Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images

To promote the classification accuracy and decrease the time of extracting features and finding (near) optimal classification model of an ultrasound breast tumor image computer-aided diagnosis system, we propose an approach which simultaneously combines feature selection and parameter setting in this study.In our approach ultrasound breast tumors were segmented automatically by a level set method. The auto-covariance texture features and morphologic features were first extracted following the use of a genetic algorithm to detect significant features and determine the near-optimal parameters for the support vector machine (SVM) to identify the tumor as benign or malignant.The proposed CAD system can differentiate benign from malignant breast tumors with high accuracy and short feature extraction time. According to the experimental results, the accuracy of the proposed CAD system for classifying breast tumors is 95.24% and the computing time of the proposed system for calculating features of all breast tumor images is only 8% of that of a system without feature selection. Furthermore, the time of finding (near) optimal classification model is significantly than that of grid search. It is therefore clinically useful in reducing the number of biopsies of benign lesions and offers a second reading to assist inexperienced physicians in avoiding misdiagnosis.     

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 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.     

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.     

Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.

PURPOSE: To increase the capabilities of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors by using a neural network. MATERIALS AND METHODS: One hundred forty US images of solid breast nodules were evaluated. When a sonogram was obtained, an analog video signal from the VCR output of the scanner was transmitted to a notebook computer. A frame grabber connected to the printer port of the computer was then used to digitize the data. The suspicious tumor region on the digitized US image was manually selected. The texture information of the subimage was extracted, and a neural network classifier with autocorrelation features was used to classify the tumor as benign or malignant. In this experiment, 140 pathologically proved tumors (52 malignant and 88 benign tumors) were sampled with k-fold cross-validation (k = 10) to evaluate the performance with receiver operating characteristic curves. RESULTS: The accuracy of neural networks for classifying malignancies was 95.0% (133 of 140 tumors), the sensitivity was 98% (51 of 52), the specificity was 93% (82 of 88), the positive predictive value was 89% (51 of 57), and the negative predictive value was 99% (82 of 83). CONCLUSION: This system differentiated solid breast nodules with relatively high accuracy and helped inexperienced operators to avoid misdiagnoses. Because the neural network is trainable, it could be optimized if a larger set of tumor images is supplied.     

基于二维超声图像的纹理分析在桥本甲状腺炎背景下甲状腺结节良、恶性鉴别中的价值

目的:探讨基于二维超声图像的纹理分析对桥本甲状腺炎背景下甲状腺结节良、恶性的鉴别诊断价值.方法:回顾性分析2018年2月-8月在本院经病理证实的合并桥本甲状腺炎的甲状腺结节的二维超声图像.根据病理结果将甲状腺结节分为良性组和恶性组.采用ITK-SNAPE软件在甲状腺结节的二维超声图像上手工勾画兴趣区,通过python的...     

Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks

PURPOSE: To develop a computer-aided diagnosis (CAD) algorithm with setting-independent features and artificial neural networks to differentiate benign from malignant breast lesions. MATERIALS AND METHODS: Two sets of breast sonograms were evaluated. The first set contained 160 lesions and was stored directly on the magnetic optic disks from the ultrasonographic (US) system. Four different boundaries were delineated by four persons for each lesion in the first set. The second set comprised 111 lesions that were extracted from the hard-copy images. Seven morphologic features were used, five of which were newly developed. A multilayer feed-forward neural network was used as the classifier. Reliability, extendability, and robustness of the proposed CAD algorithm were evaluated. Results with the proposed algorithm were compared with those with two previous CAD algorithms. All performance comparisons were based on paired-samples t tests. RESULTS: The area under the receiver operating characteristic curve (A(z)) was 0.952 +/- 0.014 for the first set, 0.982 +/- 0.004 for the first set as the training set and the second set as the prediction set, 0.954 +/- 0.016 for the second set as the training set and the first set as the prediction set, and 0.950 +/- 0.005 for all 271 lesions. At the 5% significance level, the performance of the proposed CAD algorithm was shown to be extendible from one set of US images to the other set and robust for both small and large sample sizes. Moreover, the proposed CAD algorithm was shown to outperform the two previous CAD algorithms in terms of the A(z) value. CONCLUSION: The proposed CAD algorithm could effectively and reliably differentiate benign and malignant lesions. The proposed morphologic features were nearly setting independent and could tolerate reasonable variation in boundary delineation.     

Support vector machines for diagnosis of breast tumors on US images

RATIONALE AND OBJECTIVES: Breast cancer has become the leading cause of cancer deaths among women in developed countries. To decrease the related mortality, disease must be treated as early as possible, but it is hard to detect and diagnose tumors at an early stage. A well-designed computer-aided diagnostic system can help physicians avoid misdiagnosis and avoid unnecessary biopsy without missing cancers. In this study, the authors tested one such system to determine its effectiveness. MATERIALS AND METHODS: Many computer-aided diagnostic systems for ultrasonography are based on the neural network model and classify breast tumors according to texture features. The authors tested a refinement of this model, an advanced support vector machine (SVM), in 250 cases of pathologically proved breast tumors (140 benign and 110 malignant), and compared its performance with that of a multilayer propagation neural network. RESULTS: The accuracy of the SVM for classifying malignancies was 85.6% (214 of 250); the sensitivity, 95.45% (105 of 110); the specificity, 77.86% (109 of 140); the positive predictive value, 77.21% (105 of 136); and the negative predictive value, 95.61% (109 of 114). CONCLUSION: The SVM proved helpful in the imaging diagnosis of breast cancer. The classification ability of the SVM is nearly equal to that of the neural network model, and the SVM has a much shorter training time (1 vs 189 seconds). Given the increasing size and complexity of data sets, the SVM is therefore preferable for computer-aided diagnosis.     

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.     

Computer-aided diagnosis of breast tumors with different US systems

RATIONALE AND OBJECTIVES: The authors performed this study to determine whether a computer-aided diagnostic (CAD) system was suitable from one ultrasound (US) unit to another after parameters were adjusted by using intelligent selection algorithms. MATERIALS AND METHODS: The authors used texture analysis and data mining with a decision tree model to classify breast tumors with different US systems. The databases of training cases from one unit and testing cases from another were collected from different countries. Regions of interest on US scans and co-variance texture parameters were used in the diagnosis system. Proposed adjustment schemes for different US systems were used to transform the information needed for a differential diagnosis. RESULTS: Comparison of the diagnostic system with and without adjustment, respectively, yielded the following results: accuracy, 89.9% and 82.2%; sensitivity, 94.6% and 92.2%; specificity, 85.4% and 72.3%; positive predictive value, 86.5% and 76.8%; and negative predictive value, 94.1% and 90.4%. The improvement in accuracy, specificity, and positive predictive value was statistically significant. Diagnostic performance was improved after the adjustment. CONCLUSION: After parameters were adjusted by using intelligent selection algorithms, the performance of the proposed CAD system was better both with the same and with different systems. Different resolutions, different setting conditions, and different scanner ages are no longer obstacles to the application of such a CAD system.     

乳腺肿块的热断层成像分析

目的:探讨乳腺肿块在热断层成像系统(TTM)中热源的特点。方法:将乳腺肿块106例病人进行TTM检查,与病理结果对照,从乳腺异常热源的形态、结构、深度及热辐射值来分析良、恶性病变在TTM上的表现。结果:病理结果恶性病变49例,良性病变57例,TTM在乳腺良、恶性肿瘤诊断与病理诊断的符合率为89.5%和91.9%,良、恶性肿瘤热辐射值分别为1.822和2.599(P<0.001),深度在良、恶性肿瘤中也有明显差异。结论:恶性病变的形态多不规则,结构密实,热辐射值较高,良性病变则相反;TTM在乳腺良、恶性肿瘤的鉴别诊断中有重要的价值。     

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.     

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.     

Duodenal neoplasms: predictive value of CT for determining malignancy and tumor resectability.

CT examinations of 25 patients with proved primary or metastatic duodenal neoplasms were retrospectively reviewed to determine if morphologic features seen on CT scans could be used to predict the benign or malignant nature of these neoplasms and to assess the effectiveness of using CT findings to predict tumor resectability. We studied 19 malignant and six benign tumors. Histologic proof was obtained by means of surgery in 20 patients and by endoscopic biopsy in five. CT features of tumor morphology were assessed in the 22 cases in which a duodenal tumor was seen on CT. These features included central necrosis, ulceration or excavation, and the location of the tumor with respect to the bowel wall. The specific morphologic features used to predict that a tumor was malignant included the presence of an exophytic or intramural mass, central necrosis, and ulceration. The only criterion used to predict that a tumor was benign was that the mass be entirely intraluminal. Whenever vascular encasement, invasion of contiguous organs other than the head of the pancreas, distant lymphadenopathy, or metastases were present, the tumor was predicted to be unresectable for cure. With the exception of three benign smooth muscle tumors, all tumors with one or more CT morphologic features indicative of a malignant neoplasm were malignant (n = 16). Three of four intraluminal masses were benign. In three cases of polypoid tumors smaller than 2 cm, a duodenal tumor was not seen on CT. Whenever extraduodenal disease was found (15 cases), the neoplasms were malignant. In the 22 cases in which a tumor was detected on CT, the sensitivity of using the presence of one or more morphologic features associated with a malignant neoplasm as a predictor was 94%; the specificity was 50%, and the accuracy was 82%. If the presence of any morphologic feature indicative of a malignant neoplasm was combined with the presence of any finding of extraduodenal disease, CT was 100% sensitive and 86% accurate for predicting that the tumor was malignant. CT appears to be reliable for predicting duodenal tumor resectability. On the basis of CT findings, 10 tumors were correctly predicted as being unresectable for cure, and 12 were predicted as being resectable; no surgery was performed in the remaining three cases. In conclusion, evaluation of the morphologic features of duodenal neoplasms is a sensitive, but nonspecific, method for predicting that a tumor is malignant.(ABSTRACT TRUNCATED AT 400 WORDS)     

乳腺纤维结构改变对乳腺恶性肿瘤诊断价值

目的:探讨乳腺内纤维结构改变对判断乳腺恶性肿瘤的价值。方法:乳腺肿瘤64例(良性21例,恶性43例),术前或活检前高频超声检查,记录肿物周围纤维结构的声像图改变,初步判断肿物良、恶性质并按有无浸润和浸润深度分级,共两类各4级,术后将病理结果与术前的超声分级做对比分析。结果:恶性肿瘤中浅、深筋膜和cooper韧带发生改变的几率明显高于良性肿瘤,分别为64%,26%(P<0.01),诊断价值的大小依次为:浅、深膜受侵>cooper韧带。结论:高频超声对观察乳腺内纤维结构改变对恶性肿瘤诊断有重要提示价值。     

Apparent diffusion coefficient mapping of salivary gland tumors: prediction of the benignancy and malignancy

BACKGROUND AND PURPOSE: Preoperative prediction of tumor malignancy is clinically very important, because this information strongly influences the surgical plan. We evaluate the preoperative apparent diffusion coefficient (ADC) maps of benign and malignant salivary gland tumors. MATERIALS AND METHODS: High-resolution MR imaging was performed on 31 patients with benign or malignant salivary gland tumors; ADC maps of the tumors were also obtained. Surface coils of 47 or 110 mm diameter were used to improve the image resolution. The ADCs were compared with histologic features of the excised tumors. RESULTS: The ADC maps effectively depicted the histologic features of the salivary gland tumors, such as presence of cancer cells, myxomatous tissues, fibrosis, necrosis, cyst formation, and lymphoid tissues. The ADC maps showed that more frequent areas with high ADCs (> or = 1.8 x 10(-3) mm(2)/s) were significantly greater in benign tumors than in malignant tumors. The sensitivity and specificity for high ADC occupying fewer than 5% of the area of a tumor was 89% and 100%, respectively, resulting in 97% accuracy, 100% positive predictive value, and 96% negative predictive value. CONCLUSION: The ADC may provide preoperative tissue characterization of the salivary gland tumors.     

Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system

PURPOSE: To develop and test a computer-aided diagnosis (CAD) system to improve the performance of radiologists in classifying lesions on breast MRI (BMRI). MATERIALS AND METHODS: A CAD system was developed that uses a semiautomated segmentation method. After segmentation, 42 features based on lesion shape, texture, and enhancement kinetics were computed, and the 13 best features were selected and used as inputs to a backpropagation neural network (BNN). The BNN was trained and tested using the leave-one-out method on 80 BMRI lesions (37 benign, 43 malignant). Lesion histopathology was used as the reference standard. Five human readers classified the 80 lesions first without and then with CAD assistance. The performance of the computer classifier and the human readers was assessed using receiver operating characteristic curves; the performance of the human readers was also evaluated using multireader multicase (MRMC) analysis. RESULTS: The performance of the human readers significantly improved when aided by the CAD system (P < 0.05). MRMC analysis showed that human reader performance with and without CAD system assistance can be generalized to the population of cases (P < 0.001). CONCLUSION: A CAD system based on lesion morphology and enhancement kinetics can improve the performance of human readers in classifying lesions on breast MRI.     

基于乳腺X线图像纹理特征建立机器学习模型在鉴别良恶性乳腺肿块中的价值

目的:探讨基于乳腺X线图像的纹理分析建立机器学习模型在鉴别乳腺肿块良恶性中的价值。方法:回顾性搜集经病理证实的124个乳腺良性肿块和139个乳腺恶性肿块的乳腺X线图像。并按照7﹕3的比例划将所有病灶随即分为训练集和验证集。使用MaZda软件,在X线图像上于乳腺病灶内手动勾画ROI,提取6类共133个纹理特征,经降维处理后,利用训练集数据得到线性判别分析(LDA)、Logistic回归(LR)、随机森林(RF)和支持向量机(SVM)共4种模型。在验证集中对这4种模型进行验证。通过符合率、Kappa系数和AUC值分别评价4种模型在训练集和验证集中的表现,并通过delong法比较4种模型间AUC值的差异。结果:RF模型在训练集和验证集中符合率、Kappa系数和AUC值均高于其它模型;其中,RF模型在验证集中的符合率为94.9%、Kappa系数为0.896、AUC值为0.946,与LDA模型、LR模型间AUC值的差异均具有统计学意义(P<0.05)。SVM模型的符合率和Kappa系数仅次于RF模型;在验证集中,SVM模型的AUC值高于LDA和LR模型,但差异无统计学意义(P>0.05)。结论:基于乳腺X线图像纹理特征建立的机器学习模型在鉴别乳腺肿块良恶性中具有一定优势。其中RF模型表现出较好的诊断效能,SVM模型的表现仅次于RF模型。