scholarly journals Radiology Informatics : Image Processing In Pacs-Lung Cancer Detection in CT Images

2020 ◽  
pp. 189-194
Author(s):  
Tulika Choudhury ◽  
Mandar Karyakarte
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Three Dimensional ◽  
Cross Sectional ◽  
Radiology Informatics ◽  
Svm Algorithm ◽  
Cross Sectional Imaging ◽  
Time Required ◽  
Pacs Server ◽  
Processing Techniques

<p>In this Paper, I will do Image Processing Techniques in DICOM Images acquired from the PACS Server and by utilizing KNN and SVM Algorithm and I will utilize a prescient strategy to examine the disarranges of any patient by contrasting the prior datasets of same methodology and Predict the turmoil of the patient, which will diminish the time taken to break down any DICOM pictures. Mix of RIS and PACS administrations into a solitary arrangement has turned into a broad reality in day by day radiological work on, permitting significant increasing speed of work process without any difficulty of work contrasted and more seasoned age film-based radiological movement. Specifically, the quick and stupendous late development of computerized radiology (with unique reference to cross-sectional imaging modalities, for example, CT and MRI) has been paralleled by the improvement of incorporated RIS– PACS frameworks with cutting edge picture preparing devices (either two-and additionally three-dimensional) that were a restrictive undertaking of expensive devoted workstations until a couple of years prior. This new situation is probably going to additionally enhance profitability in the radiology division with decrease of the time required for picture translation and revealing, and also to cut expenses for the buy of devoted independent picture handling workstations. In this paper, a general depiction of common incorporated RIS– PACS design with picture preparing capacities will be given, and the primary accessible picture handling devices will be delineated. The most well-known kind of malignancy is Lung Cancer. The demise rate is higher in this kind of growth, which can be lessened, if found in its before stages. The Lung Cancer can be recognized utilizing picture preparing strategies on the CT pictures of the Chest of a patient. In this Paper, I will utilize the CT pictures of the Chest to distinguish Lung Cancer by decreasing the clamor of the picture and changing over it to grayscale and after that utilization water shed calculation to identify lung disease.</p>

Three-Dimensional Structure of Rotavirus-Fab Complex

1990 ◽  
Vol 48 (1) ◽  
pp. 238-239
Author(s):  
B.V.V. Prasad ◽  
E. Marietta ◽  
J.W. Burns ◽  
M.K. Estes ◽  
W. Chiu
Keyword(s):  
Image Processing ◽  
Smooth Surface ◽  
Three Dimensional ◽  
Outer Shell ◽  
Dimensional Structure ◽  
Structural Studies ◽  
Cell Penetration ◽  
Electron Cryomicroscopy ◽  
Image Processing Techniques ◽  
Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

scholarly journals A Numerical Study of Sub-Millisecond Integrated Mix-and-Inject Microfluidic Devices for Sample Delivery at Synchrotron and XFELs

2021 ◽  
Vol 11 (8) ◽  
pp. 3404
Author(s):  
Majid Hejazian ◽  
Eugeniu Balaur ◽  
Brian Abbey
Keyword(s):  
Molecular Dynamics ◽  
Flow Rate ◽  
Numerical Study ◽  
Three Dimensional ◽  
Microfluidic Devices ◽  
Liquid Jets ◽  
Mixing Efficiency ◽  
X Ray Diffraction ◽  
Cross Sectional ◽  
Time Required

Microfluidic devices which integrate both rapid mixing and liquid jetting for sample delivery are an emerging solution for studying molecular dynamics via X-ray diffraction. Here we use finite element modelling to investigate the efficiency and time-resolution achievable using microfluidic mixers within the parameter range required for producing stable liquid jets. Three-dimensional simulations, validated by experimental data, are used to determine the velocity and concentration distribution within these devices. The results show that by adopting a serpentine geometry, it is possible to induce chaotic mixing, which effectively reduces the time required to achieve a homogeneous mixture for sample delivery. Further, we investigate the effect of flow rate and the mixer microchannel size on the mixing efficiency and minimum time required for complete mixing of the two solutions whilst maintaining a stable jet. In general, we find that the smaller the cross-sectional area of the mixer microchannel, the shorter the time needed to achieve homogeneous mixing for a given flow rate. The results of these simulations will form the basis for optimised designs enabling the study of molecular dynamics occurring on millisecond timescales using integrated mix-and-inject microfluidic devices.

scholarly journals 3D-Volume Rendering of the Pelvis with Emphasis on Paraurethral Structures Based on MRI Scans and Comparisons between 3D Slicer and OsiriX®

2021 ◽  
Vol 45 (3) ◽  
Author(s):  
C. M. Durnea ◽  
S. Siddiqi ◽  
D. Nazarian ◽  
G. Munneke ◽  
P. M. Sedgwick ◽  
...  
Keyword(s):  
Image Processing ◽  
Secondary Analysis ◽  
Three Dimensional ◽  
3D Models ◽  
Clinical Role ◽  
Cross Sectional ◽  
3D Slicer ◽  
Mri Scans ◽  
3D Volume ◽  
Good Agreement

AbstractThe feasibility of rendering three dimensional (3D) pelvic models of vaginal, urethral and paraurethral lesions from 2D MRI has been demonstrated previously. To quantitatively compare 3D models using two different image processing applications: 3D Slicer and OsiriX. Secondary analysis and processing of five MRI scan based image sets from female patients aged 29–43 years old with vaginal or paraurethral lesions. Cross sectional image sets were used to create 3D models of the pelvic structures with 3D Slicer and OsiriX image processing applications. The linear dimensions of the models created using the two different methods were compared using Bland-Altman plots. The comparisons demonstrated good agreement between measurements from the two applications. The two data sets obtained from different image processing methods demonstrated good agreement. Both 3D Slicer and OsiriX can be used interchangeably and produce almost similar results. The clinical role of this investigation modality remains to be further evaluated.

Lung Cancer Detection using Image Processing Techniques and its Classification

2021 ◽  
Vol 9 (9) ◽  
pp. 1578-1583
Author(s):  
Aishwarya .R
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Support Vector Machine ◽  
Cell Carcinoma ◽  
Mortality Rates ◽  
The Body ◽  
Support Vector ◽  
Lung Tumour ◽  
Image Processing Techniques ◽  
Processing Techniques

Abstract: Lung cancer has been a major contribution to mortality rates world-wide for many years now. There is a need for early diagnosis of lung cancer which if implemented, will help in reducing mortality rates. Recently, image processing techniques have been widely applied in various medical facilities for accurate detection and diagnosis of abnormality in the body images like in various cancers such as brain tumour, breast tumour and lung tumour. This paper is a development of an algorithm based on medical image processing to segment the lung tumour in CT images due to the lack of such algorithms and approaches used to detect tumours. The work involves the application of different image processing tools in order to arrive at the desired result when combined and successively applied. The segmentation system comprises different steps along the process. First, Image preprocessing is done where some enhancement is done to enhance and reduce noise in images. In the next step, the different parts in the images are separated to be able to segment the tumour. In this phase threshold value was selected automatically. Then morphological operation (Area opening) is implemented on the thresholded image. Finally, the lung tumour is accurately segmented by subtracting the opened image from the thresholded image. Support Vector Machine (SVM) classifier is used to classify the lung tumour into 4 different types: Adenocarcinoma(AC), Large Cell Carcinoma(LCC) Squamous Cell Carcinoma(SCC), and No tumour (NT). Keywords: Lung tumour; image processing techniques; segmentation; thresholding; image enhancement; Support Vector Machine; Machine learning;

scholarly journals THE ETIOLOGIC RELEVANCE OF 3-D PATHOANATOMY OF ADOLESCENT IDIOPATHIC SCOLIOSIS

2017 ◽  
Vol 16 (4) ◽  
pp. 302-307
Author(s):  
Tom Schlösser ◽  
Rob Brink ◽  
René Castelein
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Idiopathic Scoliosis ◽  
Three Dimensional ◽  
Imaging Studies ◽  
Cross Sectional ◽  
Ongoing Research ◽  
Biomechanical Loading ◽  
Cross Sectional Imaging ◽  
Sectional Imaging ◽  
The One

ABSTRACT Despite many years of dedicated research into the etiopathogenesis of adolescent idiopathic scoliosis, there is still no single distinct cause for this puzzling condition. In this overview, we attempt to link knowledge on the complex three-dimensional pathoanatomy of AIS, based on our ongoing research in this field, with etiopathogenic questions. Evidence from multiple recent cross-sectional imaging studies is provided that supports the hypothesis that AIS has an intrinsic biomechanical basis: an imbalance between the biomechanical loading of the upright human spine due to its unique sagittal configuration on the one hand, and the body’s compensating mechanisms on the other. The question that remains in the etiology of AIS, and the focus of our ongoing research, is to determine what causes or induces this imbalance.

scholarly journals Lung Cancer Detection using Convolutional Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 3256-3262 ◽  
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Detection System ◽  
Histogram Equalization ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Cad System ◽  
Processing Techniques

The mortality rate is increasing among the growing population and one of the leading causes is lung cancer. Early diagnosis is required to decrease the number of deaths and increase the survival rate of lung cancer patients. With the advancements in the medical field and its technologies CAD system has played a significant role to detect the early symptoms in the patients which cannot be carried out manually without any error in it. CAD is detection system which has combined the machine learning algorithms with image processing using computer vision. In this research a novel approach to CAD system is presented to detect lung cancer using image processing techniques and classifying the detected nodules by CNN approach. The proposed method has taken CT scan image as input image and different image processing techniques such as histogram equalization, segmentation, morphological operations and feature extraction have been performed on it. A CNN based classifier is trained to classify the nodules as cancerous or non-cancerous. The performance of the system is evaluated in the terms of sensitivity, specificity and accuracy

scholarly journals CAD System for Lung Cancer and its Stages Detection using Image Processing Techniques

2020 ◽  
Vol 9 (4) ◽  
pp. 1612-1615
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
False Positive ◽  
False Negative ◽  
Region Growing ◽  
High Rate ◽  
Support Vector ◽  
Image Region ◽  
Image Processing Techniques ◽  
Processing Techniques

The Lung Cancer is a most common cancer which causes of death to people. Early detection of this cancer will increase the survival rate. Usually, cancer detection is done manually by radiologists that had resulted in high rate of False Positive (FP) and False Negative (FN) test results. Currently Computed Tomography (CT) scan is used to scan the lung, which is much efficient than X-ray. In this proposed system a Computer Aided Detection (CADe) system for detecting lung cancer is used. This proposed system uses various image processing techniques to detect the lung cancer and also to classify the stages of lung cancer. Thus the rates of human errors are reduced in this system. As the result, the rate of obtaining False positive and (FP) False Negative (FN) has reduced. In this system, MATLAB have been used to process the image. Region growing algorithm is used to segment the ROI (Region of Interest). The SVM (Support Vector Machine) classifier is used to detect lung cancer and to identify the stages of lung cancer for the segmented ROI region. This proposed system produced 98.5 % accuracy when compared to other existing system

Analysis of Insole Geometry and Deformity by Using a Three-Dimensional Image Processing Technique: A Preliminary Study

2019 ◽  
Vol 109 (2) ◽  
pp. 98-107
Author(s):  
Kit-lun Yick ◽  
Wai-ting Lo ◽  
Sun-pui Ng ◽  
Joanne Yip ◽  
Hung-hei Kwan ◽  
...  
Keyword(s):  
Image Processing ◽  
Cross Sections ◽  
Plantar Pressure ◽  
Three Dimensional ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Diabetic Patients ◽  
Clinical Practices ◽  
Cross Sectional ◽  
Orthotic Treatment

Background: Accurate representation of the insole geometry is crucial for the development and performance evaluation of foot orthoses designed to redistribute plantar pressure, especially for diabetic patients. Methods: Considering the limitations in the type of equipment and space available in clinical practices, this study adopted a simple portable three-dimensional (3-D) desktop scanner to evaluate the 3-D geometry of an orthotic insole and the corresponding deformities after the insole has been worn. The shape of the insole structure along horizontal cross sections is defined with 3-D scanning and image processing. Accompanied by an in-shoe pressure measurement system, plantar pressure distribution in four foot regions (hallux, metatarsal heads, midfoot, and heel) is analyzed and evaluated for insole deformity. Results: Insole deformities are quantified across the four foot regions. The hallux region tends to show the greatest changes in shape geometry (17%–50%) compared with the other foot regions after 2 months of insole wear. As a result of insole deformities, plantar peak pressures change considerably (–4.3% to +69.5%) during the course of treatment. Conclusions: Changes in shape geometry of the insoles could be objectively quantified with 3-D scanning techniques and image processing. This investigation finds that, in general, the design of orthotic insoles may not be adequate for diabetic individuals with similar foot problems. The drastic changes in the insole shape geometry and cross-sectional areas during orthotic treatment may reduce insole fit and conformity. An inadequate insole design may also affect plantar pressure reduction. The approach proposed herein, therefore, allows for objective quantification of insole shape geometry, which results in effective and optimal orthotic treatment.

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