA. A 3-part study was performed that included the analysis of cadaveric humerus CT scans followed by retrospective application to a clinical cohort and classification with a machine learning model. Thirty cadaveric humeri were evaluated with clinical CT and micro-CT (μCT) imaging. Phantom-calibrated CT data were used to extract 3-D regions of interest and defined radiographic scores. The final image processing script was applied retrospectively to a clinical cohort (n=150) that had a preoperative CT and intraoperative bone density assessment using the "thumb test," followed by placement of an anatomic stemmed or stemless humeral component. Postscan patient-specific calibration was used to improve the functionality and accuracy of the density analysis. A machine learning model (Support vector machine [SVM]) was utilized to improve the classification of bone densities for a stemless humeral component. The image processing of clinical CT images demonstrated good to excellent accuracy for cylindrical cancellous bone densities (metaphysis [ICC=0.986] and epiphysis [ICC=0.883]). Patient-specific internal calibration significantly reduced biases and unwanted variance compared with standard HU CT scans (P<.0001). The SVM showed optimized prediction accuracy compared with conventional statistics with an accuracy of 73.9% and an AUCof0.83 based on the intraoperative decision of the surgeon. The SVM model based on density clusters increased the accuracy of the bone quality classification to 87.3% with an AUC of 0.93. Preoperative CT imaging allows accurate evaluation of the bone densities in the proximal humerus. Three-dimensional regions of interest, rescaling using patient-specific calibration, and a machine learning model resulted in good to excellent prediction for objective bone quality classification. This approach may provide an objective tool extending preoperative selection criteria for stemless humeral component implantation.

A Novel Framework for Multimodal Brain Tumor Detection with Scarce Labels.

Brain tumor detection has advanced significantly with the development of deep learning technology. Although multimodal data, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), has potential advantages in diagnostics, most existing studies rely solely on a single modality. This is because common fusion methods may lead to the loss of critical information when attempting multimodal fusion. Therefore, effectively integrating multimodal data has become a significant challenge. Additionally, medical image analysis requires large amounts of annotated data, and labeling images is a resourceintensive task that demands experienced professionals to spend a considerable amount of time. To address these challenges, this paper introduces a new unsupervised learning framework named Double-SimCLR. This framework builds on the foundation of contrastive learning and features a dual-branch structure, enabling direct and simultaneous processing of MRI and CT images for multimodal feature fusion. Given the "weak feature" characteristics of CT images (e.g., low soft tissue contrast and low resolution), we incorporated adaptive weight masking technology to enhance CT feature extraction. Moreover, we introduced a multimodal attention mechanism, which ensures that the model focuses on salient information, thereby elevating the precision and robustness of brain tumor detection. Even without substantial labeled data, experimental results demonstrate that Double-SimCLR achieves 93.458% accuracy, 92.463% precision, and a 93.058% F1-score, outperforming state-of-the-art (SOTA) models by 2.871%, 2.643%, and 3.098%, respectively.

Unsupervised metal artifacts reduction network for CT images based on efficient transformer

In computed tomography (CT) imaging, artifacts caused by metals can seriously interfere with the accurate judgment, evaluation, and analysis of object structures. Deep learning presented new opportunities for minimizing metal artifacts. The currently proposed supervised methods mainly resort to simulated data for network training, which is difficult to be applied in real scenarios. Unsupervised methods do not completely suppress artifacts and are prone to instability in the corrected results. Therefore, this paper proposes an unsupervised CycleGAN model based on the efficient Transformer for metal artifact reduction in CT images. The encoder and decoder based on the efficient Transformer achieve the extraction and processing of global image content features and artifact features. By correlating global features through attention mechanisms, vital features are generated to enhance the consistency of recovery information. The efficient Transformer framework reduces the computational complexity of the attention mechanism, allowing efficient processing of standard medical CT images with 512 × 512 pixels. Projection preserving loss uses the metal trace outer projection as a fidelity constraint to overcome the problem that unsupervised networks are difficult to train and prone to spurious information. Experimental results indicate that the proposed method outperforms the original CycleGAN and ADN networks in suppressing metal artifacts while avoiding introducing spurious structures. Specifically, the proposed method improved the PSNR of the correction results by 9.23% and reduced the NMAD by 13.21% compared to the original CycleGAN, and improved the PSNR by 5.17% and reduced the NMAD by 5.57% compared to the ADN.

A CT image-driven computational framework for investigating complex 3D fracture in mesoscale concrete

Accurate prediction of fracture in concrete depends on the characterization of real meso-structures. Micro X-ray computed tomography (CT) can fulfil this task but is still too expensive and time-consuming to repetitively generate numerical models, also with massive degrees of freedom (DOFs) that prohibit nonlinear computations. To address such problems, we develop a novel computational framework driven by only one CT image for 3D fracture investigation of realistic mesoscale concrete models. The voxelized CT data of aggregates is first processed to obtain spatially continuous surfaces. A number of aggregates with real shapes from a created library are then randomly packed into containers through compaction and vibration simulated by a dynamic physics engine. In this way, realistic concrete models with various aggregate contents (up to 60%) can be achieved without performing extra CT tests and image processing. To capture potential micro-cracks, cohesive interface elements with zero-thickness are automatically inserted between solid elements. The proposed models with more than 15 million DOFs are well validated through typical uniaxial tension tests, elucidating complex formation mechanisms of micro-cracking, pre-peak nonlinearity, softening behaviour and 3D fracture networks. Quantitative analyses indicate that with the increase of aggregate content, the coupling effect of aggregate blocking and interfacial cracking becomes stronger to make the crack surfaces more tortuous with higher ITZ-mortar area ratios and lower load-carrying capacities, although more aggregates are often required to enhance overall elastic modulus and compressive strength, and reduce carbon footprints of concrete.

Ways of transformation of typical X-ray signs of community acquired pneumonia of viral etiology (COVID-19) according to radiomics data

For 3 years since the announcement of the coronavirus disease (COVID-19) pandemic, doctors around the world have been studying the complications caused by different strains of SARS-CoV-2. To study the structure of the lung parenchyma in patients with a complicated course of community acquired viral pneumonia of COVID-19 and different ways of transformation, the most informative is the digital software processing of computed tomography (CT) images of the chest organs (CT).&#x0D; Objective — to investigate the ways of transformation of typical radiological signs in patients with community-acquired pneumonia of viral etiology (COVID-19) and the possibility of their transformation into bronchioloalveolar cancer (BAC) by the radiomics method.&#x0D; Materials and methods. Chest CT data in the dynamics of 112 patients with a complicated course of community-acquired viral pneumonia COVID-19 were analyzed. Chest CT was performed on an Aquilion TSX-101A Tochiba scanner (Japan) with subsequent digital software processing of CT images using the Dragonfly program from Obyect Research Systems (ORS), Montreal, Canada. The diagnosis of BAC was made based on the data of the pathomorphological examination. Transbronchial biopsy of lung tissue was performed during diagnostic fibrobronchoscopy.&#x0D; Results and discussion. As a result of the analysis of possible ways of transformation of typical X-ray changes of COVID-19 community-acquired pneumonia, we identified 3 main ways. In 71 (64.0 %) subjects, according to the chest CT scan, there was gradual resorption of pathological changes and recovery of the lung parenchyma. In 35 (31.2 %) patients, the formation of signs of «vanishing lung syndrome» was detected. 5 (4.5 %) patients were diagnosed with BAC according to the CT scan and pathomorphological examination. Digital software processing of chest CT in dynamics allows to track the process of transformation of the lung parenchyma structure in patients with a complicated course of COVID-19 community-acquired viral pneumonia into BAC and in some cases to confirm the secondary nature of the oncological process.&#x0D; Conclusions. Digital software processing of the chest CT data is a highly informative research method that clearly reflects the morphological structure of the lung parenchyma and allows diagnosis and differential diagnosis of diseases.

Influence of the Pre-Existing Defects on the Strain Distribution in Concrete Compression Stress Field by the AE and DICM Techniques

This research investigates the influence of the pre-existing defects within concrete taken from the in-service irrigation structure on the strain distribution. The X-ray Computed Tomography (CT) technique is employed to investigate the internal concrete matrix and evaluate the defect distribution in it. The cracking system in a concrete matrix is detected as a damage type caused by the severe environment, and it is varied by the different degrees in all samples. The geometric properties of defects and their spatial location are obtained by image processing of CT images. The compression test with Acoustic Emission (AE) and Digital Image Correlation (DIC) measurements is conducted to analyze the fracture processes and acquire the damage spatial information. The AE signal descriptors are effective parameters for real-time detection and potential local damage monitoring. Moreover, the analysis of the DICM strain and displacement fields reveals the most potential fracture zones. The AE source location analysis indicated a connection between pre-existing defects and strain localization. The AE events and strain are high in the defect areas. Additionally, the amplitude and frequency of the AE events correlated with the location of the defects indicating that the structure weakness at that point leads to concentrated deformation development.

Macro-meso deterioration characteristics and statistical damage constitutive model for acid-corroded sandstone

Long-term hydrochemical corrosion leads to mineral dissolution, meso-structure change, and further mechanical deterioration of rock. This paper studied the effects of pH = 1, 3, 5 HCl solutions and pH = 7H2O on sandstone damage from macro-meso perspectives. The mesostructure from different corroded parts was fine analyzed, and the sandstone porosity was calculated by CT (Computed Tomography) image processing techniques. The mechanical properties and energy evolution laws under uniaxial compression were investigated. The full deformation of sandstone was theoretically analyzed, and the damage constitutive model considering initial compression was developed and verified. The results showed that after acid corrosion, more pores and microcracks occurred in the sandstone, the mesostructure became loose, and the calculated porosity based on CT images can represent the actual sandstone porosity. The yield stress, peak stress, and elastic modulus declined, and the peak strain improved. The energy storage capacity weakened, and the plastic deformation before the peak point increased. The effects were more evident with the acidity and immersion time. The acid-corroded sandstone was abstracted into two portions: the initial voids and the solid skeleton. The initial voids deformed nonlinearly in the compaction stage, and the solid skeleton deformed linearly before yielding and nonlinearly after yielding. The theoretical curves of the constitutive model for acid-corroded sandstone agreed well with the experimental ones, which verifies the model is reasonable.

CT image crack segmentation method based on linear feature enhancement.

Industrial computed tomography (CT) crack segmentation is a key technology in industrial CT image processing. Unfortunately, the interference of artifact and noise in CT image often bring great trouble to the crack segmentation. In order to improve the segmentation accuracy of cracks in CT images, we propose to develop and test a new crack segmentation algorithm based on linear feature enhancement by analyzing the features of cracks in CT images. Firstly, the total variational model is used to denoise the input image. Next, a Frangi multiscale filter is used to extract linear structures in the image, and then the extracted linear structures are used to enhance the contrast of the image. Finally, the cracks in the image are detected and segmented by Otsu algorithm. By comparing with the manual segmentation results, the average intersection-over-union (IOU) reaches 86.10% and the average F1 score reaches 92.44%, which verifies the effectiveness and correctness of the algorithm developed in this study. Overall, experiments demonstrate that the new algorithm improves the accuracy of crack segmentation and it is effective applying to industry CT images.

Dual-domain sparse-view CT reconstruction with Transformers

Purpose:Computed Tomography (CT) has been widely used in the medical field. Sparse-view CT is an effective and feasible method to reduce the radiation dose. However, the conventional filtered back projection (FBP) algorithm will suffer from severe artifacts in sparse-view CT. Iterative reconstruction algorithms have been adopted to remove artifacts, but they are time-consuming due to repeated projection and back projection and may cause blocky effects. To overcome the difficulty in sparse-view CT, we proposed a dual-domain sparse-view CT algorithm CT Transformer (CTTR) and paid attention to sinogram information. Methods:CTTR treats sinograms as sentences and enhances reconstructed images with sinogram’s characteristics. We qualitatively evaluate the CTTR, an iterative method TVM-POCS, a convolutional neural network based method FBPConvNet in terms of a reduction in artifacts and a preservation of details. Besides, we also quantitatively evaluate these methods in terms of RMSE, PSNR and SSIM. Results:We evaluate our method on the Lung Image Database Consortium image collection with different numbers of projection views and noise levels. Experiment studies show that, compared with other methods, CTTR can reduce more artifacts and preserve more details on various scenarios. Specifically, CTTR improves the FBPConvNet performance of PSNR by 0.76dB with 30 projections. Conclusions:The performance of our proposed CTTR is better than the method based on CNN in the case of extremely sparse views both on visual results and quantitative evaluation. Our proposed method provides a new idea for the application of Transformers to CT image processing.

Influence of CT image processing on the predicted impact of pores on fatigue of additively manufactured Ti6Al4V and AlSi10Mg

AbstractPores are inherent to additively manufactured components and critical especially in technical components. Since they reduce the component's fatigue life, a reliable identification and description of pores is vital to ensure the component's performance. X‐ray computed tomography (CT) is an established and non‐destructive testing method to investigate internal defects. The CT scan process can induce noise and artefacts in the resulting images which afterwards have to be reduced through image processing. To reconstruct the internal defects of a component, the images need to be segmented in defect region and bulk material by applying a threshold. The application of the threshold as well as the previous image processing alter the geometry and size of the identified defects. This contribution aims to quantify the influence of selected commercial image processing and segmentation methods on identified pores in several additively manufactured components made of AlSi10Mg and Ti6Al4V as well as in an artificial CT scan. To that aim, gray value histograms and characteristic parameters thereof are compared for different image processing tools. After the segmentation of the processed images, particle characteristics are compared. The influence of image processing and segmentation on the predicted fatigue life of the material is evaluated through the change of the largest pore in each set of data applying Murakami's empirical ‐parameter model.

Computed Tomography Images under Artificial Intelligence Algorithms on the Treatment Evaluation of Intracerebral Hemorrhage with Minimally Invasive Aspiration.

The aim of this study was to investigate the therapeutic effect of minimally invasive aspiration on intracerebral hemorrhage (ICH) and the value of artificial intelligence algorithm combined with computed tomography (CT) image evaluation. Ninety-two patients with intracerebral hemorrhage were divided into experimental group (46 cases, minimally invasive aspiration therapy) and control group (46 cases, traditional craniotomy therapy) according to different treatment methods, and CT image scanning was performed. In addition, a CT image segmentation model of intracerebral hemorrhage based on improved fuzzy C-means clustering algorithm (n-FCM) was proposed to process the CT images of the patients. The results showed that the Dice coefficient of n-FCM algorithm after the addition of salt and pepper noise was 0.89, which was higher than that of traditional algorithm; the average operation time of experimental group was 58.93 ± 5.33 min, which was significantly lower than that of control group (90.21 ± 16.24 min) (P < 0.05); the overall response rate of experimental group was 93.48%, which was significantly higher than that of control group (76.09%) (P < 0.05); one month after operation, the National Institutes of Health Stroke Scale (NIHSS) score of experimental group was 3.89 ± 1.95 points, and the Scandinavian Stroke Scale (SSS) score was 10.67 ± 1.76 points, which was significantly lower than that of control group (P < 0.05); the incidence rate of complications in experimental group was significantly lower than that of control group (P < 0.05). It showed that the n-FCM algorithm was superior to the traditional algorithm in CT image processing, with the advantages of good denoising effect and less running time. Minimally invasive aspiration treatment had the advantages of operation time, convenient operation, and less damage to patients, which was beneficial to postoperative recovery and prognosis of patients.

Efficacy Evaluation of 64-Slice Spiral Computed Tomography Images in Laparoscopic-Assisted Distal Gastrectomy for Gastric Cancer under the Reconstruction Algorithm.

This study was aimed to analyze the application value of the filtered back-projection (FBP) reconstruction algorithm of computed tomography (CT) images in laparoscopic-assisted distal gastrectomy. In this study, 56 patients with gastric cancer were selected as research subjects and randomly divided into the control group (CT-guided laparoscopic radical gastrectomy) and the observation group (CT-guided laparoscopic radical gastrectomy with the FBP reconstruction algorithm), with 28 patients in each group. Fourier transform and iterative reconstruction were introduced for comparison, and finally, the postoperative curative effect and adverse events were compared between the two groups. The results showed that the CT image quality score processed by the FBP reconstruction algorithm (4.31 ± 0.31) was significantly higher than that of the iterative reconstruction method (3.5 ± 0.29) and the Fourier transform method (3.97 ± 0.38) (P < 0.05). The incidences of postoperative wound infection and gastric motility disorder (5.88% and 8.16%, respectively) in the observation group were significantly lower than those in the control group (8.21% and 10.82%, respectively) (P < 0.05). The levels of serum interleukin-6 (IL-6) (280.35 ± 15.08 ng/L) and tumor necrosis factor-α (TNF-α) (144.32 ± 10.32 ng/L) in the observation group after the treatment were significantly lower than those in the control group, which were 399.71 ± 14.19 ng/L and 165.33 ± 10.08 ng/L, respectively (P < 0.05). In conclusion, the FBP reconstruction algorithm was better than other algorithms in the processing of gastric cancer CT images. The FBP reconstruction algorithm showed a good reconstruction effect on CT images of gastric cancer; CT images based on this algorithm helped to formulate targeted surgical treatment plans for gastric cancer, showing a high clinical application value.

Finite element modelling of UHPC under pulsating load using X-ray computed tomography based fiber distributions

The benefits of including fibers in ultra-high performance concrete (UHPC) are attributed to their good bond with the matrix and, hence, an optimal utilization of their properties. At the same time, though, fiber reinforcement may contribute to anisotropy in the composite material and induce weak areas. The influence of the fibers’ orientation on the material properties is a matter of current scientific discourse and it is known to play a vital role in structural design. In the case studies presented herein, mechanical laboratory tests using pulsating load regimes on UHPC with a strength of more than 200 MPa were simulated by use of finite element models. The orientations of the fibers were measured for each test sample prior to failure using an X-ray computed tomography (CT) scanner, and these orientations are explicitly implemented into the model. The paper discusses the methodology of merging data retrieved by CT image processing and state-of-the-art FE simulation techniques Moreover, the CT scanning was carried out throughout the testing procedure, which further enables the comparison of the mechanical tests and the FE models in terms of damage propagation and failure patterns. The results indicate that the overall fiber configuration and behavior of the samples can be realistically modelled and validated by the proposed CT-FE coupling, which can enhance the structural analysis and design process of elements produced with steel fiber reinforced and UHPC materials.

An image processing tool for the detection of anthracycline-induced cardiotoxicity by evaluating the myocardial metabolic activity in [18F]FDG PET/CT

PurposeChemotherapy-induced cardiotoxicity is one of the main complications during and after cancer treatment. While echocardiography is the most used technique in clinical practice to evaluate left ventricular (LV) dysfunction, a multimodal approach is preferred for the early detection of anthracycline-induced cardiotoxicity. In this paper, an image processing tool allowing the qualitative and quantitative analysis of myocardial metabolic activity by [18F]fluorodeoxyglucose (FDG) positron emission tomography computed tomography (PET/CT) images, acquired routinely during and after cancer treatment, is presented.MethodsThe methodology is based on cardiac single photon emission computed tomography image processing protocols used in clinical practice. LV polar maps are created, and quantitative regional values are calculated. The tool was validated in a study group of 24 patients with Hodgkin or non-Hodgkin lymphoma (HL and NHL, respectively) treated with anthracyclines. Staging, interim and end-of-treatment [18F]FDG PET/CT images were acquired and the presented tool was used to extract the quantitative metrics of LV metabolic activity.ResultsResults show an overall increase of metabolic activity in the interim PET image acquired while on treatment compared to staging PET, which then decreased in the end-of-treatment scan. Positive correlation coefficients between staging and interim scans, and negative correlation coefficients between interim and end-of-treatment scans also support this finding. Metabolic changes occur predominantly in the septal region.ConclusionThe proposed methodology and presented software solution provides the capability to assess quantitatively myocardial metabolism acquired by routine [18F]FDG PET/CT scanning during cancer treatment for evaluating anthracycline-induced cardiotoxicity. The [18F]FDG PET/CT septal-lateral uptake ratio is proposed as a new quantitative measure of myocardial metabolism.

Use of X-ray tomography for examining root architecture in soils

Despite the critical importance of roots within soils for supporting plant growth, the assessment of root distribution in soils remains difficult and much is unknown regarding their behaviour. In this review, we examine X-ray computed tomography (CT) as a non-invasive method for examining root distribution in soils. X-ray CT enables three-dimensional reconstruction of soil cores to accurately estimate a wide range of features within the soil, including roots, not only examining changes spatially but also temporally. With the development of high-end X-ray CT systems and image processing algorithms, this approach can now be used to examine a range of factors, including root system architecture, soil-root interactions, soil pore architecture, soil biophysical interactions, and soil microorganism behaviour. In addition, we examine the use of synchrotron-based X-ray CT which has been used to provide better resolution, larger sample analysis, faster scanning, and images with greater contrast compared to conventional systems.

Basic features of VIC-T dosimeter with spiral CT readout, CT scanning conditions and data processing with a new polyGeVero-CT software package

This work reports on the results obtained for VIC-T dosimeter for radiotherapy dosimetry (17 % (w/v) N-vinylpyrrolidone, 8 % (w/v) N,N′-methylenebisacrylamide, 12 % (w/v) tert-butyl alcohol, 5 % (w/v) gelatine, 0.02 % (w/v) hydroquinone and 14 mM tetrakis (hydroxymethyl) phosphonium chloride (THPC)) measured with a spiral computed tomography (CT). Optimal spiral CT protocol, CT measurements conditions and image processing are provided. Basic features of VIC-T are discussed such as a calibration relation and dose resolution with respect to former N-vinylpyrrolidone containing dosimeters. Both adopted data processing performed with a new polyGeVero®-CT software package and its features are reported.

Development of Computed Tomography Image Processing Procedure for the Diagnosis of Human Respiratory Infectious Diseases: COVID-19

Computed Tomography (CT) is a non-invasive method to give CT images of every part of the human body without superimposition of end-to-end structures. Some issues in measurements with CT are limiting too few parameters like quantum noise, beam hardening, X-ray scattering by the patient, and nonlinear partial volume effects. Image processing with Adobe Photoshop, ImageJ, and Origin software have been used to achieve good quality images for numerical analysis. Statistical functions permit to investigate the general characteristics of a human respiratory infections disease. Using Automatic Diagnosis system, differentiation in diseases can be filtered out with the help of CT images. Data can be analyzed from the CT images to distinguish between a human respiratory infections disease, a common disorder like Major Depression (MD) or Obsessive-Compulsive Disorder (OCD) and a normal lung.