Breast ultrasound image classification using fractal analysis

2004 ◽  
Author(s):  
Ruey-Feng Chang ◽  
Chii-Jen Chen ◽  
Ming-Feng Ho ◽  
Dar-Ren Chen ◽  
Woo Kyung Moon
Keyword(s):  
Image Classification ◽  
Fractal Analysis ◽  
Ultrasound Image ◽  
Breast Ultrasound

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.

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 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 Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Bing ◽  
Wei Wang
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Ultrasound Image ◽  
Classification Problem ◽  
Relevance Vector Machine ◽  
Concentric Circle ◽  
Breast Ultrasound ◽  
Learning Problem ◽  
Novel Method ◽  
Global And Local

We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

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