Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms

Purpose We propose a method for the detection of architectural distortion in prior mammograms of interval-cancer cases based on the expected orientation of breast tissue patterns in mammograms. Methods The expected orientation of the breast tissue at each pixel was derived by using automatically detected landmarks including the breast boundary, the nipple, and the pectoral muscle (in mediolateral-oblique views). We hypothesize that the presence of architectural distortion changes the normal expected orientation of breast tissue patterns in a mammographic image. The angular deviation of the oriented structures in a given mammogram as compared to the expected orientation was analyzed to detect potential sites of architectural distortion using a measure of divergence of oriented patterns. Each potential site of architectural distortion was then characterized using measures of spicularity and angular dispersion specifically designed to represent spiculating patterns. The novel features for the characterization of spiculating patterns include an index of divergence of spicules computed from the intensity image and Gabor magnitude response using the Gabor angle response; radially weighted difference and angle-weighted difference (AWD) measures of the intensity, Gabor magnitude, and Gabor angle response; and AWD in the entropy of spicules computed from the intensity, Gabor magnitude, and Gabor angle response. Results Using the newly proposed features with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases, through feature selection and pattern classification with an artificial neural network, an area under the receiver operating characteristic curve of 0.75 was obtained. Free-response receiver operating characteristic analysis indicated a sensitivity of 0.80 at 5.3 false positives (FPs) per patient. Combining the features proposed in the present paper with others described in our previous works led to significant improvement with a sensitivity of 0.80 at 3.7 FPs per patient. Conclusion The proposed methods can detect architectural distortion in prior mammograms taken 15 months (on the average) before clinical diagnosis of breast cancer, but the FP rate needs to be reduced.     

Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms

Purpose

Architectural distortion is an important sign of early breast cancer. We present methods for computer-aided detection of architectural distortion in mammograms acquired prior to the diagnosis of breast cancer in the interval between scheduled screening sessions.

Methods

Potential sites of architectural distortion were detected using node maps obtained through the application of a bank of Gabor filters and linear phase portrait modeling. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs, and from 52 mammograms of 13 normal cases. Each ROI was represented by three types of entropy measures of angular histograms composed with the Gabor magnitude response, angle, coherence, orientation strength, and the angular spread of power in the Fourier spectrum, including Shannon’s entropy, Tsallis entropy for nonextensive systems, and Rényi entropy for extensive systems.

Results

Using the entropy measures with stepwise logistic regression and the leave-one-patient-out method for feature selection and cross-validation, an artificial neural network resulted in an area under the receiver operating characteristic curve of 0.75. Free-response receiver operating characteristics indicated a sensitivity of 0.80 at 5.2 false positives (FPs) per patient.

Conclusion

The proposed methods can detect architectural distortion in prior mammograms taken 15 months (on the average) before clinical diagnosis of breast cancer, with a high sensitivity and a moderate number of FPs per patient. The results are promising and may be improved with additional features to characterize subtle abnormalities and larger databases including prior mammograms.     

Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model

Objective One of the commonly missed signs of breast cancer is architectural distortion. We have developed techniques for the detection of architectural distortion in mammograms, based on the analysis of oriented texture through the application of Gabor filters and a linear phase portrait model. In this paper, we propose constraining the shape of the general phase portrait model as a means to reduce the false-positive rate in the detection of architectural distortion. Material and methods The methods were tested with one set of 19 cases of architectural distortion and 41 normal mammograms, and with another set of 37 cases of architectural distortion. Results Sensitivity rates of 84% with 4.5 false positives per image and 81% with 10 false positives per image were obtained for the two sets of images. Conclusion The adoption of a constrained phase portrait model with a symmetric matrix and the incorporation of its condition number in the analysis resulted in a reduction in the false-positive rate in the detection of architectural distortion. The proposed techniques, dedicated for the detection and localization of architectural distortion, should lead to efficient detection of early signs of breast cancer.     

Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis

Objective Mammography is a widely used screening tool for the early detection of breast cancer. One of the commonly missed signs of breast cancer is architectural distortion. The purpose of this study is to explore the application of fractal analysis and texture measures for the detection of architectural distortion in screening mammograms taken prior to the detection of breast cancer. Materials and methods A method based on Gabor filters and phase portrait analysis was used to detect initial candidates for sites of architectural distortion. A total of 386 regions of interest (ROIs) were automatically obtained from 14 “prior mammograms”, including 21 ROIs related to architectural distortion. From the corresponding set of 14 “detection mammograms”, 398 ROIs were obtained, including 18 related to breast cancer. For each ROI, the fractal dimension and Haralick’s texture features were computed. The fractal dimension of the ROIs was calculated using the circular average power spectrum technique. Results The average fractal dimension of the normal (false-positive) ROIs was significantly higher than that of the ROIs with architectural distortion (p = 0.006). For the “prior mammograms”, the best receiver operating characteristics (ROC) performance achieved, in terms of the area under the ROC curve, was 0.80 with a Bayesian classifier using four features including fractal dimension, entropy, sum entropy, and inverse difference moment. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.79 at 8.4 false positives per image in the detection of sites of architectural distortion in the “prior mammograms”. Conclusion Fractal dimension offers a promising way to detect the presence of architectural distortion in prior mammograms.     

Multiple-frequency ultrasonic imaging by transmitting pulsed waves of two frequencies

Purpose  The aim of this study was realization of a broadband measurement system that is capable of effectively carrying out a frequency compound method. In the present method, the secondary wave components of difference and sum frequencies are generated along with the higher harmonic components through the nonlinear interaction of two-frequency ultrasound. A multiple-frequency beam is generated together with the initially radiated frequency components. Methods  For the structure of a transducer capable of simultaneously radiating two sound waves with different frequencies, a coaxial arrangement of a circular-disc piezoelectric transducer and a ring piezoelectric transducer was designed. The radiating frequencies chosen were 2 and 8 MHz. In addition to the 4-MHz second harmonic sound of the 2-MHz primary sound, sounds of the 6-MHz difference frequency and the 10-MHz sum frequency can be generated. Results  By measuring the acoustic pressure distribution, the formation of a multiple-frequency beam was confirmed. The signal-to-noise ratio in an agar-gel phantom image was increased by 5–6 dB with application of the frequency compound method. The validity of the proposed method was demonstrated through the generation of a human finger image. Further, it was found that the influence of the Doppler effect was small enough that almost all the secondary waves were attributable to the nonlinear propagation of sounds. Conclusions  A multiple-frequency sound beam was realized by radiating a two-frequency sound. The effectiveness of the presented method was demonstrated through actual imaging.     

Segmentation of breast ultrasound image with semantic classification of superpixels

Breast cancer is a great threat to females. Ultrasound imaging has been applied extensively in diagnosis of breast cancer. Due to the poor image quality, segmentation of breast ultrasound (BUS) image remains a very challenging task. Besides, BUS image segmentation is a crucial step for further analysis. In this paper, we proposed a novel method to segment the breast tumor via semantic classification and merging patches. The proposed method firstly selects two diagonal points to crop a region of interest (ROI) on the original image. Then, histogram equalization, bilateral filter and pyramid mean shift filter are adopted to enhance the image. The cropped image is divided into many superpixels using simple linear iterative clustering (SLIC). Furthermore, some features are extracted from the superpixels and a bag-of-words model can be created. The initial classification can be obtained by a back propagation neural network (BPNN). To refine preliminary result, k-nearest neighbor (KNN) is used for reclassification and the final result is achieved. To verify the proposed method, we collected a BUS dataset containing 320 cases. The segmentation results of our method have been compared with the corresponding results obtained by five existing approaches. The experimental results show that our method achieved competitive results compared to conventional methods in terms of TP and FP, and produced good approximations to the hand-labelled tumor contours with comprehensive consideration of all metrics (the F1-score = 89.87% ± 4.05%, and the average radial error = 9.95% ± 4.42%).     

乳腺导管原位癌X线表现与病理及年龄对照研究

目的探讨乳腺导管原位癌的X线表现及其与病理分级、患病年龄的相关性。方法经手术、病理证实的94例乳腺导管原位癌患者,观察其X线表现中钙化形态及软组织异常（分类包括肿块、不对称致密、结构扭曲）与病理分级、患病年龄的相关性。结果（1）乳腺导管原位癌的X线摄影表现为钙化者占80．4％。低、中级别中不定型模糊钙化和粗糙不均质钙化占45．8％,高级别中细小多形性钙化和线样分支状钙化占71．1％（P〈O．05）。线段样分布占53．6％,簇状分布占33．3％,区域分布占13．1％。本研究中有63．8％的导管原位癌患者每平方厘米钙化数目超过20枚。（2）肿块、局灶性不对称致密、结构扭曲（伴或不伴钙化）在乳腺导管原位癌的X线摄影中分别占39．1％、21．7％、3．2％。其中,病理为高级别者占64．4％,中、低级别分别占18．6％、17．0％。（3）50岁以前钙化出现的几率是50以岁后的1.3倍（P〈0．05）,单纯钙化容易出现在40-50岁（12．0％）;50岁以后出现软组织异常的比例是50岁以前人群的1．4倍（P〈0．05）。结论乳腺导管原位癌常见的X线摄影表现为单纯钙化、钙化伴肿块、局灶性不对称致密和结构扭曲,与病理分级和年龄有一定相关性,在一定程度上可为临床诊治提供参考。     

Does better access to FPs decrease the likelihood of emergency department use?: Results from the Primary Care Access Survey

Objective

To determine whether better access to FP services decreases the likelihood of emergency department (ED) use among the Ontario population.

Design

Population-based telephone survey.

Setting

Ontario.

Participants

A total of 8502 Ontario residents aged 16 years and older.

Main outcome measures

Emergency department use in the 12 months before the survey.

Results

Among the general population, having a regular FP was associated with having better access to FPs for immediate care (P < .001) but was not associated with a decreased likelihood of ED visits (odds ratio [OR] = 1.49, P = .03). Better actual access to FP services for immediate care was associated with a decreased likelihood of ED use (OR = 0.62, P < .001) among the general population. Among those with chronic diseases, having a regular FP was associated with a decreased likelihood of ED use (OR = 0.47, P = .01). Of the Ontario population, 39.3% wanted to see FPs for immediate care at least once a year; 63.1% of them had seen FPs without difficulties and were significantly less likely to use EDs than those who did not see FPs or had difficulties accessing physicians when needed (OR = 0.62, P < .001). Having a chronic health condition, recent immigrant status, residence in rural and northern parts of Ontario, and lower educational and income levels were significant predictors of a higher likelihood of ED use, independent of access to FPs (P < .05).

Conclusion

A decreased likelihood of ED use is strongly associated with having a regular FP among those with chronic diseases and with having access to FPs for immediate care among the general population. Further research is needed to understand what accounts for a higher likelihood of ED use among those with regular FPs, new immigrants, residents of northern and rural areas of Ontario, and people with low socioeconomic status when actual access and sociodemographic characteristics have been taken into consideration. More important, this study demonstrates a need of distinguishing between potential and actual access to care, as having a regular FP and having timely and effective access to FP care might mean different things and have different effects on ED use.     

The relationship of social status and social image with the choice of nursing career among the next generation of Turkish youth: A cross-sectional study

AimTo evaluate the nursing image and social status characteristics on young people’s decision to choose nursing as a career.BackgroundLow social status and social image threaten the nursing workforce.DesignA cross-sectional study.MethodsThe study was conducted with 745 high school students and 349 nursing students between 24 March and 10 May 2022. The data were collected through the Nursing Image Scale, Job Satisfaction Scale and Personal Form and analyzed using multiple regression and decision tree analysis.ResultsHigh school (43.1 %) and 73.6 % of nursing students expressed their intention to become a nurse. While the likelihood of choosing nursing as a career decreases among man high school students (p = 0.018) with a high occupation (p = 0.003) and income index (p = 0.003), it increases among low-income female students (p = 0.012), perceiving occupational status more positively (p = 0.002). The possibility of continuing in the profession increases among nursing students who have a higher opinion of the profession's status (p = 0.010) and job satisfaction increases as the image perception improves (r = 0.385, p < 0.01).Conclusion and recommendationsPerception of professional image guides career choice in nursing. The choice of nursing as a future profession differs according to gender and is influenced by social status. The glass ceiling effect should be evaluated in nurses coming from low status.     

Achieving consistent image quality with dose optimization in 64-row multidetector computed tomography prospective ECG gated coronary calcium scoring

To evaluate the clinical value of a body mass index (BMI) based tube current (mA) selection method for obtaining consistent image quality with dose optimization in MDCT prospective ECG gated coronary calcium scoring. A formula for selecting mA to achieve desired image quality based on patient BMI was established using a control group (A) of 200 MDCT cardiac patients with a standard scan protocol. One hundred patients in Group B were scanned with this BMI-dependent mA for achieving a desired noise level of 18 HU at 2.5 mm slice thickness. The CTDIvol and image noise on the ascending aorta for the two groups were recorded. Two experienced radiologists quantitatively evaluated the image quality using scores of 1–4 with 4 being the highest. The image quality scores had no statistical difference (P = 0.71) at 3.89 ± 0.32, 3.87 ± 0.34, respectively, for groups A and B of similar BMI. The image noise in Group A had linear relationship with BMI. The image noise in Group B using BMI-dependent mA was independent of BMI with average value of 17.9 HU and smaller deviations for the noise values than in Group A (2.0 vs. 2.9 HU). There was a 35% dose reduction with BMI-dependent mA selection method on average with the lowest effective dose being only 0.35 mSv for patient with BMI of 18.3. A quantitative BMI-based mA selection method in MDCT prospective ECG gated coronary calcium scoring has been proposed to obtain a desired and consistent image quality and provide dose optimization across patient population.     

Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier

Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. In this work, we propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN).In the proposed method, a 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Starting from a single seed point placed manually or automatically anywhere in a coronary artery, a tracker follows the vessel centerline in two directions using the predictions of the CNN. Tracking is terminated when no direction can be identified with high certainty. The CNN is trained using manually annotated centerlines in training images. No image preprocessing is required, so that the process is guided solely by the local image values around the tracker’s location.The CNN was trained using a training set consisting of 8 CCTA images with a total of 32 manually annotated centerlines provided in the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08). Evaluation was performed within the CAT08 challenge using a test set consisting of 24 CCTA test images in which 96 centerlines were extracted. The extracted centerlines had an average overlap of 93.7% with manually annotated reference centerlines. Extracted centerline points were highly accurate, with an average distance of 0.21 mm to reference centerline points. Based on these results the method ranks third among 25 publicly evaluated methods in CAT08. In a second test set consisting of 50 CCTA scans acquired at our institution (UMCU), an expert placed 5448 markers in the coronary arteries, along with radius measurements. Each marker was used as a seed point to extract a single centerline, which was compared to the other markers placed by the expert. This showed strong correspondence between extracted centerlines and manually placed markers. In a third test set containing 36 CCTA scans from the MICCAI 2014 Challenge on Automatic Coronary Calcium Scoring (orCaScore), fully automatic seeding and centerline extraction was evaluated using a segment-wise analysis. This showed that the algorithm is able to fully-automatically extract on average 92% of clinically relevant coronary artery segments. Finally, the limits of agreement between reference and automatic artery radius measurements were found to be below the size of one voxel in both the CAT08 dataset and the UMCU dataset. Extraction of a centerline based on a single seed point required on average 0.4 ± 0.1 s and fully automatic coronary tree extraction required around 20 s.The proposed method is able to accurately and efficiently determine the direction and radius of coronary arteries based on information derived directly from the image data. The method can be trained with limited training data, and once trained allows fast automatic or interactive extraction of coronary artery trees from CCTA images.     

Fusion of contrast-enhanced breast MR and mammographic imaging data

Increasing use is being made of Gd-DTPA contrast-enhanced magnetic resonance imaging for breast cancer assessment since it provides 3D functional information via pharmacokinetic interaction between contrast agent and tumour vascularity, and because it is applicable to women of all ages as well as patients with post-operative scarring. Contrast-enhanced MRI (CE-MRI) is complementary to conventional X-ray mammography, since it is a relatively low-resolution functional counterpart of a comparatively high-resolution 2D structural representation. However, despite the additional information provided by MRI, mammography is still an extremely important diagnostic imaging modality, particularly for several common conditions such as ductal carcinoma in situ (DCIS) where it has been shown that there is a strong correlation between microcalcification clusters and malignancy. Pathological indicators such as calcifications and fine spiculations are not visible in CE-MRI and therefore there is clinical and diagnostic value in fusing the high-resolution structural information available from mammography with the functional data acquired from MRI imaging. This paper presents a novel data fusion technique whereby medial-lateral oblique (MLO) and cranial-caudal (CC) mammograms (2D data) are registered to 3D contrast-enhanced MRI volumes. We utilise a combination of pharmacokinetic modelling, projection geometry, wavelet-based landmark detection and thin-plate spline non-rigid 'warping' to transform the coordinates of regions of interest (ROIs) from the 2D mammograms to the spatial reference frame of the contrast-enhanced MRI volume. Of key importance is the use of a flexible wavelet-based feature extraction technique that enables feature correspondences to be robustly determined between the very different image characteristics of X-ray mammography and MRI. An evaluation of the fusion framework is demonstrated with a series of clinical cases and a total of 14 patient examples.     

Microcalcifications Versus Artifacts: Initial Evaluation of a New Ultrasound Image Processing Technique to Identify Breast Microcalcifications in a Screening Population

A new commercial image processing technique (MicroPure, Toshiba America Medical Systems, Tustin, CA, USA) that identifies breast microcalcifications was evaluated at the time of patients' annual screening mammograms. Twenty women scheduled for annual screening mammography were enrolled in the study. Patients underwent bilateral outer-upper-quadrant real-time dual gray scale ultrasound and MicroPure imaging using an Aplio XG scanner (Toshiba). MicroPure combines non-linear imaging and speckle suppression to mark suspected calcifications as white spots in a blue overlay image. Four independent and blinded readers analyzed digital clips to determine the presence or absence of microcalcifications and artifacts. The presence of microcalcifications determined by readers was not significantly different from that of mammography (p = 0.57). However, the accuracy was low overall (52%) and also in younger women (<50 years, 54%). In conclusion, although microcalcifications can be identified using MicroPure imaging, this method is not currently appropriate for a screening population and should be used in more focused applications.     

3D ultrasound-CT registration of the liver using combined landmark-intensity information

Purpose  An important issue in computer-assisted surgery of the liver is a fast and reliable transfer of preoperative resection plans to the intraoperative situation. One problem is to match the planning data, derived from preoperative CT or MR images, with 3D ultrasound images of the liver, acquired during surgery. As the liver deforms significantly in the intraoperative situation non-rigid registration is necessary. This is a particularly challenging task because pre- and intraoperative image data stem from different modalities and ultrasound images are generally very noisy. Methods  One way to overcome these problems is to incorporate prior knowledge into the registration process. We propose a method of combining anatomical landmark information with a fast non-parametric intensity registration approach. Mathematically, this leads to a constrained optimization problem. As distance measure we use the normalized gradient field which allows for multimodal image registration. Results  A qualitative and quantitative validation on clinical liver data sets of three different patients has been performed. We used the distance of dense corresponding points on vessel center lines for quantitative validation. The combined landmark and intensity approach improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks. Conclusion  The proposed algorithm offers the possibility to incorporate additional a priori knowledge—in terms of few landmarks—provided by a human expert into a non-rigid registration process.     

结节型局灶性肺炎CT影像分析

目的探讨结节型局灶性肺炎的CT征像，提高对本病的认识。方法回顾性分析经临床证实的8例结节型局灶性肺炎的CT资料。结果病变部位：该病大多部于肺外周、胸膜下区．主要CT表现：病灶与相邻胸膜的关系密切：常与邻近胸膜广泛均匀增厚，呈广基相连，部分病灶中部层面有垂直部分呈“刀切样”边缘及出现“方形征”；病灶多呈现楔形或不规则形；病灶边缘可见粗长毛刺状改变，部分病灶与肺门之间可见一条或多条增粗血管相连；病灶内支气管充气征；抗炎治疗后病灶7例吸收缩小，1例无变化。结论病灶部位与CT征像对结节型局灶性肺炎的诊断、鉴别诊断有很大的价值。     

MR空间精度测试体模与图像校正方法

目的 观察自行设计的MR空间精度测试体模及图像校正方法在临床MRI定位精度方面的应用价值。方法 根据MR质量控制标准设计并校验体模,对试验获取的MRI进行预处理、特征提取、检测与校正,提取空间各控制点的失真参数,计算几何畸变情况并校正失真。比较美国放射学会（ACR）与自制体模在相同试验条件下几何畸变率的一致性,分析MR设备几何畸变情况及校正方法的有效性。结果 相同试验条件下,自制体模与ACR体模的几何畸变率一致性良好（ICC>0.96）。校正前试验控制点位置与已标定控制点位置差异有统计学意义（P<0.05）。将体模置于MR设备成像时,所得MR图像存在一定程度几何畸变。校正后试验控制点位置与已标定控制点位置差异无统计学意义（P>0.05）。结论 自制体模能满足临床MR质量监控需求。通过检测与校正几何失真,可在一定程度上保障MRI定位精度,为临床诊断疾病及放射治疗计划提供辅助依据。     

Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map

Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information in localizing most of boundaries and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping applied to 2D and 3D OCT datasets is composed of two steps, one for partitioning the data into important and less important sections, and another one for localization of internal layers. In the first step, the pixels/voxels are grouped in rectangular/cubic sets to form a graph node. The weights of the graph are calculated based on geometric distances between pixels/voxels and differences of their mean intensity. The first diffusion map clusters the data into three parts, the second of which is the area of interest. The other two sections are eliminated from the remaining calculations. In the second step, the remaining area is subjected to another diffusion map assessment and the internal layers are localized based on their textural similarities. The proposed method was tested on 23 datasets from two patient groups (glaucoma and normals). The mean unsigned border positioning errors (mean ± SD) was 8.52 ± 3.13 and 7.56 ± 2.95 μm for the 2D and 3D methods, respectively.     

Simulation of mammographic breast compression in 3D MR images using ICP-based B-spline deformation for multimodality breast cancer diagnosis

Purpose

   Multimodality mammography using conventional 2D mammography and dynamic contrast-enhanced 3D magnetic resonance imaging (DCE-MRI) is frequently performed for breast cancer detection and diagnosis. Combination of both imaging modalities requires superimposition of corresponding structures in mammograms and MR images. This task is challenging due to large differences in (1) dimensionality and spatial resolution, (2) variations in tissue contrast, as well as (3) differences in breast orientation and deformation during the image acquisition. A new method for multimodality breast image registration was developed and tested.

Methods

   Combined diagnosis of mammograms and MRI datasets was achieved by simulation of mammographic breast compression to overcome large differences in breast deformation. Surface information was extracted from the 3D MR image, and back-projection of the 2D breast contour in the mammogram was done. B-spline-based 3D/3D surface-based registration was then used to approximate mammographic breast compression. This breast deformation simulation was performed on 14 MRI datasets with 19 corresponding mammograms. The results were evaluated by comparison with distances between corresponding structures identified by an expert observer.

Results

   The evaluation revealed an average distance of 6.46 mm between corresponding structures, when an optimized initial alignment between both image datasets is performed. Without the optimization, the accuracy is 9.12 mm.

Conclusion

   A new surface-based method that approximates the mammographic deformation due to breast compression without using a specific complex model needed for finite-element-based methods was developed and tested with favorable results. The simulated compression can serve as foundation for a point-to-line correspondence between 2D mammograms and 3D MR image data.     

自适应超分辨率图像的重建算法（英文）

背景：超分辨率重建已经在视频、遥感等许多领域内的到广泛的研究与应用。目的：介绍一种自适应超分辨率重建算法,以期从序列低分辨率图像中重建出高分辨率图像。方法：采用常数λ=2/3作为正则化参数和自适应步长作为第一种方案。第二种方案充分考虑到低分辨率图像中的运动误差估计、点扩散函数以及加性高斯白噪声对重建算法的影响。实验构造出新的非线性自适应正则化函数,进而利用实验方法分析代价函数的凸性。通过数学理论,根据代价函数凸性实验得到自适应步长因子,从而改进了图像的空间分辨率和算法的收敛速度。结果与结论：为验证此算法的有效性,采用光学图像进行实验。方案二图像峰值信噪比增高,其收敛速度为方案一的2倍以上;方案二的平均计算需要的时间为68.25s。结果证实,自适应超分辨率图像重建算法对图像分辨率和迭代的收敛速度均改善显著,其稳定性较好。