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

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Boopathi M ◽  
◽  
Khanna D ◽  
Vennila R ◽  
Rajan R ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Quantum Noise ◽  
Respiratory Infections ◽  
Ct Images ◽  
Normal Lung ◽  
Obsessive Compulsive ◽  
Beam Hardening ◽  
Compulsive Disorder ◽  
Computed Tomography Image

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.

scholarly journals Usefulness of Computed Tomography Image Processing by OsiriX Software in Detecting Wooden and Bamboo Foreign Bodies

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Shinpei Matsuda ◽  
Hitoshi Yoshimura ◽  
Hisato Yoshida ◽  
Takashi Ryoke ◽  
Takashi Yoshida ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Foreign Bodies ◽  
Three Dimensional ◽  
Ct Images ◽  
Ct Scanner ◽  
Multidetector Row Ct ◽  
Look Up Table ◽  
Tomography Image ◽  
Computed Tomography Image

Objective. The aim of this study was to evaluate the usefulness of reconstructed computed tomography (CT) images using OsiriX software in detecting wooden and bamboo foreign bodies. Methods. Four sizes of wet and dry wooden and bamboo foreign bodies were selected to be analyzed. Those in the air and in the head of edible swine were scanned with a multidetector row CT scanner. The images were evaluated with OsiriX software in the bone and the abdomen window setting as unprocessed images. Three-dimensional rendered images assigned colors and opacity by a 16-bit color look-up table (CLUT) editor in OsiriX software were evaluated as processed images. Results. In the unprocessed images, dry and wet foreign bodies in the air were not detected except a part of wet wooden foreign bodies, and all the dry and wet foreign bodies in the swine’s head mimicked air with linear shapes. In the processed images, all the dry and wet foreign bodies in the air were detected clearly, and all the wooden and some of the bamboo foreign bodies in the swine’s head were detected clearly. Conclusions. CT images processed using OsiriX software, especially with a CLUT editor, were useful in detecting wooden and bamboo foreign bodies.

scholarly journals Lung Nodule Detection using Segmentation Approach for Computed Tomography Scan Images

2021 ◽  
Vol 9 (9) ◽  
pp. 1778-1790
Author(s):  
Henil Satra
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Ct Scan ◽  
Lung Nodule ◽  
Morphological Operations ◽  
Lung Nodule Detection ◽  
Contour Segmentation ◽  
Nodule Detection ◽  
Segmentation Approach ◽  
Computed Tomography Image

Abstract: Lung disorders have become really common in today’s world due to growing amount of air pollution, our increased exposure to harmful radiations and our unhealthy lifestyles. Hence, the diagnosis of lung disorders has become of paramount importance. The commonly used Thresholding approaches and morphological operations often fail to detect the peripheral pathology bearing areas. Hence, we present the segmentation approach of the lung tissue for computer aided diagnosis system. We use a novel technique for segmentation of lungs from CT scan (Computed Tomography) of the chest or upper torso. The accuracy of analysis and its implication majorly depends on the kind of segmentation technique used. Hence, it is important that the method used is highly reliable and is successful in nodule detection and classification. We use MATLAB and OpenCV libraries to apply segmentation on CT scan images to get the desired output. We have also created a working proprietary user interface called “PULMONIS” for the ease of doctors and patients to upload the CT scan images and get the output after the image processing is done in the backend. Keywords: Lung nodule detection, Image Processing, Computed Tomography, Image Segmentation, Lung Cancer, Contour Segmentation, MATLAB, OpenCV, Computer Vision.

MORPHOLOGICAL SEGMENTATION OF THE KIDNEYS FROM ABDOMINAL CT IMAGES

2014 ◽  
Vol 14 (05) ◽  
pp. 1450073 ◽  
Author(s):  
AICHA BELGHERBI ◽  
ISMAHEN HADJIDJ ◽  
ABDELHAFID BESSAID
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Ct Images ◽  
Second Step ◽  
Watershed Algorithm ◽  
Morphological Filters ◽  
Abdominal Ct ◽  
Morphological Segmentation ◽  
Kidney Segmentation ◽  
Anatomical Information

The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of kidneys from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of kidneys from CT images is usually a difficult task. This difficulty is the gray's level which is similar to the spine level. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the spine by applying morphological filters. This first step makes the extraction of interest regions easier. This step is fulfilled by using various transformations such as the geodesic reconstruction. In the second step, we apply the watershed algorithm controlled by marker for kidney segmentation. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm.

scholarly journals Computed Tomography Image Feature under Intelligent Algorithms in Diagnosing the Effect of Humanized Nursing on Neuroendocrine Hormones in Patients with Primary Liver Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiujie Wang ◽  
Lin Liu ◽  
Na Ma ◽  
Xinxin Zhao
Keyword(s):  
Computed Tomography ◽  
Liver Cancer ◽  
Primary Liver Cancer ◽  
Ct Images ◽  
Image Feature ◽  
Control Group ◽  
Intelligent Algorithms ◽  
Residual Coding ◽  
Computed Tomography Image ◽  
Experimental Group

This study was to explore the application value of computed tomography (CT) images processed by intelligent algorithm denoising in the evaluation of humanized nursing in postoperative neuroendocrine hormone changes in patients with primary liver cancer (PLC). In this study, a simple-structured recursive residual coding and decoding (RRCD) algorithm was constructed on the basis of residual network, which can effectively remove artifacts and noise in CT images and can also restore image details and lesion features well. In addition, 60 postoperative patients with primary liver cancer were collected and divided into routine nursing control group (30 cases) and humanized nursing experimental group (30 cases). After a period of nursing, CT images based on intelligent algorithms were evaluated by determining the hormone content. The results showed that the focal necrosis rate (FNR) of the experimental group was 6%. The adrenocorticotropic hormone (ACTH) levels of 6 and 15 days after admission (T3 and T4) were 41.25 ± 3.81 pg/mL and 19.55 ± 1.72 pg/mL, respectively. The cortisol levels of days 6, 15, and 30 after admission (T3, T4, and T5) were 424.86 ± 16.82 nmol/L, 277.98 ± 14.36 nmol/L, and 241.53 ± 13.27 nmol/L, respectively. Estradiol levels were 53.48 ± 11.19 pg/mL, 41.64 ± 9.28 pg/mL, and 30.59 ± 8.16 pg/mL, respectively. Testosterone levels were 2.18 ± 1.14 ng/mL, 1.78 ± 1.03 ng/mL, and 1.42 ± 0.69 ng/mL, respectively. Self-Rating Anxiety Scale (SAS) scores were 40.24 ± 5.81 points, 36.55 ± 5.02 points, and 32.53 ± 4.8 points, respectively. There were 24 cases, 27 cases, 23 cases, and 21 patients who followed no smoking and drinking, taking medication on time, diet control, and self-monitoring. The scores of physical function, self-cognition, emotional function, and social function were 62.59 ± 6.82 points, 69.26 ± 8.14 points, 73.89 ± 6.35 points, and 66.88 ± 7.04 points, which were better than those of the control group in all aspects ( P < 0.05 ). In short, the humanized nursing course can enhance the compliance of the patients after the surgery, improve the quality of life, and inhibit the anxiety and depression of the patients, so it showed a positive effect on the neuroendocrine hormones and the prognosis of the patients.

scholarly journals Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy

2021 ◽  
Vol 11 ◽  
Author(s):  
Ge Ren ◽  
Sai-kit Lam ◽  
Jiang Zhang ◽  
Haonan Xiao ◽  
Andy Lai-yin Cheung ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Radiation Therapy ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Contrast Enhancement ◽  
Lung Tissue ◽  
Ct Images ◽  
Normal Lung ◽  
Ct Contrast

Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image preparation, image processing, and proposed convolutional neural network) was adopted to extract features from 3D CT images and synthesize perfusion as estimations of regional lung function. Ablation experiments were performed to assess the effects of each framework component by removing each element of the framework and analyzing the testing performances. Major results showed that the removal of the CT contrast enhancement component in the image processing resulted in the largest drop in framework performance, compared to the optimal performance (~12%). In the CNN part, all the three components (residual module, ROI attention, and skip attention) were approximately equally important to the framework performance; removing one of them resulted in a 3–5% decline in performance. The proposed CNN improved ~4% overall performance and ~350% computational efficiency, compared to the U-Net model. The deep convolutional neural network, in conjunction with image processing for feature enhancement, is capable of feature extraction from CT images for pulmonary perfusion synthesis. In the proposed framework, image processing, especially CT contrast enhancement, plays a crucial role in the perfusion synthesis. This CTPM framework provides insights for relevant research studies in the future and enables other researchers to leverage for the development of optimized CNN models for functional lung avoidance radiation therapy.

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