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Purpose: Calcification nodules in thyroid can be found in thyroid disease. Current clinical computed tomography systems can be used to detect calcification nodules. Our aim is to identify the nature of thyroid calcification nodule based on plain CT images. Method: Sixty-three patients (36 benign and 27 malignant nodules) found thyroid calcification nodules were retrospectively analyzed, together with computed tomography images and pathology finding. The regions of interest (ROI) of 6464 pixels containing calcification nodules were manually delineated by radiologists in CT plain images. We extracted thirty-one texture features from each ROI. And nineteen texture features were picked up after feature optimization by logistic regression analysis. All the texture features were normalized to [0, 1]. Four classification algorithms, including ensemble learning, support vector machine, K-nearest neighbor, decision tree, were used as classification algorithms to identity the benign and malignant nodule. Accuracy, PPV, NPV, SEN, and AUC were calculated to evaluate the performance of different classifiers. Results: Nineteen texture features were selected after feature optimization by logistic regression analysis (P

BackgroundWe aimed to describe the chest CT findings in sixty-seven patients infected by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).MethodsWe retrospectively reviewed 67 patients hospitalized in Ruian People’s Hospital. All the patients received the positive diagnosis of SARS-CoV-2 infection. The CT and clinical data were collected between January 23rd, 2020 and February 10th, 2020. The CT images were analyzed by the senior radiologists.ResultsThere are 54 patients with positive CT findings and 13 patients with negative CT findings. The typical CT findings in hospitalized patients with SARS-CoV-2 infection were ground glass opacities (42/54), lesions located in the peripheral area (50/54), multiple lesions (46/54), and lesions located in the lower lobes (42/54). There were less typical CT findings, including air bronchogram (18/54), pleural thickening or pleural effusion (14/54), consolidation (12/54), lesions in the upper lobes (12/54), interlobular septal thickening (11/54), reversed halo sign (9/54), single lesion (8/54), air cavities (4/54), bronchial wall thickening (3/54), and intrathoracic lymph node enlargement (2/54).ConclusionsCT features can play an important role in the early diagnosis and follow-up of COVID-19 patients.

Silicon piezoresistive pressure sensors can only operate below 125 °C due to the leakage current of the PN junction. However, silicon-on-insulator (SOI) high-temperature pressure sensors use SiO2 for total dielectric isolation to solve this problem. At present, SOI high-temperature pressure sensors mostly use lead bonding package structure, with gold wire to lead the electrical signal and silicone oil as the protection medium, but the working temperature of silicone oil is limited to about 150 °C. In this paper, the leadless package structure is designed using pressure conduction on the backside of the chip and replacing the gold wire with sintered silver paste. The materials and dimensions of the leadless package structure are determined and then obtained a complete package structure through manufacturing. The reliability of the leadless package structure after silver paste sintering was verified by finite element analysis, and the results showed that the thermal stress caused by high and low-temperature cycles in the leadless package is minimal and does not affect the sensitivity of the pressure-sensitive chip. The size of the leadless package structure was optimized by Taguchi orthogonal method, and the maximum thermal stress was effectively reduced. Also, the key factors affecting the thermal stress of the leadless package in the package structure were identified by the variance number analysis method. The optimized leadless package structure size was remanufactured, and the sintered package structure was tested. The data show that the sensitivity of the pressure sensor is 30.82 mV/MPa with a nonlinearity of less than 0.4% full-scale (FS).

Biomedical image fusion is the process of combining the information from different imaging modalities to get a synthetic image. Fusion of phase contrast and green fluorescent protein (GFP) images is significant to predict the role of unknown proteins, analyze the function of proteins, locate the subcellular structure, and so forth. Generally, the fusion performance largely depends on the registration of GFP and phase contrast images. However, accurate registration of multi-modal images is a very challenging task. Hence, we propose a novel fusion method based on convolutional sparse representation (CSR) to fuse the mis-registered GFP and phase contrast images. At first, the GFP and phase contrast images are decomposed by CSR to get the coefficients of base layers and detail layers. Secondly, the coefficients of detail layers are fused by the sum modified Laplacian (SML) rule while the coefficients of base layers are fused by the proposed adaptive region energy (ARE) rule. ARE rule is calculated by discussion mechanism based brain storm optimization (DMBSO) algorithm. Finally, the fused image is achieved by carrying out the inverse CSR. The proposed fusion method is tested on 100 pairs of mis-registered GFP and phase contrast images. The experimental results reveal that our proposed fusion method exhibits better fusion results and superior robustness than several existing fusion methods.

Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature.

Background Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation. Results and conclusions The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

Background: Thyroid nodules are commonly found at palpation amounting to 4–7% of the asymptomatic population and 50% of the cases are found at autopsy. Only a small proportion of thyroid nodules are malignant. The major challenge is the differential diagnosis of benign or malignant thyroid nodules, so we aim to develop the computer-assisted diagnostic method based on computed tomography (CT) images for thyroid lesions. Methods: In this study, we retrospectively collected 52 benign and 46 malignant thyroid nodules from 90 patients in CT examinations, together with the pathologist findings and radiology diagnosis. The first-order statistic and gray-level co-occurrence matrix features were extracted from thyroid computed tomography images. These texture features were used to assess the malignancy risk of the thyroid nodules. Several classification algorithms, including support vector machine, linear discriminant analysis, random forest, and bootstrap aggregating, were applied in the prediction. Leave-one-out cross-validation was used to evaluate the performance of thyroid cancer recognition. Results: In thyroid cancer identification based on a computed tomography image, we found the system using 17 texture features and support vector machine performed well. The accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, were 0.8673, 0.9105, 0.9130, 0.8269, 0.8235 and 0.9146, respectively. Conclusions: The proposed computer-aided diagnosis system provides a good assessment of the malignancy-risk of the thyroid nodules, which may help radiologists to improve the accuracy and efficiency of thyroid diagnosis.

Background Computed tomography (CT) is one of the popular tools for early detection of thyroid nodule. The pixel intensity of thyroid in CT image is very important information to distinguish nodule from normal thyroid tissue. The pixel intensity in normal thyroid tissues is homogeneous and smooth. In the benign or malignant nodules, the pixel intensity is heterogeneous. Several studies have shown that the first order features in ultrasound image can be used as imaging biomarkers in nodule recognition. Methods In this paper, we investigate the feasibility of utilizing the first order texture features to identify nodule from normal thyroid tissue in CT image. A total of 284 thyroid CT images from 113 patients were collected in this study. We used 150 healthy controlled thyroid CT images from 55 patients and 134 nodule images (50 malignant and 84 benign nodules) from 58 patients who have undergone thyroid surgery. The final diagnosis was confirmed by histopathological examinations. In the presented method, first, regions of interest (ROIs) from axial non-enhancement CT images were delineated manually by a radiologist. Second, average, median, and wiener filter were applied to reduce photon noise before feature extraction. The first-order texture features, including entropy, uniformity, average intensity, standard deviation, kurtosis and skewness were calculated from each ROI. Third, support vector machine analysis was applied for classification. Several statistical values were calculated to evaluate the performance of the presented method, which includes accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area of under receiver operating characteristic curve (AUC). Results The entropy, uniformity, mean intensity, standard deviation, skewness (P

Purpose:To investigate the feasibility of utilizing texture features to classify nodule from normal thyroid tissue in Computed Tomography (CT) images. Materials and Methods: Group A (negative) includes 152 normal thyroid CT images from 55 patients healthy controls enrolled in the study. Group B (positive) includes 134 thyroid images with nodules (50 malignant, 84 benign) of 58 patients undergone thyroid surgery and final diagnoses were confirmed by histopathology. Regions of interest (ROIs) from axial noncontrast CT images were delineated manually and 31 texture features including the gray level co-occurrence matrix (GLCM), the gray level gradient co-occurrence matrix (GLGCM), average intensity, contrast and coherence were extracted. Support Vector Machine (SVM) was used in data classification. Leave one out cross validation (LOOCV) strategy was utilized to take full advantage of the samples. To evaluate the performance of the proposed method, accuracyrate, sensitivity, specificity and area of under receiver operating characteristic (ROC) curve (AUC) etc. were calculated. Results: the accuracy-rate, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) are 0.8895±0.0186, 0.8265±0.0448, 0.9467±0.0141, 0.9340±0.0146 and 0.8586±0.0300 respectively, and the AUC is 0.9520±0.0089. Conclusion: Texture features can help radiologists to classify the nodule from normal thyroid tissue.

Objectives Multifocal skeletal tuberculosis (MSTB) is an uncommon presentation of skeletal tuberculosis. In order to provide more clinically meaningful information on the diagnosis and management of MSTB, we present a case of MSTB with multiple tuberculous lesions in multiple locations, along with a review of 13 MSTB cases from different studies. Patients and methods A 29-year-old male patient with a one-year history of back pain was initially diagnosed with ankylosing spondylitis and arthritis deformans, and received treatment with oral glucocorticosteroid and leflunomide for 24 weeks. The back pain worsened with weight loss and fever one month prior to admission to our hospital. The diagnosis, MSTB, with 26 tuberculous lesions in 19 locations, was made by clinical findings, bone scan (computed topography and Tc-99m HDP scintigraphy), and bone marrow smear. Result Multiple antituberculous drugs, with supportive and immune-enhancing therapies cured the patient. Conclusions This case indicates that MSTB may develop in patients on long-term immunosuppressive drugs. In addition, our experience, along with previously reported data, suggest that strong clinical suspicion is required for an early diagnosis of MSTB, and chemotherapy, combined with supportive and immune-based therapies is effective for the treatment of MSTB.