Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines

Huang, Yu-Len; Wang, Kao-Lun; Chen, Dar‐Ren

doi:10.1007/s00521-005-0019-5

Cited by 103 publications

(60 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since different tissues have different textures, textural features provide useful information for classifying breast tumors as benign or malignant. The autocovariance matrix can specify the inter-pixel relationships in an image and it has been used to classify breast tumors [8,21,22]. In the method that was proposed by Chang et al [22], the modified auto-covariance coefficients inside a tumor are defined as follows.…”

Section: Textural Featuresmentioning

confidence: 99%

“…Kuo et al [19] combined the AIS algorithm with a fuzzy neural network (FNN) to increase the accuracy of an RFID-based positioning system. This work develops an efficient SVM [20][21][22][23][24][25] method that is based on the AIS algorithm (AISSVM) for diagnosing ultrasound images of breast tumors. The proposed CAD system simultaneously performs parameter tuning and feature selection, and thus achieves high classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Wu¹,

Lin²,

Moon

2015

J Digit Imaging

View full text Add to dashboard Cite

A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96. 67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A z is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.

show abstract

Section: Textural Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Wu¹,

Lin²,

Moon

2015

J Digit Imaging

View full text Add to dashboard Cite

show abstract

“…These mathematical descriptors can be morphological features (based on the shape or contour of the lesion) or textural features (based on intensity distribution) [30]. Both these morphological as well as textural features are significant for developing CAD systems for breast lesions from B-mode US [31][32][33][34][35][36][37][38]. Experienced participating radiologists were of the view that morphological features does not give any significant information for characterization of FLLs from B-mode US and the same is also evident from other related researches, the proposed CAD systems for characterization of FLLs from B-mode US have relied on textural features only [15, 16, 21-23, 28, 29].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

et al. 2014

View full text Add to dashboard Cite

A neural network ensemble (NNE) based computeraided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.

show abstract

“…Ultrasound examination, which is noninvasive and nonradioactive, is more convenient and suitable for palpable tumors in daily clinical practice [3]. Differential diagnosis of breast lesions can be acquired from ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

“…Different tissues have different textures; therefore, the texture of BUS image is an effective feature for differentiating benign and malignant breast tumors [3,8]. Auto-covariance [9], fractal dimension [10], co-occurrence matrix [11], runlength matrix [12], and wavelet coefficients [13] have been widely utilized to derive discriminant features.…”

Section: Introductionmentioning

confidence: 99%

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

et al. 2012

View full text Add to dashboard Cite

Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p<0.005).

show abstract

Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines

Cited by 103 publications

References 16 publications

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

Contact Info

Product

Resources

About