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

Wu, W.; Lin, Shih-Wei; Moon, Woo Kyung

doi:10.1007/s10278-014-9757-1

Cited by 28 publications

(14 citation statements)

References 42 publications

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“…GLCM (9, 10), wavelet, shearlet and curvelet (10), contourlet (10), auto-covariance matrix (12) and a combination of texture features (10,24). Results of the current study have also indicated that RLM texture analysis possesses significantly more discriminative ability than other methods, such as morphology and elastography (9,12,(20)(21)(22). Likewise, the proposed method has demonstrated a more reliable performance in comparison with previous studies that combined texture and morphological features (9,12).…”

supporting

confidence: 53%

“…Wu et al (12) has combined texture and morphological features from ultrasound images and indicated that combined features gained better for classifying breast tumors with a sensitivity, specificity and accuracy of 96.67%, PPV of 95.6%, NPV of 97.48%, and A z of 0.9827. In a Moon et al study (9), how-…”

Section: Discussionmentioning

confidence: 99%

“…Moon et al (9) Moon has shown that combining shape and ellipsoid fitting features achieved a performance in which sensitivity, specificity, accuracy, PPV, NPV, and A z were 84.51%, 85.53%, 85.03%, 84.51%, 85.53%, and 0.9466, respectively. Wu et al (12) has extracted morphological features from ultrasound images. They have used a support vector machine to classify breast tumors with a sensitivity of 88.89%, specificity of 92.5%, accuracy of 90.95%, PPV of 89.89%, NPV of 91.47%, and A z of 0.9389.…”

Section: Discussionmentioning

confidence: 99%

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Diagnosis of Breast Tumors with Sonographic Texture Analysis Using Run-length Matrix

et al. 2017

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Background: Early detection and reliable diagnosis of breast cancer could lead to improved cure rates and reduce mortality and management costs. Objectives: To explore the potential of texture analysis based on run-length matrix features for classifying benign and malignant breast tumors in ultrasound imaging. Methods: A total of 70 breast tumors (38 benign and 32 malignant) have used in the proposed computer-aided diagnosis system. Twenty run-length matrix features have extracted for texture analysis in three normalizations (default, 3sigma, and 1% -99%). Linear discriminant analysis and principal component analysis have employed to transform raw data to lower-dimensional spaces and increase discriminative power. The features have classified by the first nearest neighbor classifier. Results:The features under 3sigma normalization have designed via Linear discriminant analysis indicated high performance in classifying benign and malignant breast tumors with a sensitivity of 96.87%, specificity of 100%, accuracy of 98.57%, positive predictive value of 100%, and negative predictive value of 97.43%. The area under receiver operating characteristic curve was 0.992. Conclusions: Run-length matrix features had a high potential to characterize and could help radiologist to diagnosis breast tumors.

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supporting

confidence: 53%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Diagnosis of Breast Tumors with Sonographic Texture Analysis Using Run-length Matrix

et al. 2017

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“…Numerous new methods of Quantitative Ultrasound (QUS) dedicated to the estimation of the structural changes in tissue are being developed (Mamou, Oelze, 2013; Wu et al, 2015). Particularly, analysis of statistical properties of the backscattered radiofrequency (RF) signals have been successfully applied to differentiate healthy tissue from tissue regions changed pathologically (Nowicki et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Measurement of Temperature Rise in Breast Cyst and in Neighbouring Tissues as a Method of Tissue Differentiation

Gambin

Byra

Kruglenko

et al. 2016

Archives of Acoustics

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Texture of ultrasound images contain information about the properties of examined tissues. The analysis of statistical properties of backscattered ultrasonic echoes has been recently successfully applied to differentiate healthy breast tissue from the benign and malignant lesions. We propose a novel procedure of tissue characterization based on acquiring backscattered echoes from the heated breast. We have proved that the temperature increase inside the breast modifies the intensity, spectrum of the backscattered signals and the probability density function of envelope samples. We discuss the differences in probability density functions in two types of tissue regions, e.g. cysts and the surrounding glandular tissue regions. Independently, Pennes bioheat equation in heterogeneous breast tissue was used to describe the heating process. We applied the finite element method to solve this equation. Results have been compared with the ultrasonic predictions of the temperature distribution. The results confirm the possibility of distinguishing the differences in thermal and acoustical properties of breast cyst and surrounding glandular tissues.

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“…The classifier is a tool that provides as output the risk degree related to the tissue. Classifiers like support vector machine (SVM) [5,6,12,13], decision tree [5], neural network [5,11], and linear discriminant analysis (LDA) [5,14] have been widely used and performed well. Wu et al [6] segmented the breast tumor by level set method, and the auto-covariance texture features and morphologic features were extracted, then they used the genetic algorithm to detect the significant features and identified the tumors by SVM, which get the accuracy of 95.24 %.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Dong

Lǚ

et al. 2015

J Digit Imaging

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Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew's correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73 %, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.

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

Cited by 28 publications

References 42 publications

Diagnosis of Breast Tumors with Sonographic Texture Analysis Using Run-length Matrix

Diagnosis of Breast Tumors with Sonographic Texture Analysis Using Run-length Matrix

Ultrasonic Measurement of Temperature Rise in Breast Cyst and in Neighbouring Tissues as a Method of Tissue Differentiation

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

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