scholarly journals Fully Automatic Detection and Segmentation Approach for Juxta-Pleural Nodules From CT Images

2021 ◽  
Vol 16 (2) ◽  
pp. 87-104
Author(s):  
Vijayalaxmi Mekali ◽  
Girijamma H. A.
Keyword(s):  
Lung Parenchyma ◽  
Ct Images ◽  
Experimental Result ◽  
Computational Time ◽  
Detection Rates ◽  
Iterative Thresholding ◽  
Automated Method ◽  
Segmentation Approach ◽  
Thresholding Algorithm ◽  
Lung Lobes

Early detection of all types of lung nodules with different characters in medical modality images using computer-aided detection is the best acceptable remedy to save the lives of lung cancer sufferers. But accuracy of different types of nodule detection rates is based on chosen segmented procedures for parenchyma and nodules. Separation of pleural from juxta-pleural nodules (JPNs) is difficult as intensity of pleural and attached nodule is similar. This research paper proposes a fully automated method to detect and segment JPNs. In the proposed method, lung parenchyma is segmented using iterative thresholding algorithm. To improve the nodules detection rate separation of connected lung lobes, an algorithm is proposed to separate connected left and right lung lobes. The new method segments JPNs based on lung boundary pixels extraction, concave points extraction, and separation of attached pleural from nodule. Validation of the proposed method was performed on LIDC-CT images. The experimental result confirms that the developed method segments the JPNs with less computational time and high accuracy.

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%.

A vision transformer for emphysema classification using CT images

2021 ◽  
Author(s):  
Yanan Wu ◽  
Shouliang Qi ◽  
Yu Sun ◽  
Shuyue Xia ◽  
Yudong Yao ◽  
...  
Keyword(s):  
Lung Parenchyma ◽  
Ct Images ◽  
Normal Lung ◽  
Massive Data ◽  
Large Patch ◽  
The Public ◽  
Evaluation Approach ◽  
Average Accuracy ◽  
Public Dataset ◽  
Aided Diagnosis

Abstract Objective: Emphysema is characterized by the destruction and permanent enlargement of the alveoli in the lung. According to visual CT appearance, emphysema can be divided into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE), and paraseptal emphysema (PSE). Automating emphysema classification can help precisely determine the patterns of lung destruction and provide a quantitative evaluation. Approach: We propose a vision transformer (ViT) model to classify the emphysema subtypes via CT images. First, large patches (61×61) are cropped from CT images which contain the area of normal lung parenchyma (NLP), CLE, PLE, and PSE. After resizing, the large patch is divided into small patches and these small patches are converted to a sequence of patch embeddings by flattening and linear embedding. A class embedding is concatenated to the patch embedding, and the positional embedding is added to the resulting embeddings described above. Then, the obtained embedding is fed into the transformer encoder blocks to generate the final representation. Finally, the learnable class embedding is fed to a softmax layer to classify the emphysema. Main results: To overcome the lack of massive data, the transformer encoder blocks (pre-trained on ImageNet) are transferred and fine-tuned in our ViT model. The average accuracy of the pre-trained ViT model achieves 95.95% in our lab’s own dataset which is higher than that of AlexNet, Inception-V3, MobileNet-V2, ResNet34, and ResNet50. Meanwhile, the pre-trained ViT model outperforms the ViT model without the pre-training. The accuracy of our pre-trained ViT model is higher than or comparable to that by available methods for the public dataset. Significance: The results demonstrated that the proposed ViT model can accurately classify the subtypes of emphysema using CT images. The ViT model can help make an effective computer-aided diagnosis of emphysema, and the ViT method can be extended to other medical applications.

Sequence of interstitial liquid accumulation in liquid-inflated sheep lung lobes

1989 ◽  
Vol 66 (6) ◽  
pp. 2659-2666 ◽  
Author(s):  
R. L. Conhaim ◽  
S. J. Lai-Fook ◽  
A. Eaton
Keyword(s):  
Species Differences ◽  
Lung Parenchyma ◽  
Cuff Volume ◽  
Analogue Model ◽  
Maximum Value ◽  
Cuff Size ◽  
Mechanical Linkage ◽  
Perivascular Cuff ◽  
Lung Lobes ◽  
Space Volume

In the initial stages of pulmonary edema, liquid accumulates in the lung interstitium and appears as cuffs around pulmonary vessels. To determine the pattern, rate, and magnitude of cuff formation, we inflated sheep lungs to capacity with liquid (inflation pressure 19 cmH2O) for 3–300 min. After freezing the lobes in liquid N2, we measured perivascular cuff size and total perivascular volume in frozen blocks of each lobe and compared the results with previous measurements in dog lungs. Total cuff volume in sheep lungs reached a maximum value of 5% of air space volume, compared with 9% in dog lungs. In sheep lungs 94% of vessels greater than or equal to 0.5 mm diam and 16% of smaller vessels were surrounded by cuffs. In dog lungs these values were 99 and 47%, respectively. The ratio of cuff area to vessel area reached a maximum of 2.3 in sheep lungs and 3.4 in dog lungs. In an electrical analogue model designed to simulate cuff growth, estimated interstitial resistance to liquid flow was 6–15 times higher than similar estimates in dog lungs. These species differences might be the result of differences in the composition of the interstitial gel or to differences in the mechanical linkage between the lung parenchyma and vessel wall.

scholarly journals Energy Preserved Sampling for Compressed Sensing MRI

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Yudong Zhang ◽  
Bradley S. Peterson ◽  
Genlin Ji ◽  
Zhengchao Dong
Keyword(s):  
Compressed Sensing ◽  
Cost Function ◽  
Computation Time ◽  
Reconstruction Algorithm ◽  
Simple Random Sampling ◽  
Variable Density ◽  
Reconstruction Algorithms ◽  
Iterative Thresholding ◽  
Thresholding Algorithm

The sampling patterns, cost functions, and reconstruction algorithms play important roles in optimizing compressed sensing magnetic resonance imaging (CS-MRI). Simple random sampling patterns did not take into account the energy distribution ink-space and resulted in suboptimal reconstruction of MR images. Therefore, a variety of variable density (VD) based samplings patterns had been developed. To further improve it, we propose a novel energy preserving sampling (ePRESS) method. Besides, we improve the cost function by introducing phase correction and region of support matrix, and we propose iterative thresholding algorithm (ITA) to solve the improved cost function. We evaluate the proposed ePRESS sampling method, improved cost function, and ITA reconstruction algorithm by 2D digital phantom and 2Din vivoMR brains of healthy volunteers. These assessments demonstrate that the proposed ePRESS method performs better than VD, POWER, and BKO; the improved cost function can achieve better reconstruction quality than conventional cost function; and the ITA is faster than SISTA and is competitive with FISTA in terms of computation time.

scholarly journals Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 263
Author(s):  
Xin Chen ◽  
Hong Zhao ◽  
Ping Zhou
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Region Growing ◽  
Disease Diagnosis ◽  
Ct Images ◽  
Normal Vector ◽  
Lung Lobe ◽  
Surface Classification ◽  
Lobe Segmentation ◽  
Lung Lobes

In anatomy, the lung can be divided by lung fissures into several pulmonary lobe units with specific functions. Identifying the lung lobes and the distribution of various diseases among different lung lobes from CT images is important for disease diagnosis and tracking after recovery. In order to solve the problems of low tubular structure segmentation accuracy and long algorithm time in segmenting lung lobes based on lung anatomical structure information, we propose a segmentation algorithm based on lung fissure surface classification using a point cloud region growing approach. We cluster the pulmonary fissures, transformed into point cloud data, according to the differences in the pulmonary fissure surface normal vector and curvature estimated by principal component analysis. Then, a multistage spline surface fitting method is used to fill and expand the lung fissure surface to realize the lung lobe segmentation. The proposed approach was qualitatively and quantitatively evaluated on a public dataset from Lobe and Lung Analysis 2011 (LOLA11), and obtained an overall score of 0.84. Although our approach achieved a slightly lower overall score compared to the deep learning based methods (LobeNet_V2 and V-net), the inter-lobe boundaries from our approach were more accurate for the CT images with visible lung fissures.

scholarly journals Dystrophic mineralisation in chronic exogenous lipid pneumonia in cats

2019 ◽  
Vol 5 (1) ◽  
pp. 205511691985025
Author(s):  
Claudia Mallol ◽  
Yvonne Espada ◽  
Albert Lloret ◽  
Raúl Altuzarra ◽  
Carlo Anselmi ◽  
...  
Keyword(s):  
Chronic Constipation ◽  
Case Series ◽  
Clinical History ◽  
Lung Parenchyma ◽  
Mineral Oil ◽  
Radiographic Findings ◽  
Dependent Part ◽  
Exogenous Lipid ◽  
Lipid Pneumonia ◽  
Lung Lobes

Case series summary Exogenous lipid pneumonia with mineralisation of the lung parenchyma was diagnosed in three cats with radiographs, CT and/or bronchoalveolar lavage cytological findings. All three cats had a common clinical history of chronic constipation and long-term forced oral administration of mineral oil. All three cases showed radiographic findings compatible with aspiration pneumonia, with an alveolar pattern in the ventral part of the middle and/or cranial lung lobes. Minor improvement of the radiographic lung pattern in the follow-up studies was seen in two cats, and a miliary ‘sponge-like’ mineralised pattern appeared in the previously affected lung lobes months to years after the diagnosis. In one cat, patchy fat-attenuating areas in the consolidated lung lobes were present on thoracic CT. Cases 1 and 2 showed respiratory signs at the initial presentation, while in case 3 the radiographic findings were incidental and the cat had never exhibited respiratory signs. Relevance and novel information This is the first report to describe dystrophic mineralisation of the lung in exogenous lipid pneumonia and also the first to describe the CT features in cats. Exogenous lipid pneumonia should be included in the differential diagnosis in cases of miliary ‘sponge-like’ mineral opacities in the dependent part of the lung lobes on thoracic radiographs or CT in cats, especially in cases of chronic constipation, previously exposed to mineral oil.

SEGMENTATION OF MASS IN MAMMOGRAMS USING A NOVEL INTELLIGENT ALGORITHM

2006 ◽  
Vol 20 (02) ◽  
pp. 255-270 ◽  
Author(s):  
WEIDONG XU ◽  
SHUNREN XIA ◽  
HUILONG DUAN ◽  
MIN XIAO
Keyword(s):  
Region Growing ◽  
Experimental Result ◽  
Fatty Tissue ◽  
Intelligent Algorithm ◽  
Matching Model ◽  
Canny Edge Detection ◽  
Iterative Thresholding ◽  
Canny Edge ◽  
Mass Segmentation ◽  
Suspicious Area

In order to improve the performance of mass segmentation on mammograms, an intelligent algorithm is proposed in this paper. It establishes two mass models to characterize the various masses, and the ones in the denser tissue are represented with Model I, while the ones in the fatty tissue are represented with Model II. Then, it uses iterative thresholding to extract the suspicious area, as well as the rough regions of those masses matching Model II, and applies a DWT-based technique to locate those masses matching Model I, which are hidden in the high gray-level intensity and contrast area. A region growing process restricted by Canny edge detection is subsequently used to segment the rough regions of those masses matching Model I, and finally snakes are carried out to find all the mass regions roughly extracted above. Thirty patient cases with 60 mammograms and 107 masses were used for evaluation, and the experimental result has demonstrated the algorithm's better performance over the conventional methods.

Sign in / Sign up

Export Citation Format

Share Document