A Simplified Measurement Configuration for Evaluation of Relative Permittivity Using a Microstrip Ring Resonator with a Variational Method-Based Algorithm.

In this paper, we present a simple yet efficient method for determination of the relative permittivity of thin dielectric materials. An analysis that led to definition of the proper size and placement of a sample under test (SUT) on the surface of a microstrip ring resonator (MRR) was presented based on the full-wave simulations and measurements on benchmark materials. For completeness, the paper includes short descriptions of the design of an MRR and the variational method-based algorithm that processes the measured values. The efficiency of the proposed method is demonstrated on 12 SUT materials of different thicknesses and permittivity values, and the accuracy between 0% and 10% of the relative error was achieved for all SUTs thinner than 2 mm.

Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion.

BACKGROUND AND OBJECTIVE: Breast cancer is a fatal threat to the health of women. Ultrasonography is a common method for the detection of breast cancer. Computer-aided diagnosis of breast ultrasound images can help doctors in diagnosing benign and malignant lesions. In this paper, by combining image decomposition and fusion techniques with adaptive spatial feature fusion technology, a reliable classification method for breast ultrasound images of tumors is proposed. METHODS: First, fuzzy enhancement and bilateral filtering algorithms are used to process the original breast ultrasound image. Then, various decomposition images representing the clinical characteristics of breast tumors are obtained using the original and mask images. By considering the diversity of the benign and malignant characteristic information represented by each decomposition image, the decomposition images are fused through the RGB channel, and three types of fusion images are generated. Then, from a series of candidate deep learning models, transfer learning is used to select the best model as the base model to extract deep learning features. Finally, while training the classification network, adaptive spatial feature fusion technology is used to train the weight network to complete deep learning feature fusion and classification. RESULTS: In this study, 1328 breast ultrasound images were collected for training and testing. The experimental results show that the values of accuracy, precision, specificity, sensitivity/recall, F1 score, and area under the curve of the proposed method were 0.9548, 0.9811, 0.9833, 0.9392, 0.9571, and 0.9883, respectively. CONCLUSION: Our research can automate breast cancer detection and has strong clinical utility. When compared to previous methods, our proposed method is expected to be more effective while assisting doctors in diagnosing breast ultrasound images.

Automatic segmentation for ultrasound image of carotid intimal-media based on improved superpixel generation algorithm and fractal theory.

OBJECTIVE: Carotid atherosclerosis (CAS) is the main reason leading to cardiovascular conditions such as coronary heart disease and cerebrovascular diseases. In the carotid ultrasound images, the carotid intima-media structure can be observed in an annular narrow strip, which its inner contour corresponds to the carotid intima, and the outer contour corresponds to the carotid extima. With the development of carotid atherosclerosis, the carotid intima-media will gradually thicken. Therefore, doctors can observe the carotid intima-media so as to obtain the pathological changes of the internal structure of the patient's carotid arteries. However, due to the presence of artifacts and noises the quality of the ultrasound images are degraded, making it difficult to obtain accurate carotid intima-media structures. This article presents a novel self-adaptive method to enable obtaining the carotid intima-media through carotid intima/extima segmentation. METHOD: After preprocessing the ultrasound images by homomorphic filtering and median filtering, we propose an improved superpixel generation algorithm that employs the fusion of gray-level and luminosity-based information to decompose the image into numerous superpixels and later presents the carotid intima. Meanwhile, based on the features of the carotid artery, the initial position of the carotid extima is located by the normalized cut algorithm and later the fractal theory is employed to segment the carotid extima. RESULTS: The proposed method for segmenting carotid intima obtained mean values of the DICE true positive ratio (TPR), false positive ratio (FPR), precision scores of 97.797%, 99.126%, 0.540%, 97.202%, respectively. Further from the segmentation method of the carotid extima the performance measures such as mean DICE, TPR, accuracy, F-score obtained are 95.00%, 92.265%, 97.689%, 94.997%, respectively. CONCLUSION: Comparing with traditional methods, the proposed method performed better. The experimental results indicated that the proposed method obtained the carotid intima-media both automatically and accurately thus effectively assist doctors in the diagnosis of CAS.

Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning.

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA's. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.

Cardiac VFM visualization and analysis based on YOLO deep learning model and modified 2D continuity equation.

In order to realize the visual analysis of cardiac fluid motion, according to the characteristics of cardiac flow field ultrasound image, a method for the cardiac Vector Flow Mapping (VFM) analysis and evaluation based on the You-Only-Look-Once (YOLO) deep learning model and the improved two-dimensional continuity equation is proposed in this paper. Firstly, based on the ultrasound Doppler data, the radial velocity values of the blood particles are obtained; due to the real-time VFM's high requirement on the computing speed, the YOLO deep learning model is combined with an improved block matching algorithm for the localization and tracking of myocardial wall, and then the azimuth velocity of myocardial wall speckles can be obtained; in addition, it is proposed in this paper to use a nonlinear weight function to fuse the radial velocity of the blood particles and azimuth velocity of myocardial wall speckles nonlinearly, and further the vortex streamline diagram in the cardiac flow field can be obtained. The results of the experiments on the evaluation of the Ultrasonic apical long-axis view show that the proposed method not only improves the accuracy of VFM, but also provides a new evaluation basis for cardiac function impairment.