A New CAD System for Detecting Localized Ground Glass Opacity Nodules in Lung CT images Using Cross and Coronary Section Images

2006 ◽  
Author(s):  
H.A. Bastawrous ◽  
N. Nitta ◽  
M. Tsudagawa
Keyword(s):  
Ground Glass Opacity ◽  
Ct Images ◽  
Ground Glass ◽  
Cad System ◽  
Lung Ct

A Review of Ground Glass Opacity Detection Methods in Lung CT Images

2017 ◽  
Vol 13 (1) ◽  
pp. 20-31 ◽  
Author(s):  
Lv Linying ◽  
Liu Xiabi ◽  
Zhou Chunwu ◽  
Zhao Xinming ◽  
Zhao Yanfeng
Keyword(s):  
Ground Glass Opacity ◽  
Ct Images ◽  
Detection Methods ◽  
Ground Glass ◽  
Lung Ct

A novel approach of computer-aided detection of focal ground-glass opacity in 2D lung CT images

2013 ◽  
Author(s):  
Song Li ◽  
Xiabi Liu ◽  
Ali Yang ◽  
Kunpeng Pang ◽  
Chunwu Zhou ◽  
...  
Keyword(s):  
Ground Glass Opacity ◽  
Ct Images ◽  
Ground Glass ◽  
Computer Aided Detection ◽  
Novel Approach ◽  
Computer Aided ◽  
Lung Ct

GROUND-GLASS OPACITY DETECTION BY USING CORRELATION BETWEEN SUCCESSIVE SLICE IMAGES

2007 ◽  
Vol 16 (04) ◽  
pp. 583-592 ◽  
Author(s):  
HYOUNGSEOP KIM ◽  
MASAKI MAEKADO ◽  
JOO KOOI TAN ◽  
SEIJI ISHIKAWA ◽  
MASAAKI TSUKUDA
Keyword(s):  
Soft Tissues ◽  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Processing Technique ◽  
Ct Images ◽  
Image Processing Technique ◽  
Ground Glass ◽  
Lung Region ◽  
Mdct Image ◽  
Thorax Ct

Medical imaging systems such as computed tomography, magnetic resonance imaging provided a high resolution image for powerful diagnostic tool in visual inspection fields by physician. Especially MDCT image can be used to obtain detailed images of the pulmonary anatomy, including pulmonary diseases such as the pulmonary nodules, the pulmonary vein, etc. In the medical image processing technique, segmentation is a difficult task because surrounding soft tissues and organs have similar CT values and sometimes contact with each other. We propose a new technique for automatic segmentation of lung regions and its classification for ground-glass opacity from the extracted lung regions by computer based on a set of the thorax CT images. In this paper, we segment the lung region for extraction of the region of interest employing binarization and labeling process from the inputted each slices images. The region having the largest area is regarded as the tentative lung regions. Furthermore, the ground-glass opacity is classified by correlation distribution on the slice to slice from the extracted lung region with respect to the thorax CT images. Experiment is performed employing twenty six thorax CT image sets and 96% of recognition rates were achieved. Obtained results are shown along with a discussion.

scholarly journals Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinglun Liang ◽  
Guoliang Ye ◽  
Jianwen Guo ◽  
Qifan Huang ◽  
Shaohui Zhang
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
False Positives ◽  
Classification Systems ◽  
Cad System ◽  
Nodule Detection ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

scholarly journals The Volume Ratio of Ground Glass Opacity in Early Lung CT Predicts Mortality in Acute Paraquat Poisoning

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0121691 ◽  
Author(s):  
Xin Kang ◽  
Da-Yong Hu ◽  
Chang-Bin Li ◽  
Xin-Hua Li ◽  
Shu-Ling Fan ◽  
...  
Keyword(s):  
Volume Ratio ◽  
Ground Glass Opacity ◽  
Ground Glass ◽  
Paraquat Poisoning ◽  
Lung Ct

scholarly journals Studi Awal Deteksi COVID-19 Menggunakan Citra CT Berbasis Deep Learning

2020 ◽  
Vol 7 (3) ◽  
pp. 629
Author(s):  
Windra Swastika
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Ground Glass Opacity ◽  
Normal Subjects ◽  
Ct Images ◽  
Ground Glass ◽  
Virus Spread ◽  
Training Process ◽  
Specificity And Sensitivity ◽  
Preliminary Study

<p class="Abstrak">Pada bulan Desember 2019, virus COVID-19 menyebar ke banyak negara, termasuk di Indonesia yang kemudian menjadi pandemi dan menimbulkan masalah serius karena masih belum adanya vaksin untuk mencegah penularan. Uji spesimen saluran nafas atas dan saluran nafas bawah saat ini merupakan salah satu metode yang efektif untuk mengetahui apakah seseorang terinfeksi COVID-19 atau tidak. Salah satu indikasi dari infeksi COVID-19 adalah sesak nafas atau pneumonia serta munculnya <em>ground-glass opacity</em> pada citra CT. Penelitian ini merupakan studi awal untuk melihat apakah citra CT dari organ thorax dapat digunakan sebagai alternatif untuk mendeteksi infeksi virus COVID-19. Deep learning digunakan untuk membuat sebuah model dengan citra CT sebagai masukan. Total 140 data citra CT yang terbagi menjadi 2 yaitu citra dari pasien terinfeksi dan citra dari subjek normal digunakan sebagai masukan pada deep learning. Proses pelatihan dilakukan menggunakan CNN dengan arsitektur VGG16 dan optimizer SGD dan Adam. Hasil yang didapatkan adalah akurasi sebesar 92,86% untuk mengklasifikasikan infeksi COVID-19 dan normal. Nilai spesifisitas dan sensitivitas sebesar 100% dan 85,71% untuk pelatihan dengan menggunakan optimizer SGD.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>In December 2019, the COVID-19 virus spread to many countries, including Indonesia which later became a pandemic and caused serious problems because there was still no vaccine to prevent transmission. Tests of upper and lower respiratory tract specimens are now an effective method of finding whether a person is infected with COVID-19 or not. One indication of COVID-19 infection is shortness of breath or pneumonia and the appearance of ground-glass opacity on CT images. This research is a preliminary study to see whether CT images of the thorax organs can be used as an alternative to detect COVID-19 virus. The deep learning is used to create a model with CT images as input. A total of 140 CT image data which are divided into 2 images from infected patients and images from normal subjects are used as input for deep learning. The training process is carried out using CNN with VGG16 architecture and SGD and Adam optimizers. The results obtained are 92.86% accuracy for classifying COVID-19 infections and normal. Specificity and sensitivity values were 100% and 85.71% for training using the SGD optimizer.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals A novel localization technique for peripheral ground glass opacity using geometric parameters measured on CT images

BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mengjun Bie ◽  
Xuemin Zhao ◽  
Min Zhang ◽  
Guang Fu ◽  
Mingjian Ge
Keyword(s):  
Resection Margin ◽  
Surgical Margin ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Geometric Parameters ◽  
Ground Glass ◽  
Localization Technique ◽  
Preoperative Ct ◽  
Optimal Localization ◽  
Geometric Localization

Abstract Background Currently no optimal localization technique has been established for localization of ground glass opacity (GGO). We aimed to introduce a localization technique using geometric localization for peripheral GGO. Methods We delineated the location of pulmonary GGO using geometric method which was similar with localization of a point in a spatial coordinate system. The localization technique was based on the anatomical landmarkers (ribs or intercostal spaces, capitulum costae and sternocostal joints). The geometric parameters were measured on preoperative CT images and the targeted GGO could be identified intraoperatively according to the parameters. We retrospectively collected the data of the patients with peripheral GGOs which were localized using this method and were wedge resected between June 2019 and July 2020. The efficacy and feasibility of the localization technique were assessed. Results There were 93 patients (male 34, median = 55 years) with 108 peripheral GGOs in the study. All the targeted GGOs were successfully wedge resected in the operative field with negative surgical margin at the first attempt. For each GGO, the localization parameters could be measured in 2–4 min (median = 3 min) on CT images before operation, and surgical resection could be completed in 5–10 min (median = 7 min). A total of 106 (98.15%) GGOs achieved sufficient resection margin. No complications and deaths occurred related to the localization and surgical procedure. Conclusions The localization technique can achieve satisfactory localization success rate and good safety profile. It can provide an easy-to-use alternative to localize peripheral GGO.

scholarly journals Early clinical and CT features of COVID-19 and community-acquired pneumonia from a fever observation ward in Ningbo, China

2021 ◽  
Author(s):  
G Qian ◽  
Y Lin ◽  
AHY Ma ◽  
X Zhang ◽  
G Li ◽  
...  
Keyword(s):  
Early Stage ◽  
Laboratory Data ◽  
Clinical Manifestations ◽  
Lower Lobe ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Ground Glass ◽  
Significant Difference ◽  
Eosinophil Counts

Introduction: We aimed to compare the early clinical manifestations, laboratory results and chest computed tomography (CT) images of coronavirus disease 2019 (COVID-19) patients with those of other community-acquired pneumonia (CAP) patients to differentiate COVID-19 before reverse transcription-polymerase chain reaction results are obtained. Methods: The clinical and laboratory data and chest CT images of 51 patients were assessed in a fever observation ward for evidence of COVID-19 between January and February 2020. Results: 24 patients had laboratory-confirmed COVID-19, whereas 27 individuals had negative results. No statistical difference in clinical features was found between COVID-19 and CAP patients except for diarrhoea. There was a significant difference in lymphocyte and eosinophil counts between COVID-19 and CAP patients. 22 (91.67%) COVID-19 patients had bilateral involvement and multiple lesions according to their lung CT images; the left lower lobe (87.50%) and right lower lobe (95.83%) were most often affected, and all lesions were located in peripheral zones of the lung. The most common CT feature of COVID-19 was ground-glass opacity, found in 95.83% of patients, compared to 66.67% of CAP patients. Conclusion: Diarrhoea, lymphocyte counts, eosinophil counts and CT findings (e.g. ground-glass opacity) could help to distinguish COVID-19 from CAP at an early stage of infection, based on findings from our fever observation ward.

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