scholarly journals Fully Automated Segmentation of Lung Parenchyma Using Break and Repair Strategy

2019 ◽  
Vol 28 (2) ◽  
pp. 275-289 ◽  
Author(s):  
S. Pramod Kumar ◽  
Mrityunjaya V. Latte
Keyword(s):  
Active Contour ◽  
Lung Parenchyma ◽  
Threshold Selection ◽  
Chain Code ◽  
Repair Strategy ◽  
Automated Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Pulmonary Parenchyma ◽  
Optimal Threshold Selection

Abstract The traditional segmentation methods available for pulmonary parenchyma are not accurate because most of the methods exclude nodules or tumors adhering to the lung pleural wall as fat. In this paper, several techniques are exhaustively used in different phases, including two-dimensional (2D) optimal threshold selection and 2D reconstruction for lung parenchyma segmentation. Then, lung parenchyma boundaries are repaired using improved chain code and Bresenham pixel interconnection. The proposed method of segmentation and repairing is fully automated. Here, 21 thoracic computer tomography slices having juxtapleural nodules and 115 lung parenchyma scans are used to verify the robustness and accuracy of the proposed method. Results are compared with the most cited active contour methods. Empirical results show that the proposed fully automated method for segmenting lung parenchyma is more accurate. The proposed method is 100% sensitive to the inclusion of nodules/tumors adhering to the lung pleural wall, the juxtapleural nodule segmentation is >98%, and the lung parenchyma segmentation accuracy is >96%.

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

2021 ◽  
pp. 1-19
Author(s):  
Maria Tamoor ◽  
Irfan Younas
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Heart Diseases ◽  
Medical Image Segmentation ◽  
Gaussian Kernel ◽  
Initial Contour ◽  
Automated Method ◽  
Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

scholarly journals ANGIOPULMONOGRAPHY WITH NITROGLYCERIN TEST IN THE DIAGNOSIS OF ACUTE INFECTIOUS LUNG DESTRUCTION

2021 ◽  
Vol 29 (4) ◽  
pp. 412-419
Author(s):  
V.I. Petukhov ◽  
◽  
V.I. Derkach ◽  
S.N. Ermashkevich ◽  
M.V. Kuntsevich ◽  
...  
Keyword(s):  
Blood Flow ◽  
Differential Diagnosis ◽  
Test Performance ◽  
Blood Circulation ◽  
Lung Parenchyma ◽  
Inflammatory Infiltration ◽  
Affected Area ◽  
Vascular Bed ◽  
Pulmonary Parenchyma ◽  
Flow Through

Objective. To develop a method for additional and differential diagnosis of acute infectious lung destruction (AILD) based on angiopulmonography with the nitroglycerin test. Methods. Angiopulmonography with the nitroglycerin test was used in 10 patients with suppurative diseasesof thelung and pleura for additional and differential diagnosis of AILD The method was used in such situations when chest computed tomography did not allow to determine unambiguously the presence and / or prevalence of necrosis of the lung parenchyma. Results. In 3 patients with the lung abscess, a clear restriction of the decay cavity was registered with the preservation of the main blood flow and weakening of the parenchymal phase of the blood circulation along the periphery of the destructive area. During the nitroglycerin test performance there was no change in the filling of the microvascular bed with contrast along the periphery of the decay cavity, which made it possible to determine the presence of parietal sequesters. According to the results of the study, the lung gangrene was diagnosed in 6 patients. At the same time, two variants of circulatory disorders were noted: the first - with preservation of the blood flow through the main vessels and with the absence of a parenchymal phase in the lesion focus, the second - with the violation of the main blood flow. In the affected area no change in blood flow was observed after the nitroglycerin test performance. Similar results of the study indicated the development of necrosis of the pulmonary parenchyma, which was subsequently confirmed during the operations performed. In the site of inflammatory infiltration of the pulmonary parenchyma with preserved main blood flow, the depletion of the parenchymal phase of blood circulation was determined, but after the nitroglycerin test, a pronounced enrichment of the vascular architecture to the parenchymal phase in the pneumonia affecting part of the lung was noted. Conclusion. It has been established that AILD is characterized by irreversible changes in the vascular bed of the lung parenchyma in the lesion focus. Angiopulmonography with the nitroglycerin test is considered to be an additional highly informative method improving the early and differential diagnosis of AILD in difficult clinical situations. What this paper adds It has been found out that during angiopulmonography the areas of pulmonary necrosis are characterized by the absence of a vascular pattern with or without disturbance of the blood flow through the segmental arteries. At the same time, in contrast to the foci of pneumonia, the nitroglycerin test is not accompanied by an evaluation of the filling of the pulmonary vascular bed in the affected area, i.e. blood supply disorders are irreversible. Thus, based on an assessment of the nature and reversibility of the blood flow disturbances in the affected lung, it is possible to carry out differential diagnosis of the early stages of acute infectious lung destruction (AILD) and pneumonia.

scholarly journals An Ore Image Segmentation Method Based on RDU-Net Model

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4979
Author(s):  
Dong Xiao ◽  
Xiwen Liu ◽  
Ba Tuan Le ◽  
Zhiwen Ji ◽  
Xiaoyu Sun
Keyword(s):  
Image Segmentation ◽  
Production Efficiency ◽  
Fragment Size ◽  
Conveyor Belt ◽  
Segmentation Method ◽  
Residual Structure ◽  
Production Safety ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Main Index

The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator.

scholarly journals Noncircular Iris Segmentation Based on Weighted Adaptive Hough Transform Using Smartphone Database

2018 ◽  
Vol 15 (2) ◽  
pp. 739-743 ◽  
Author(s):  
Noor Amjed ◽  
Fatimah Khalid ◽  
Rahmita Wirza O. K. Rahmat ◽  
Hizmawati Binit Madzin
Keyword(s):  
Hough Transform ◽  
Iris Recognition ◽  
Recognition System ◽  
Iris Segmentation ◽  
Negative Image ◽  
Processing Step ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
High Level ◽  
Imaging Conditions

Iris segmentation methods work based on ideal imaging conditions which produce good output results. However, the segmentation accuracy of an iris recognition system significantly influences its performance, especially with data that captured in unconstrained environment of the Smartphone. This paper proposes a novel segmentation method for unconstrained environment of the Smartphone videos based on choose the best frames from the videos and try to enhance the contrast of this frames by applying the two fuzzy logic membership functions on the negative image which delimit between dark and bright regions in able to make the dark region darker and the bright region brighter. This pre-processing step Facilitates the work of the Weighted Adaptive Hough Transform to automatically find the diameter of the iris region to apply the osiris v4.1. The proposed method results on the video of (Mobile Iris Challenge Evaluation (MICHE))-I, iris databases indicate a high level of accuracy and more efficient computationally using the proposed technique.

scholarly journals Automated mapping of local layer slope and tracing of internal layers in radio echograms

2014 ◽  
Vol 55 (67) ◽  
pp. 71-77 ◽  
Author(s):  
Christian Panton
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Ice Cores ◽  
Internal Layers ◽  
Automated Mapping ◽  
Iterative Optimization ◽  
Automated Method ◽  
Contour Model ◽  
Intensity Response ◽  
Radio Echo Sounding

AbstractAn automated method is presented for tracing layers in radio echograms. The method is designed to work with most radio-echo sounding echograms and has been successfully tested with a 180–210 MHz multichannel coherent depth sounder. To accurately trace layers, first approximate layer positions are calculated by integrating the local layer slope which is inferred by the intensity response to a slanted filter, then the positions are refined using an iterative optimization. The layers are traced using an active contour model or snake, which can be constrained to conserve both echogram features and smooth layers. With this technique it is possible to trace internal layers over distances of several hundred kilometers. The method was tested between two Greenland deep ice cores where the age–depth relation is known.

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