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.     

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.     

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.     

Model-based detection of spiculated lesions in mammograms

Computer-aided mammographic prompting systems require the reliable detection of a variety of signs of cancer. In this paper we concentrate on the detection of spiculated lesions in mammograms. A spiculated lesion is typically characterized by an abnormal pattern of linear structures and a central mass. Statistical models have been developed to describe and detect both these aspects of spiculated lesions. We describe a generic method of representing patterns of linear structures, which relies on the use of factor analysis to separate the systematic and random aspects of a class of patterns. We model the appearance of central masses using local scale-orientation signatures based on recursive median filtering, approximated using principal-component analysis. For lesions of 16 mm and larger the pattern detection technique results in a sensitivity of 80% at 0.014 false positives per image, whilst the mass detection approach results in a sensitivity 80% at 0.23 false positives per image. Simple combination techniques result in an improved sensitivity and specificity close to that required to improve the performance of a radiologist in a prompting environment.     

A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows

Objective  We present herein a novel algorithm for architectural distortion detection that utilizes the point convergence index with the likelihood of lines (e.g., spiculations) relating to architectural distortion. Materials and methods  Validation was performed using 25 computed radiography (CR) mammograms, each of which has an architectural distortion with radiating spiculations. The proposed method comprises five steps. First, the lines were extracted on mammograms, such as spiculations of architectural distortion as well as lines in the mammary gland. Second, the likelihood of spiculation for each extracted line was calculated. In the third step, point convergence index weighted by this likelihood was evaluated at each pixel to enhance distortion only. Fourth, local maxima of the index were extracted as candidates for the distortion, then classified based on nine features in the last step. Results  Point convergence index without the proposed likelihood generated 84.48/image false-positives (FPs) on average. Conversely, the proposed index succeeded in decreasing this number to 12.48/image on average when sensitivity was 100%. After the classification step, number of FPs was reduced to 0.80/image with 80.0% sensitivity. Conclusion  Combination of the likelihood of lines with point convergence index is effective in extracting architectural distortion with radiating spiculations.     

全数字化乳腺X线摄影在乳腺癌诊断中的作用

目的分析经手术病理证实的乳腺病变在全数字化乳腺摄影（FFDM）中的影像学表现,提高X线在乳腺癌诊断中的准确性。方法搜集2008年1月-2010年10月313例行FFDM检查、手术和病理证实的乳腺病变患者的临床资料及乳腺X线片,由放射科医师对患者的X线片观察并分析,参照乳腺影像学报告和数据系统（BI-RADS）进行分级,以术后病理结果为金标准,评价FFDM检查诊断乳腺癌的灵敏度、特异度、准确率、阳性预测值和阴性预测值,并分析误诊和漏诊的原因。结果病理结果显示,313例乳腺标本中,乳腺癌194例,乳腺良性病变119例。在乳腺恶性病变中,X线主要表现为单纯肿块型83例,钙化型95例,结构扭曲6例,不对称致密影8例,乳腺内弥漫性腺体结构模糊2例。X线诊断假阳性18例,假阴性20例。FFDM检查对乳腺癌诊断的灵敏度、特异度、准确率、阳性预测值、阴性预测值分别约89.2%、84.9%、87.7%、90.6%、82.8%。误诊的主要原因是肿块的边缘形态、微小钙化等类似于恶性;漏诊的主要原因是乳腺腺体致密、不对称致密影及微小结构扭曲。结论数字化乳腺X线摄影在乳腺癌诊断中具有较高的价值。     

基于深度学习技术的乳腺智能检测系统在触诊阴性乳腺肿瘤诊断中的应用

目的 拟探讨基于深度学习技术的乳腺X线智能检测系统在临床触诊阴性乳腺肿瘤诊断中的应用价值.方法 回顾性收集2014年1月至2016年12月期间就诊于陕西省肿瘤医院的临床触诊阴性乳腺肿瘤患者322例,均手术治疗且临床病理资料齐全.使用MammoWorks?乳腺智能检测系统对所有入组患者乳腺X线图片进行分析,以术后病理结果...     

A biologically inspired algorithm for microcalcification cluster detection

The early detection of breast cancer greatly improves prognosis. One of the earliest signs of cancer is the formation of clusters of microcalcifications. We introduce a novel method for microcalcification detection based on a biologically inspired adaptive model of contrast detection. This model is used in conjunction with image filtering based on anisotropic diffusion and curvilinear structure removal using local energy and phase congruency. An important practical issue in automatic detection methods is the selection of parameters: we show that the parameter values for our algorithm can be estimated automatically from the image. This way, the method is made robust and essentially free of parameter tuning. We report results on mammograms from two databases and show that the detection performance can be improved by first including a normalisation scheme.     

不同CT图像重建算法下基于深度学习的肺结节检测算法效能

目的 探索CT图像重建算法对于基于深度学习（DL）的肺结节检测算法的影响。方法 选取298例接受肺部CT检查患者,依次采用肺窗重建、纵隔重建、骨窗重建3种算法重建CT图像。先由2名主治医师对入组病例进行标注,结果不一致时由1名高年资医师进行审核,以结果作为金标准。以深度神经网络为基础构建肺结节检测算法,与医师标注结果进行比对,得到算法在不同重建方法下检出肺结节的敏感度、准确率、F分数等指标以及模型检出的假阳性分布,对比分析模型在不同CT图像重建算法下的诊断效果。结果 基于DL的肺结节检测算法在肺重建、纵隔重建和骨重建3种重建方法下的敏感度分别为92.33%（313/339）、86.97%（287/330）及92.73%（319/344）,准确率分别为23.55%（313/1 329）、37.91%（287/757）及27.84%（319/1 146）,F分数分别为0.38、0.53及0.43,3种算法重建下模型检出敏感度、模型误检结节类型与医师漏标结节类型差异均无统计学意义（P均>0.05）。结论 基于DL的肺结节检测算法在肺窗、纵隔和骨窗重建下均性能优良,能帮助医生提高工作效率和诊断质量。     

计算机辅助检测系统对乳腺癌检出的价值评估

目的：评估全数字化乳腺X线摄影的计算机辅助检测系统（CAD）对乳腺癌检出的临床应用价值.材料与方法：收集97例经手术病理证实的乳腺癌全数字化乳腺X线摄影图像,所有病例均经CAD软件检测,记录乳腺癌的X线征象、BI-RADS分类,病理类型并评估CAD检出的敏感性.结果：共有41例（42.3％）肿块,18例（18.5％）钙化,30例（31％）肿块合并钙化,7例（7.2％）结构扭曲和1例（1％）结构不对称.CAD检出乳腺癌X线征象总敏感性为88.7％（肿块,85.4％;钙化,94.4％;肿块合并钙化,100％;结构扭曲,57.1％）,97例乳腺癌有11例（6例肿块;3例结构扭曲;1例钙化;1例结构不对称）未被CAD检出.肿块形状、边缘、BI-RADS分类及病理类型与CAD检测没有显著差异性.CAD检测有27.2％特异性及46.7％假阳性率）.结论：CAD检测显示了高敏感性及低特异性.乳腺癌肿块形状、边缘、BI-RADS分类及病理类型不影响CAD检出敏感性,肿块密度、结构扭曲的毛刺粗细影响CAD的检出率.     

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.     

Computerized analysis of images in the detection and diagnosis of breast cancer.

Improvements in mammographic acquisition techniques have resulted in making the early signs of breast cancer more apparent on mammograms. However, the accuracy of the overall mammographic examination depends on both the quality of the mammographic images and the ability of the radiologist to interpret those images. While mammography is the best screening method for the early detection of breast cancer, radiologists do miss lesions on mammograms. Use of output, however, from a computerized analysis of an image by a radiologist may help him/her in the detection or diagnostic tasks, and potentially improve the overall interpretation of breast images and the subsequent patient care. Computer-aided detection and diagnosis (CAD) involves the application of computer technology to the process of medical image interpretation. CAD can be defined as a diagnosis made by a radiologist, who uses the output from a computerized analysis of medical images as a "second opinion" in detecting and diagnosing lesions, with the final diagnosis being made by the radiologist. The computer output must be at a sufficient performance level, and in addition, the output must be displayed in a user-friendly format for effective and efficient use by the radiologist. This chapter reviews CAD in breast cancer detection and diagnosis, including examples of image analyses, multi-modality approaches (i.e., special-view diagnostic mammography, ultrasound, and MRI), and means of communicating the computer output to the human.     

Space-Based Abstinence: Alleviation of Withdrawal Symptoms in Combinative Cocaine-Phencyclidine Abuse

Abstract

Background. Previous reports describe false-positive urine immunoassay screens for phencyclidine (PCP) associated with use of tramadol, dextromethorphan, or diphenhydramine. The likelihood of these false positives is unknown. Objective. We sought to find the relative frequency of false-positive PCP screens associated with these medications and to look for any other medications with similar associations. Methods. In an IRB-approved study, we retrospectively reviewed charts of all ED encounters with positive urine screens for PCP in our hospital from 2007 through 2011, inclusive. Urine samples were tested for drugs of abuse using the Siemens Syva EMIT II Immunoassay. Our laboratory routinely confirmed all positive screens using GC-MS with results classified as either “confirmed” (true positive) or “failed to confirm” (false positive). We recorded all medications mentioned in the chart as current medications or medications given before the urine sample. We used Fisher's exact test to compare frequencies of tramadol, dextromethorphan, diphenhydramine, and other medications between the two groups. Results. Tramadol, dextromethorphan, alprazolam, clonazepam, and carvedilol were significantly more frequent among the false-positive group, but the latter three were also associated with polysubstance abuse. Diphenhydramine was more frequently recorded among the false-positive group, but this was not statistically significant. Conclusion. False-positive urine screens for PCP are associated with tramadol and dextromethorphan and may also occur with diphenhydramine. Positive PCP screens associated with alprazolam, clonazepam, and carvedilol were also associated with polysubstance abuse.     

计算机辅助检测系统对数字化乳腺X线图像检测的可重复性

目的 观察计算机辅助检测系统(CAD)检出全数字化乳腺X线图像中良恶性肿块及钙化灶的可重复性。 方法 454例乳腺疾病患者经手术病理证实,其中67例乳腺癌患者于3个月内接受两次乳腺X线检查。比较数字化图像直接获得的CAD结果(CAD1)与两次重新回输原始数据生成的CAD结果(CAD2、CAD3)的一致性,评价CAD系统短期内对乳腺X线图像检测结果的可重复性。 结果 CAD1、CAD2、CAD3在肿块及钙化灶检出的数目及所标记的位置上完全相同。67例短期内两次乳腺X线检查病例中,32例病灶大小、密度未见变化,初次和再次CAD发现恶性病灶的敏感度分别为87.50%(28/32)和90.63%(29/32)。 结论 对于相同的数字化图像,CAD标记的重复率为100%。对于相同乳腺短期内两次X线检查图像,CAD系统检出乳腺癌具有较高的可重复性。     

几何模型匹配乳腺头足位与内外斜位X线片所示病灶

目的 观察几何模型(GM)匹配乳腺头足(CC)位与内外斜(MLO)位X线片所示病灶的价值。方法 回顾性分析493例接受乳腺CC位和MLO位X线摄影的乳腺病灶患者,共598个乳腺病灶,包括499个钙化灶和99个肿块。构建GM用于匹配CC与MLO位片所示乳腺病灶,再以环形法(AB)和直线法(SS)进行对比,分别计算匹配误差,包括GM匹配误差、AB径向误差及SS轴向误差;分析GM对CC及MLO位图像中同一病灶的匹配性能,评价其应用价值。结果 GM对乳腺钙化灶和肿块的匹配误差分别为2.85(1.45,5.08)及3.70(1.35,6.25)mm,差异无统计学意义(Z=-1.344,P=0.179)。对乳腺上部病灶,AB匹配的径向误差和SS匹配的轴向误差均大于下部病灶(P均<0.001);对乳腺外侧病灶,AB的径向误差和SS的轴向误差均大于内侧病灶(P均<0.05)。GM、AB及SS间匹配误差整体差异有统计学意义(H=93.012,P<0.001);两两比较差异均有统计学意义(P均<0.05),GM匹配性能明显优于AB和SS。GM匹配误差与摄片时乳腺压迫厚度无明显相关性...     

Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model

We have developed a new computer-aided diagnosis scheme for automated detection of lung nodules in digital chest radiographs based on a combination of morphological features and the wavelet snake. In our scheme, two processes were applied in parallel to reduce the false-positive detections after initial nodule candidates were selected. One process consisted of adaptive filtering for enhancement of nodules and suppression of normal lung structures, followed by extraction of conventional morphological features. The other process consisted of a novel approach for elimination of false positives called the edge-guided wavelet snake model. In the latter process, multiscale edges of the candidate nodules were extracted to yield parts of the nodule boundaries. A wavelet snake was then used for fitting of these multiscale edges for approximation of the true boundaries of nodules. A boundary feature called the weighted overlap between the snake and the multiscale edges was calculated and used for elimination of false positives. Finally, the weighted overlap and the morphological features were combined by use of an artificial neural network for efficient reduction of false positives. Our scheme was applied to a publicly available database of digital chest images for pulmonary nodules. Receiver operating characteristic analysis was employed for evaluation of the performance of each process in the scheme. The combined features yielded a large reduction of false positives, and thus achieved a high performance in discriminating between true and false positives. These results show that our new method, in particular the false-positive reduction method based on the wavelet snake, is effective in improving the performance of a computerized scheme for detection of pulmonary nodules in chest radiographs.     

乳腺单纯性浸润性小叶癌的临床、X线、超声特征

目的 观察乳腺单纯性浸润性小叶癌(ILC)的临床、X线和超声检查特征,评价联合应用三种方法的价值.方法 回顾55例经手术病理证实为单纯性ILC的详细检查结果,分析得出假阴性结果的原因.结果 主要临床检查结果为肿块(96.15%)和腺体增厚(3.85%).主要X线检查结果为边缘不规则肿块(65.38%)和结构扭曲(23.08%).主要超声检查结果为肿块(98.00%)和结构紊乱(2.00%).临床、X线、超声对病灶的定性诊断准确率分别为94.55%、83.87%、98.04%,联合应用的准确率为100%.结论 超声检测ILC病灶的准确性高于X线检查.联合应用临床、X线、超声可提高ILC的术前诊断准确率.     

Unsupervised lesion detection via image restoration with a normative prior

Unsupervised lesion detection is a challenging problem that requires accurately estimating normative distributions of healthy anatomy and detecting lesions as outliers without training examples. Recently, this problem has received increased attention from the research community following the advances in unsupervised learning with deep learning. Such advances allow the estimation of high-dimensional distributions, such as normative distributions, with higher accuracy than previous methods. The main approach of the recently proposed methods is to learn a latent-variable model parameterized with networks to approximate the normative distribution using example images showing healthy anatomy, perform prior-projection, i.e. reconstruct the image with lesions using the latent-variable model, and determine lesions based on the differences between the reconstructed and original images. While being promising, the prior-projection step often leads to a large number of false positives. In this work, we approach unsupervised lesion detection as an image restoration problem and propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation. The probabilistic model punishes large deviations between restored and original images, reducing false positives in pixel-wise detections. Experiments with gliomas and stroke lesions in brain MRI using publicly available datasets show that the proposed approach outperforms the state-of-the-art unsupervised methods by a substantial margin, +0.13 (AUC), for both glioma and stroke detection. Extensive model analysis confirms the effectiveness of MAP-based image restoration.     

Computerized Detection of Breast Lesions Using Deformable Part Models in Ultrasound Images

Ultrasound imaging is considered an important complementary technique for the screening of dense breasts. Detection of lesions at an early stage is a key step in which computerized lesion detection systems could play an important role in the analysis of US images. In this article, we propose adaptation of a generic object detection technique, deformable part models, to detect lesions in breast US images. The data set used in this study included 326 images, all from different patients (54 malignant lesions, 109 benign lesions and 163 healthy breasts). In terms of lesion detection, our proposal outperformed some of the most relevant approaches described in the literature; we obtained a sensitivity of 86% with 0.28 false-positive detection per image and an A_z value of 0.975. In the detection of malignant lesions, our proposed approached had an A_z value of 0.93 and a sensitivity of 78% at a 1.15 false-positive detections per image.