Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography

A novel glomerular basement membrane segmentation using neutrsophic set and shearlet transform on microscopic images

Purpose

Glomerular basement membrane segmentation is an ultimate step in several image processing applications for kidney diseases and abnormalities in microscopic images. However, extracting the glomerular basement membrane (GBM) regions accurately is considered challenging because of the large variants in the microscopic images. The contribution of this work is to propose a computer-aided detection system to provide accurate GBM segmentation.

Methods

A novel GBM segmentation algorithm is developed based on neutrsophic set and shearlet transform. Firstly, the shearlet features are extracted from the microscopic image samples using shearlet transform. Afterward, the neutrosophic image is defined using shearlet features, and the indeterminacy on the neutrosophic image is reduced using an α-mean operation. Lastly, the k-means clustering algorithm is applied to segment the neutrsophic image and the GBM is identified using its intensity feature.

Results

Three metrics, namely the average distance (AvgDist), the Hausdorff distance (Hdist), and percentage overlap area (POA); were employed to assess the proposed method performance. The results established that the proposed method achieved smaller distance errors and larger POAs. For the tested image, the average of AvgDist, HDist and POA are 1.99204, 4.59535 and 0.67857, respectively. The results established that the cases were segmented accurately using the proposed NS based shearlet transform.

Conclusions

The new method utilizing the shearlet features and neutrosophic set improved the accuracy of GBM segmentation. Further study is underway to improve an automated CAD system using the refined segmentation results.

Development of a minipig model for lung injury induced by a single high-dose radiation exposure and evaluation with thoracic computed tomography

Radiation-induced lung injury (RILI) due to nuclear or radiological exposure remains difficult to treat because of insufficient clinical data. The goal of this study was to establish an appropriate and efficient minipig model and introduce a thoracic computed tomography (CT)-based method to measure the progression of RILI. Göttingen minipigs were allocated to control and irradiation groups. The most obvious changes in the CT images after irradiation were peribronchial opacification, interlobular septal thickening, and lung volume loss. Hounsfield units (HU) in the irradiation group reached a maximum level at 6 weeks and decreased thereafter, but remained higher than those of the control group. Both lung area and cardiac right lateral shift showed significant changes at 22 weeks post irradiation. The white blood cell (WBC) count, a marker of pneumonitis, increased and reached a maximum at 6 weeks in both peripheral blood and bronchial alveolar lavage fluid. Microscopic findings at 22 weeks post irradiation were characterized by widening of the interlobular septum, with dense fibrosis and an increase in the radiation dose-dependent fibrotic score. Our results also showed that WBC counts and microscopic findings were positively correlated with the three CT parameters. In conclusion, the minipig model can provide useful clinical data regarding RILI caused by the adverse effects of high-dose radiotherapy. Peribronchial opacification, interlobular septal thickening, and lung volume loss are three quantifiable CT parameters that can be used as a simple method for monitoring the progression of RILI.

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed.

Measurement of myocardial perfusion and infarction size using computer-aided diagnosis system for myocardial contrast echocardiography

Proper evaluation of myocardial microvascular perfusion and assessment of infarct size is critical for clinicians. We have developed a novel computer-aided diagnosis (CAD) approach for myocardial contrast echocardiography (MCE) to measure myocardial perfusion and infarct size. Rabbits underwent 15 min of coronary occlusion followed by reperfusion (group I, n = 15) or 60 min of coronary occlusion followed by reperfusion (group II, n = 15). Myocardial contrast echocardiography was performed before and 7 d after ischemia/reperfusion, and images were analyzed with the CAD system on the basis of eliminating particle swarm optimization clustering analysis. The myocardium was quickly and accurately detected using contrast-enhanced images, myocardial perfusion was quantitatively calibrated and a color-coded map calibrated by contrast intensity and automatically produced by the CAD system was used to outline the infarction region. Calibrated contrast intensity was significantly lower in infarct regions than in non-infarct regions, allowing differentiation of abnormal and normal myocardial perfusion. Receiver operating characteristic curve analysis documented that -54-pixel contrast intensity was an optimal cutoff point for the identification of infarcted myocardium with a sensitivity of 95.45% and specificity of 87.50%. Infarct sizes obtained using myocardial perfusion defect analysis of original contrast images and the contrast intensity-based color-coded map in computerized images were compared with infarct sizes measured using triphenyltetrazolium chloride staining. Use of the proposed CAD approach provided observers with more information. The infarct sizes obtained with myocardial perfusion defect analysis, the contrast intensity-based color-coded map and triphenyltetrazolium chloride staining were 23.72 ± 8.41%, 21.77 ± 7.8% and 18.21 ± 4.40% (% left ventricle) respectively (p > 0.05), indicating that computerized myocardial contrast echocardiography can accurately measure infarct size. On the basis of the results, we believe the CAD method can quickly and automatically measure myocardial perfusion and infarct size and will, it is hoped, be very helpful in clinical therapeutics.

A new method of detecting pulmonary nodules with PET/CT based on an improved watershed algorithm

BACKGROUND

Integrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives.

METHOD

Our proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.

RESULTS

Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).