Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features

RATIONALE AND OBJECTIVES: Computer-aided diagnosis (CAD) systems based on shape analysis have been proved to be highly accurate in evaluating breast tumors. However, it takes considerable time to train the classifier and diagnose breast tumors, because extracting morphologic features require a lot of computation. Hence, to develop a highly accurate and quick CAD system, we combined the texture and morphologic features of ultrasound breast tumor imaging to evaluate breast tumors in this study. MATERIALS AND METHODS: This study evaluated 210 ultrasound breast tumor images, including 120 benign tumors and 90 malignant tumors. The breast tumors were segmented automatically by the level set method. The autocovariance texture features and solidity morphologic feature were extracted, and a support vector machine was used to identify the tumor as benign or malignant. RESULTS: The accuracy of the proposed diagnostic system for classifying breast tumors was 92.86%, the sensitivity was 94.44%, the specificity was 91.67%, the positive predictive value was 89.47%, and the negative predictive value was 95.65%. In addition, the proposed system reduced the training time compared to systems based only on the morphologic analysis. CONCLUSIONS: The CAD system based on texture and morphologic analysis can differentiate benign from malignant breast tumors with high accuracy and short training time. It is therefore clinically useful to reduce the number of biopsies of benign lesions and offer a second reading to assist inexperienced physicians in avoiding misdiagnosis.     

Computer-aided diagnosis with textural features for breast lesions in sonograms

Rationale and objectives

Computer-aided diagnosis (CAD) systems provided second beneficial support reference and enhance the diagnostic accuracy. This paper was aimed to develop and evaluate a CAD with texture analysis in the classification of breast tumors for ultrasound images.

Materials and methods

The ultrasound (US) dataset evaluated in this study composed of 1020 sonograms of region of interest (ROI) subimages from 255 patients. Two-view sonogram (longitudinal and transverse views) and four different rectangular regions were utilized to analyze each tumor. Six practical textural features from the US images were performed to classify breast tumors as benign or malignant. However, the textural features always perform as a high dimensional vector; high dimensional vector is unfavorable to differentiate breast tumors in practice. The principal component analysis (PCA) was used to reduce the dimension of textural feature vector and then the image retrieval technique was performed to differentiate between benign and malignant tumors. In the experiments, all the cases were sampled with k-fold cross-validation (k = 10) to evaluate the performance with receiver operating characteristic (ROC) curve.

Results

The area (A_Z) under the ROC curve for the proposed CAD system with the specific textural features was 0.925 ± 0.019. The classification ability for breast tumor with textural information is satisfactory.

Conclusions

This system differentiates benign from malignant breast tumors with a good result and is therefore clinically useful to provide a second opinion.     

基于灰度共生矩阵的乳腺钼靶图像分析

目的 应用灰度共生矩阵对乳腺钼靶图像进行纹理分析,自动分类识别乳腺肿块,实现乳腺肿瘤的辅助检测.资料与方法 纳入60例乳腺钼靶图像,其中正常乳腺组织20例,良恶性乳腺肿块各20例.对图像进行预处理后,计算各感兴趣区基于灰度共生矩阵的纹理特征值,采用支持向量机和概率神经网络分别对肿块进行分类.结果 三组各项纹理特征参数间差异有统计学意义(P＜0.05);d=2时支持向量机的三组分类准确率为91.67％、86.73％、95.00％,SPREAD值取0.1时概率神经网络的三组分类准确率为79.22％、81.77％、81.13％.结论 文中计算的纹理特征参数对乳腺肿块的良恶性判别有较显著的规律,支持向量机的分类准确率比概率神经网络的分类准确率高,该方法可成为乳腺肿瘤良恶性辅助诊断的有效方法之一.     

Classification of breast ultrasound images using fractal feature

Fractal analyses have been applied successfully for the image compression, texture analysis, and texture image segmentation. The fractal dimension could be used to quantify the texture information. In this study, the differences of gray value of neighboring pixels are used to estimate the fractal dimension of an ultrasound image of breast lesion by using the fractal Brownian motion. Furthermore, a computer-aided diagnosis (CAD) system based on the fractal analysis is proposed to classify the breast lesions into two classes: benign and malignant. To improve the classification performances, the ultrasound images are preprocessed by using morphology operations and histogram equalization. Finally, the k-means classification method is used to classify benign tumors from malignant ones. The US breast image databases include only histologically confirmed cases: 110 malignant and 140 benign tumors, which were recorded. All the digital images were obtained prior to biopsy using by an ATL HDI 3000 system. The receiver operator characteristic (ROC) area index AZ is 0.9218, which represents the diagnostic performance.     

Vascular morphologic information of three-dimensional power Doppler ultrasound is valuable in the classification of breast lesions

Doppler ultrasound imaging provides vascular information that could characterize benign and malignant breast masses in many previous publications. In this study, we applied vascular quantification and morphology features derived from three-dimensional power Doppler ultrasound as classifiers based on support vector machine. An Az value under the receiver operating characteristic (ROC) curve was used to measure the significance of each vascularization feature. Sixty solid breast tumors were assessed. According to the Az value for the ROC curve of the selected features, the classification performance of the proposed method was 0.8423, indicating that vascular morphologic information is valuable in the classification of breast lesions.     

Support vector machines in sonography: application to decision making in the diagnosis of breast cancer

We evaluated a series of pathologically proven breast tumors using the support vector machine (SVM) in the differential diagnosis of solid breast tumors. This study evaluated two ultrasonic image databases, i.e., DB1 and DB2. The DB1 contained 140 ultrasonic images of solid breast nodules (52 malignant and 88 benign). The DB2 contained 250 ultrasonic images of solid breast nodules (35 malignant and 215 benign). The physician-located regions of interest (ROI) of sonography and textual features were utilized to classify breast tumors. An SVM classifier using interpixel textual features classified the tumor as benign or malignant. The receiver operating characteristic (ROC) area index for the proposed system on the DB1 and the DB2 are 0.9695+/-0.0150 and 0.9552+/-0.0161, respectively. The proposed system differentiates solid breast nodules with a relatively high accuracy and helps inexperienced operators avoid misdiagnosis. The main advantage in the proposed system is that the training procedure of SVM was very fast and stable. The training and diagnosis procedure of the proposed system is almost 700 times faster than that of multilayer perception neural networks (MLPs). With the growth of the database, new ultrasonic images can be collected and used as reference cases while performing diagnoses. This study reduces the training and diagnosis time dramatically.     

Computer-aided diagnosis of peripheral soft tissue masses based on ultrasound imaging

Medical ultrasound (US) has been widely used for distinguishing benign from malignant peripheral soft tissue tumors. However, diagnosis by US is subjective and depends on the experience of the radiologists. The rarity of peripheral soft tissue tumors can make them easily neglected and this frequently leads to delayed diagnosis, which results in a much higher death rate than with other tumors. In this paper, we developed a computer-aided diagnosis (CAD) system to diagnose peripheral soft tissue masses on US images. We retrospectively evaluated 49 cases of pathologically proven peripheral soft tissue masses (32 benign, 17 malignant). The proposed CAD system includes three main procedures: image pre-processing and region-of-interest (ROI) segmentation, feature extraction and statistics-based discriminant analysis (DA). We developed a depth-normalization factor (DNF) to compensate for the influence of the depth setting on the apparent size of the ROI. After image pre-processing and normalization, five features, namely area (A), boundary transition ratio (T), circularity (C), high intensity spots (H) and uniformity (U), were extracted from the US images. A DA function was then employed to analyze these features. A CAD algorithm was then devised for differentiating benign from malignant masses. The CAD system achieved an accuracy of 87.8%, a sensitivity of 88.2%, a specificity of 87.5%, a positive predictive value (PPV) 78.9% and a negative predictive value (NPV) 93.3%. These results indicate that the CAD system is valuable as a means of providing a second diagnostic opinion when radiologists carry out peripheral soft tissue mass diagnosis.     

妇科影像报告和数据系统在附件肿瘤良恶性鉴别中的价值

目的:探讨妇科影像报告和数据系统(GI-RADS)在附件肿瘤良恶性鉴别中的应用价值.方法:回顾性分析因附件肿瘤行手术切除的患者144例,术前均行妇科超声检查.根据附件肿瘤的超声声像图特征对其进行GI-RADS分类,以病理结果为金标准,计算GI-RADS分类的诊断效能及最佳临界值,并用Kappa检验评价不同年资医师的诊断...     

影像组学分析和机器学习在肺结节良恶性分类中的应用

目的:探讨CT纹理特征对良恶性肺结节的鉴别价值及在独立数据集上的泛化能力.方法:回顾性分析LIDC-IDRI和LUNGx数据库中共1428个肺结节(直径3～30 mm)的CT图像,其中良性1221个、恶性207个.将LIDC-IDRI数据库的1372个结节(良性1190个,恶性182个)作为训练集,LUNGx数据库的5...     

Breast ultrasound computer-aided diagnosis using BI-RADS features

RATIONALE AND OBJECTIVES: Based on the definitions in mass category of Breast Imaging Reporting and Data System developed by American College of Radiology, eight computerized features including shape, orientation, margin, lesion boundary, echo pattern, and posterior acoustic feature classes are proposed. MATERIALS AND METHODS: Our experimental database consists of 265 pathology-proven cases including 180 benign and 85 malignant masses. The capacity of each proposed feature in differentiating malignant from benign masses was validated by Student's t test and the correlation between each proposed feature and the pathological result was evaluated by point biserial coefficient. Binary logistic regression model was used to relate all proposed features and pathological result as a computer-aided diagnosis (CAD) system. The diagnostic value of each proposed feature in the CAD system was further evaluated by the feature selection methods. Additionally, the likelihood of malignancy for each individual feature was also estimated by binary logistic regression. RESULTS: On each proposed feature, the malignant cases were significantly different from the benign ones. The correlation between the angular characteristic and pathological result was indicated as very high. Three substantial correlations appear in features irregular shape, undulation characteristic, and degree of abrupt interface, but the relationship for orientation feature is low. For the constructed CAD system, the performance indices accuracy, sensitivity, specificity, PPV, and NPV were 91.70% (243 of 265), 90.59% (77 of 85), 92.22% (166 of 180), 84.62% (77 of 91), and 95.40% (166 of 174), respectively, and the area index in the ROC analysis was 0.97. Compared with the significant contribution of angular characteristic, the diagnostic values of posterior acoustic feature and orientation feature were relatively low for the CAD system. When three or more angular characteristics are discovered or the degree of abrupt interface is lower than 18, the likelihood of malignancy could be predicted as greater than 40%. CONCLUSION: The computerized BI-RADS sonographic features conform to the sign of malignancy in the clinical experience and efficiently help the CAD system to diagnose the mass.     

Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images.

Automated image analysis aims to extract relevant information from contrast-enhanced magnetic resonance images (CE-MRI) of the breast and improve the accuracy and consistency of image interpretation. In this work, we extend the traditional 2D gray-level co-occurrence matrix (GLCM) method to investigate a volumetric texture analysis approach and apply it for the characterization of breast MR lesions. Our database of breast MR images was obtained using a T1-weighted 3D spoiled gradient echo sequence and consists of 121 biopsy-proven lesions (77 malignant and 44 benign). A fuzzy c-means clustering (FCM) based method is employed to automatically segment 3D breast lesions on CE-MR images. For each 3D lesion, a nondirectional GLCM is then computed on the first postcontrast frame by summing 13 directional GLCMs. Texture features are extracted from the nondirectional GLCMs and the performance of each texture feature in the task of distinguishing between malignant and benign breast lesions is assessed by receiver operating characteristics (ROC) analysis. Our results show that the classification performance of volumetric texture features is significantly better than that based on 2D analysis. Our investigations of the effects of various of parameters on the diagnostic accuracy provided means for the optimal use of the approach.     

Solid breast masses: classification with computer-aided analysis of continuous US images obtained with probe compression

PURPOSE: To prospectively evaluate the accuracy of continuous ultrasonographic (US) images obtained during probe compression and computer-aided analysis for classification of biopsy-proved (reference standard) benign and malignant breast tumors. MATERIALS AND METHODS: This study was approved by the local ethics committee, and informed consent was obtained from all included patients. Serial US images of 100 solid breast masses (60 benign and 40 malignant tumors) were obtained with US probe compression in 86 patients (mean age, 45 years; range, 20-67 years). After segmentation of tumor contours with the level-set method, three features of strain on tissue from probe compression--contour difference, shift distance, area difference--and one feature of shape--solidity-were computed. A maximum margin classifier was used to classify the tumors by using these four features. The Student t test and receiver operating characteristic curve analysis were used for statistical analysis. RESULTS: The mean values of contour difference, shift distance, area difference, and solidity were 3.52% +/- 2.12 (standard deviation), 2.62 +/- 1.31, 1.08% +/- 0.85, and 1.70 +/- 1.85 in malignant tumors and 9.72% +/- 4.54, 5.04 +/- 2.79, 3.17% +/- 2.86, and 0.53 +/- 0.63 in benign tumors, respectively. Differences with P < .001 were statistically significant for all four features. Area under the receiver operating characteristic curve (A(Z)) values for contour difference, shift distance, area difference, and solidity were 0.88, 0.85, 0.86, and 0.79, respectively. The A(Z) value of three features of strain was significantly higher than that of the feature of shape (P < .01). The accuracy, sensitivity, specificity, and positive and negative predictive values of US classifications that were based on values for these four features were 87.0% (87 of 100), 85% (34 of 40), 88% (53 of 60), 83% (34 of 41), and 90% (53 of 59), respectively, with an A(Z) value of 0.91. CONCLUSION: Continuous US images obtained with probe compression and computer-aided analysis can aid in classification of benign and malignant breast tumors.     

超声弹性成像技术及三维成像技术对乳腺肿块诊断的临床价值

目的：探讨超声弹性成像技术结合三维成像技术在乳腺肿块良恶性临床鉴别与诊断中的应用价值。方法本文对125例接受诊治的乳腺肿块者采用超声弹性成像技术结合三维成像技术进行良恶性鉴别和诊断,并与病理结果对照。结果125例患者的139个病灶中恶性病灶19个,占13．67％;良性病灶120个,占86．33％。按照超声弹性成像评分3分以下（包括3分）为良性病灶标准,实时组织弹性成像评分在4分以上（包括4分）为恶性病灶标准,进行超声弹性成像评分鉴别诊断乳腺病灶良恶性的效能分析发现,超声弹性成像评分对良恶性鉴别诊断符合率为89．93％。超声弹性成像技术结合三维成像技术后,从形态结构、内部回声、与周边组织关系及肿块内血流情况,进一步判断乳腺肿块的良恶性,结果显示超声弹性成像技术结合三维成像技术诊断对良恶性鉴别诊断符合率为94．24％。结论超声弹性成像技术结合三维成像技术综合评价乳腺肿块的良、恶性,三维超声能提供更加丰富的三维空间信息,超声弹性成像可以有效发现微小病灶,二者互补,可以明显提高超声影像技术的准确率。     

Quantitative characterization of mass lesions on digitized mammograms for computer-assisted diagnosis

RATIONALE AND OBJECTIVES: To investigate features for discriminating benign from malignant mammographic findings by using computer-aided diagnosis (CAD) and to test the accuracy of CAD interpretations of mass lesions. METHODS: Fifty-five sequential, mammographically detected mass lesions, referred for biopsy, were digitized for computerized reevaluation with a CAD system. Quantitative features that characterize spiculation were automatically extracted by the CAD system. Data generated by 271 known retrospective cases were used to set reference values indicating the range for malignant and benign lesions. After conventional interpretation of the 55 prospective cases, they were evaluated a second time by the radiologist using the extracted features and the reference ranges. In addition, a pattern-recognition scheme based on the extracted features was used to classify the prospective cases. Accuracy of interpretation with and without the CAD system was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Sensitivity of the CAD diagnosis for the prospective cases improved from 92% to 100%. Specificity improved significantly from 26.7% to 66.7%. This was accompanied by a significant increase in the accuracy of diagnosis from 56.4% to 81.8% and in the positive predictive value from 51.1% to 71.4%. The Az for the CAD ROC curve significantly increased from 0.73 to 0.90. The performance of the classification scheme was slightly lower than that of the radiologists' interpretation with the CAD system. CONCLUSIONS: Use of the CAD system significantly improved the accuracy of diagnosis. The findings suggest that the classification scheme may improve the radiologist's ability to differentiate benign from malignant mass lesions in the interpretation of mammograms.     

肿块深度对乳腺超声弹性成像检查结果的影响分析

目的 探究超声弹性成像(UE)中肿瘤深度对于判断乳腺肿物的良、恶性影响.方法 选取本院2017年1月～2018年10月间收治的178例因乳腺肿块行手术切除患者为观察对象,纳入病灶200个.依据肿瘤深度不同分4组.对所有的患者在术前均予以常规超声检查以及超声弹性成像检查,对比不同深度肿瘤的UE成像情况、分析诊断效能(准确...     

磁共振纹理动态特征鉴别乳腺良恶性肿块

目的:提取乳腺病灶的时空变化特征作为新的DCE-MRI标记(称为纹理动态特征)并证明其鉴剐良恶性肿块的能力.方法:回顾性分析52个乳腺肿块,其中恶性肿瘤30个,良性肿块22个,提取并对动态特征信号强度特征、纹理特征、形态特征、边缘特征进行分组.为了更好评估这些特征,采用不同的特征类建立分组模型,计算正确率,敏感度,特异性及曲线下面积(AUC).结果:结合纹理动态特征所建立的良恶性肿瘤分类器具有最大的AUC=0.94,准确率90％,敏感度92％,特异性85％,优于其他各组分类器,与信号强度特征所建立的模型差异有统计学意义(AUC=0.80,P＜0.05).结论:磁共振纹理动态特征有助于鉴别良恶性肿块,甚至优于临床上最流行的DCE MRI标记信号强度动态特征.     

Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images

RATIONALE AND OBJECTIVES: Computed tomography (CT) after iodinated contrast agent injection is highly accurate for diagnosis of hepatic tumors. However, iodinating may have problems of renal toxicity and allergic reaction. We aimed to evaluate the potential role of the computer-aided diagnosis (CAD) with texture analysis in the differential of hepatic tumors on nonenhanced CT. MATERIALS AND METHODS: This study evaluated 164 liver lesions (80 malignant tumors and 84 hemangiomas). The suspicious tumor region in the digitized CT image was manually selected and extracted as a circular subimage. Proposed preprocessing adjustments for subimages were used to equalize the information needed for a differential diagnosis. The autocovariance texture features of subimage were extracted and a support vector machine classifier identified the tumor as benign or malignant. RESULTS: The accuracy of the proposed diagnosis system for classifying malignancies is 81.7%, the sensitivity is 75.0%, the specificity is 88.1%, the positive predictive value is 85.7%, and the negative predictive value is 78.7%. CONCLUSIONS: This system differentiates benign from malignant hepatic tumors with relative high accuracy and is therefore clinically useful to reduce patients needed for iodinated contrast agent injection in CT examination. Because the support vector machine is trainable, it could be further optimized if a larger set of tumor images is to be supplied.     

Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review

Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information.Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.     

肿瘤血管阻力指数和内径在乳腺良、恶性肿瘤鉴别中的应用

目的:探讨肿瘤血管阻力指数和内径对乳腺良、恶性肿瘤的鉴别诊断价值.材料和方法:应用彩色多普勒和频谱多普勒超声检测30例正常乳腺和120例乳腺肿瘤(良性66例,恶性54例),观察其血流分布,计测阻力指数(RI)和血管直径(D).结果:120例乳腺肿瘤中112例(良性60例,恶性52例)能检出血流信号,良性组与正常组相比,血管RI及D差异不显著(P＞0.05);恶性肿瘤组与正常组及良性肿瘤组比较,RI增高和D增宽(P<0.01);乳腺肿瘤动脉RI≥0.82和D≥0.28提示恶性肿瘤敏感性、特异性分别为79%、91%.结论:肿瘤血管阻力指数增高和内径增宽是乳腺良、恶性肿瘤鉴别诊断的重要指标,能提高乳腺恶性肿瘤的诊断准确性.     

Solid breast masses: neural network analysis of vascular features at three-dimensional power Doppler US for benign or malignant classification

PURPOSE: To retrospectively evaluate the accuracy of neural network analysis of tumor vascular features at three-dimensional (3D) power Doppler ultrasonography (US) for classification of breast tumors as benign or malignant, with histologic findings as the reference standard. MATERIALS AND METHODS: This study was approved by the local ethics committee; informed consent was waived. Three-dimensional power Doppler US images of 221 solid breast masses (110 benign, 111 malignant) were obtained in 221 women (mean age, 46 years; range, 25-71 years). After narrowing down vessels to skeletons with a 3D thinning algorithm, six vascular feature values--vessel-to-volume ratio, number of vascular trees, total vessel length, longest path length, number of bifurcations, and vessel diameter-were computed. A neural network was used to classify tumors by using these features. Independent-samples t test and receiver operating characteristic (ROC) curve analysis were used. RESULTS: Mean values of vessel-to-volume ratio, number of vascular trees, total vessel length, longest path length, number of bifurcations, and vessel diameter were 0.0089 +/- 0.0073 (standard deviation), 26.41 +/- 14.73, 23.02 cm +/- 19.53, 8.44 cm +/- 10.38, 36.31 +/- 37.06, and 0.088 cm +/- 0.021 in malignant tumors, respectively, and 0.0028 +/- 0.0021, 9.69 +/- 6.75, 5.17 cm +/- 4.78, 1.68 cm +/- 1.79, 6.05 +/- 7.55, and 0.064 cm +/- 0.028 in benign tumors, respectively (P < .001 for all six features). Area under ROC curve (A(z)) values of the six features were 0.84, 0.87, 0.87, 0.82, 0.84, and 0.75, respectively. Accuracy, sensitivity, specificity, and positive and negative predictive values were 85% (187 of 221), 83% (96 of 115), 86% (91 of 106), 86% (96 of 111), and 83% (91 of 110), respectively, with A(z) of 0.92 based on all six feature values. CONCLUSION: Three-dimensional power Doppler US images and neural network analysis of features can aid in classification of breast tumors as benign or malignant.