The Effect of Heavy Fe-Doping on 3D Growth Mode and Fe Diffusion in GaN for High Power HEMT Application.

The high electron mobility transistor (HEMT) structures on Si (111) substrates were fabricated with heavily Fe-doped GaN buffer layers by metalorganic chemical vapor deposition (MOCVD). The heavy Fe concentrations employed for the purpose of highly insulating buffer resulted in Fe segregation and 3D island growth, which played the role of a nano-mask. The in situ reflectance measurements revealed a transition from 2D to 3D growth mode during the growth of a heavily Fe-doped GaN:Fe layer. The 3D growth mode of Fe nano-mask can effectively annihilate edge-type threading dislocations and improve transfer properties in the channel layer, and consequently decrease the vertical leakage current by one order of magnitude for the applied voltage of 1000 V. Moreover, the employment of GaN:C film on GaN:Fe buffer can further reduce the buffer leakage-current and effectively suppress Fe diffusion.

Computer-aided tumor diagnosis using shear wave breast elastography.

The shear wave elastography (SWE) uses the acoustic radiation force to measure the stiffness of tissues and is less operator dependent in data acquisition compared to strain elastography. However, the reproducibility of the result is still interpreter dependent. The purpose of this study is to develop a computer-aided diagnosis (CAD) method to differentiate benign from malignant breast tumors using SWE images. After applying the level set method to automatically segment the tumor contour and hue-saturation-value color transformation, SWE features including average tissue elasticity, sectional stiffness ratio, and normalized minimum distance for grouped stiffer pixels are calculated. Finally, the performance of CAD based on SWE features are compared with those based on B-mode ultrasound (morphologic and textural) features, and a combination of both feature sets to differentiate benign from malignant tumors. In this study, we use 109 biopsy-proved breast tumors composed of 57 benign and 52 malignant cases. The experimental results show that the sensitivity, specificity, accuracy and the area under the receiver operating characteristic ROC curve (Az value) of CAD are 86.5%, 93.0%, 89.9%, and 0.905 for SWE features whereas they are 86.5%, 80.7%, 83.5% and 0.893 for B-mode features and 90.4%, 94.7%, 92.3% and 0.961 for the combined features. The Az value of combined feature set is significantly higher compared to the B-mode and SWE feature sets (p=0.0296 and p=0.0204, respectively). Our results suggest that the CAD based on SWE features has the potential to improve the performance of classifying breast tumors with US.

Automatic slice selection and diagnosis of breast strain elastography.

PURPOSE: Generally speaking, breast imaging experts and physicians select a representative slice from the strain elastographic image sequences to diagnose the tumor. Given the strain image qualities, it is difficult to make a successful diagnosis using human eyes only. The main purpose of this study is to develop an automatic and reliable method to select the representative slice from the elastography cine loops and/or video and then diagnose the tumor by means of the elastographic features generated from the selected slice. METHODS: In this study, the authors collected 80 biopsy-proven breast tumors, comprising of 45 benign and 35 malignant lesions, to estimate the performance of the automatic slice selection method. Images chosen using several slice selection criteria (e.g., whole-image analysis or tumor region analysis) were compared to the physician-selected images to determine the best selection criterion. The level set tumor segmentation method was applied to the corresponding B-mode part of the representative elastographic slice to overlap tumor boundaries on strain images and to calculate elastographic features for diagnosis. RESULTS: The experiment showed that the diagnostic performance, in terms of accuracy, sensitivity, and specificity, evaluated by the leave-one-out method, based on the elastographic features for the representative slice selected by the proposed slice selection method, was 71.3%, 91.4%, and 55.6%, respectively, while the performance values for the physician-selected slice were 65.0%, 77.1%, and 55.6%, respectively. CONCLUSIONS: Both the sensitivity and accuracy of the proposed slice selection method were better than those of the physician-selected slice, and the specificity of these two different schemes is similar. According to the statistical analysis of experimental results, the performance of the proposed slice selection method was similar to that of the physician's selection. The authors concluded that the proposed slice selection method could assist the physician in selecting the appropriate representative slice and in decreasing the time of selection.

Classification of breast tumors using elastographic and B-mode features: comparison of automatic selection of representative slice and physician-selected slice of images.

Inter-observer variability and image quality are two key factors that can affect the diagnostic performance of elastography and B-mode ultrasound for breast tumor characterization. The purpose of this study is to use an image quantification method that automatically chooses a representative slice and then segments the tumor contour to evaluate the diagnostic features for tumor characterization. First, the representative slice is selected based on either the stiffness inside the tumor (the signal-to-noise ratio on the elastogram [SNRe]) or the contrast between the tumor and the surrounding normal tissue (the contrast-to-noise ratio on the elastogram [CNRe]). Next, the level set method is used to segment the tumor contour. Finally, the B-mode and elastographic features related to the segmented tumor are extracted for tumor characterization. The performance of the representative slice selected using the proposed methods is compared to that of the physician-selected slice in 151 biopsy-proven lesions (89 benign and 62 malignant). The diagnostic accuracies using elastographic features are 82.1% (124/151) for the slice with the maximum CNRe value, 82.1% (124/151) for the slice with the maximum SNRe value and 82.8% (125/151) for the physician-selected slice, whereas the diagnostic accuracies using B-mode features are 80.8% (122/151) for the slice with the maximum CNRe value, 87.4% (132/151) for the slice with the maximum SNRe value and 84.1% (127/151) for the physician-selected slice. When using both the B-mode and elastographic features to characterize the tumor, the accuracy of diagnosis is 86.1% (130/151) for the slice with the maximum CNRe value, 90.1% (136/151) for the slice with the maximum SNRe value and 89.4% (135/151) for the physician-selected slice. Our results show that the representative slice selected by SNRe and CNRe could be used to reduce the observer variability and to increase the diagnostic performance by the B-mode and elastographic features.

Breast elastography diagnosis based on dynamic sequence features.

PURPOSE: Each dynamic elastography contains multiple images; therefore, physicians need to determine the most representative image from the scanned sequence to make diagnosis. To eliminate interobserver variations on diagnoses and help doctors providing correct treatments, the authors developed an objective computer-aided diagnostic scheme without requiring manually selecting representative images for diagnosis. METHODS: About 112 histological-proven lesions including 66 benign and 46 malignant tumors were involved as the material database. Suspicious lesions were automatically segmented on the first B-mode images on each captured dynamic elastography. Tissue strains inside lesions on elastograms were classified by utilizing the fuzzy c-means algorithm. In order to reduce the influence of image quality, important tumor characteristics were computed from every strain images in elastography and regressed to a probability of being malignant. Since tumor boundaries changed slightly between adjacent slices, a tumor boundary tracking scheme based on template matching was applied on slices excepting the first one. RESULTS: The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are 85.71% (96∕112), 86.96% (40∕46), 84.85% (56∕66), 80.00% (40∕50), and 90.32% (56∕62). In addition, the area under the receiver operating characteristic curve is 0.9016. CONCLUSIONS: The authors' proposed study provides a reliable computer-aided system which helps physicians to make diagnoses according to features computed from entire elastographic sequence. Experimental results illustrate that the diagnostic scheme is sufficient in distinguishing benign and malignant tumors. It is not necessary for physicians to spend a lot of time to determine the most suitable image for diagnosis. The tumor boundary tracking mechanism effectively eliminates the computation time since it slightly adjusts tumor boundaries between neighboring slices instead of segmenting the tumor contour on each image in the dynamic elastography. The system sufficiently reduces the variations of diagnosis caused by operator dependencies and image qualities and furthermore save physicians' workloads.

Automatic selection of representative slice from cine-loops of real-time sonoelastography for classifying solid breast masses.

This study aimed to evaluate the performance of automatic selection of representative slice from cine-loops of real-time sonoelastography for classifying benign and malignant breast masses. This retrospective study included 141 ultrasound elastographic studies (93 benign and 48 malignant masses). A novel computer-assisted system was developed for the automatic segmentation of the targeted lesion from cine-loops of real-time sonoelastography. Its hard ratio, defined as the ratio of the number of hard pixels within the tumor divided by the total number of pixels of the whole tumor, was also calculated. The targeted mass was segmented by edge-detection and region growing methods, with combined motion registration after manually defining the original seed. Signal-to-noise ratio (SNR(e)) and contrast-to-noise ratio (CNR(e)) of ultrasound elastogram were computed to obtain an optimum slice for differentiating benign and malignant lesions. The diagnostic results of automatic slice selection using maximum strain, maximum SNR(e), maximum CNR(e), maximum compression and the slices selected by radiologists were compared. Mann-Whitney U test, performance indexes and receiver operating characteristic (ROC) curves were used for statistical analysis. Performance using the maximum SNR(e) (accuracy 84.4%, sensitivity 83.3%, specificity 85.0% and A(z) value 0.90) was the best as compared with those of maximum CNR(e) (82.3%, 79.2%, 83.9% and 0.88, respectively), maximum compression (78.0%, 79.2%, 77.4% and 0.85, respectively), maximum strain (79.4%, 79.2%, 79.6% and 0.87, respectively) and radiologists' selection (77.3%, 77.1%, 77.4% and 0.80, respectively). Automatic selection of representative slice from the cine-loops of real-time sonoelastography is a practical, objective and accurate approach for classifying solid breast masses.

Breast tumor classification using fuzzy clustering for breast elastography.

Elastography is a new ultrasound imaging technique to provide the information about relative tissue stiffness. The elasticity information provided by this dynamic imaging method has proven to be helpful in distinguishing benign and malignant breast tumors. In previous studies for computer-aided diagnosis (CAD), the tumor contour was manually segmented and each pixel in the elastogram was classified into hard or soft tissue using the simple thresholding technique. In this paper, the tumor contour was automatically segmented by the level set method to provide more objective and reliable tumor contour for CAD. Moreover, the elasticity of each pixel inside each tumor was classified by the fuzzy c-means clustering technique to obtain a more precise diagnostic result. The test elastography database included 66 benign and 31 malignant biopsy-proven tumors. In the experiments, the accuracy, sensitivity, specificity and the area index Az under the receiver operating characteristic curve for the classification of solid breast masses were 83.5% (81/97), 83.9% (26/31), 83.3% (55/66) and 0.902 for the fuzzy c-means clustering method, respectively, and 59.8% (58/97), 96.8% (30/31), 42.4% (28/66) and 0.818 for the conventional thresholding method, respectively. The differences of accuracy, specificity and Az value were statistically significant (p < 0.05). We conclude that the proposed method has the potential to provide a CAD tool to help physicians to more reliably and objectively diagnose breast tumors using elastography.

Color-based tumor tissue segmentation for the automated estimation of oral cancer parameters.

This article presents an automatic color-based feature extraction system for parameter estimation of oral cancer from optical microscopic images. The system first reduces image-to-image variations by means of color normalization. We then construct a database which consists of typical cancer images. The color parameters extracted from this database are then used in automated online sampling from oral cancer images. Principal component analysis is subsequently used to divide the color features into four tissue types. Each pixel in the cancer image is then classified into the corresponding tissue types based on the Mahalanobis distance. The aforementioned procedures are all fully automated; in particular, the automated sampling step greatly reduces the need for intensive labor in manual sampling and training. Experiments reveal high levels of consistency among the results achieved using the manual, semiautomatic, and fully automatic methods. Parameter comparisons between the four cancer stages are conducted, and only the mean parameters between early and late cancer stages are statistically different. In summary, the proposed system provides a useful and convenient tool for automatic segmentation and evaluation for stained biopsy samples of oral cancer. This tool can also be modified and applied to other tissue images with similar staining conditions.

A color-based approach for automated segmentation in tumor tissue classification.

This paper presents a new color-based approach for automated segmentation and classification of tumor tissues from microscopic images. The method comprises three stages: (1) color normalization to reduce the quality variation of tissue image within samples from individual subjects or from different subjects; (2) automatic sampling from tissue image to eliminate tedious and time-consuming steps; and (3) principal component analysis (PCA) to characterize color features in accordance with a standard set of training data. We evaluate the algorithm by comparing the performance of the proposed fully-automated method against semi-automated procedures. Experimental studies show consist agreement between the two methods. Thus, the proposed algorithm provides an effective tool for evaluating oral cancer images. It can also be applied to other microscopic images prepared with the same type of tissue staining.