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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.

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.

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