scholarly journals A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological Analyses

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Mohammad I. Daoud ◽  
Tariq M. Bdair ◽  
Mahasen Al-Najar ◽  
Rami Alazrai
Keyword(s):  
Image Classification ◽  
Texture Analysis ◽  
Morphological Analysis ◽  
Malignant Tumors ◽  
Probabilistic Approach ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Specificity And Sensitivity ◽  
Tumor Class

Ultrasound imaging is commonly used for breast cancer diagnosis, but accurate interpretation of breast ultrasound (BUS) images is often challenging and operator-dependent. Computer-aided diagnosis (CAD) systems can be employed to provide the radiologists with a second opinion to improve the diagnosis accuracy. In this study, a new CAD system is developed to enable accurate BUS image classification. In particular, an improved texture analysis is introduced, in which the tumor is divided into a set of nonoverlapping regions of interest (ROIs). Each ROI is analyzed using gray-level cooccurrence matrix features and a support vector machine classifier to estimate its tumor class indicator. The tumor class indicators of all ROIs are combined using a voting mechanism to estimate the tumor class. In addition, morphological analysis is employed to classify the tumor. A probabilistic approach is used to fuse the classification results of the multiple-ROI texture analysis and morphological analysis. The proposed approach is applied to classify 110 BUS images that include 64 benign and 46 malignant tumors. The accuracy, specificity, and sensitivity obtained using the proposed approach are 98.2%, 98.4%, and 97.8%, respectively. These results demonstrate that the proposed approach can effectively be used to differentiate benign and malignant tumors.

scholarly journals Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Magda Marcon ◽  
Alexander Ciritsis ◽  
Cristina Rossi ◽  
Anton S. Becker ◽  
Nicole Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Texture Analysis ◽  
Area Under The Curve ◽  
Texture Features ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Maximum Area ◽  
Svm Algorithm ◽  
Automated Breast Ultrasound

Abstract Background Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification. Methods This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions. Results Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii). Conclusions TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

scholarly journals Prediction and Classification into Benign and Malignant using the Clinical Testing Features

2020 ◽  
Vol 9 (10) ◽  
pp. 55-61
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Image Classification ◽  
Naive Bayes ◽  
Malignant Tumors ◽  
Naïve Bayes ◽  
Support Vector ◽  
Natural Image ◽  
Data Set ◽  
Classification Techniques

Breast Cancer is the most often identified cancer among women and a major reason for the increased mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. The advanced engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. Data mining techniques contribute a lot to the development of such a system, Classification, and data mining methods are an effective way to classify data. For the classification of benign and malignant tumors, we have used classification techniques of machine learning in which the machine learns from the past data and can predict the category of new input. This study is a relative study on the implementation of models using Support Vector Machine (SVM), and Naïve Bayes on Breast cancer Wisconsin (Original) Data Set. With respect to the results of accuracy, precision, sensitivity, specificity, error rate, and f1 score, the efficiency of each algorithm is measured and compared. Our experiments have shown that SVM is the best for predictive analysis with an accuracy of 99.28% and naïve Bayes with an accuracy of 98.56%. It is inferred from this study that SVM is the well-suited algorithm for prediction.

Local-weighted Citation-kNN algorithm for breast ultrasound image classification

Optik ◽  
2015 ◽  
Vol 126 (24) ◽  
pp. 5188-5193 ◽  
Author(s):  
Jianrui Ding ◽  
H.D. Cheng ◽  
Min Xian ◽  
Yingtao Zhang ◽  
Fei Xu
Keyword(s):  
Image Classification ◽  
Ultrasound Image ◽  
Breast Ultrasound

scholarly journals Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhemin Zhuang ◽  
Zengbiao Yang ◽  
Shuxin Zhuang ◽  
Alex Noel Joseph Raj ◽  
Ye Yuan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Breast Tumor ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Tumor Diagnosis ◽  
Support Vector ◽  
Ultrasound Images ◽  
Deep Learning Features ◽  
Characteristic Features

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.

scholarly journals Breast Ultrasound Image Classification Based on Multiple-Instance Learning

2012 ◽  
Vol 25 (5) ◽  
pp. 620-627 ◽  
Author(s):  
Jianrui Ding ◽  
H. D. Cheng ◽  
Jianhua Huang ◽  
Jiafeng Liu ◽  
Yingtao Zhang
Keyword(s):  
Image Classification ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Multiple Instance Learning

scholarly journals Classification of Breast Ultrasound Tomography by Using Textural Analysis

2020 ◽  
Vol 17 (2) ◽  
Author(s):  
Chih-Yu Liang ◽  
Tai-Been Chen ◽  
Nan-Han Lu ◽  
Yi-Chen Shen ◽  
Kuo-Ying Liu ◽  
...  
Keyword(s):  
Roc Analysis ◽  
Malignant Tumors ◽  
Rapid Development ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Image Features ◽  
Benign Tumors ◽  
Ultrasound Images ◽  
The Mean

Background: Ultrasound imaging has become one of the most widely utilized adjunct tools in breast cancer screening due to its advantages. The computer-aided detection of breast ultrasound is rapid development via significant features extracted from images. Objectives: The main aim was to identify features of breast ultrasound image that can facilitate reasonable classification of ultrasound images between malignant and benign lesions. Patients and Methods: This research was a retrospective study in which 85 cases (35 malignant [positive group] and 50 benign [negative group] with diagnostic reports) with ultrasound images were collected. The B-mode ultrasound images have manually selected regions of interest (ROI) for estimated features of an image. Then, a fractal dimensional (FD) image was generated from the original ROI by using the box-counting method. Both FD and ROI images were extracted features, including mean, standard deviation, skewness, and kurtosis. These extracted features were tested as significant by t-test, receiver operating characteristic (ROC) analysis and Kappa coefficient. Results: The statistical analysis revealed that the mean texture of images performed the best in differentiating benign versus malignant tumors. As determined by the ROC analysis, the appropriate qualitative values for the mean and the LR model were 0.85 and 0.5, respectively. The sensitivity, specificity, accuracy, positive predicted value (PPV), negative predicted value (NPV), and Kappa for the mean was 0.77, 0.84, 0.81, 0.77, 0.84, and 0.61, respectively. Conclusion: The presented method was efficient in classifying malignant and benign tumors using image textures. Future studies on breast ultrasound texture analysis could focus on investigations of edge detection, texture estimation, classification models, and image features.

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