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Robust phase-based texture descriptor for classification of breast ultrasound images.

Cai L, Wang X, Wang Y, Guo Y, Yu J, Wang Y - Biomed Eng Online (2015)

Bottom Line: Then, in order to verify the robustness of the PCBP against illumination variations, we train the SVM classifier on texture features obtained from the original BUS images, and use this classifier to deal with the texture features extracted from BUS images with different illumination conditions (i.e., contrast-improved, gamma-corrected and histogram-equalized).The area under ROC curve (AUC) index is used as the figure of merit to evaluate the classification performances.The proposed PCBP texture descriptor achieves the highest values (i.e. 0.894) and the least variations in respect of the AUC index, regardless of the gray-scale variations.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. 12210720095@fudan.edu.cn.

ABSTRACT

Background: Classification of breast ultrasound (BUS) images is an important step in the computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel phase-based texture descriptor is proposed for efficient and robust classifiers to discriminate benign and malignant tumors in BUS images.

Method: The proposed descriptor, namely the phased congruency-based binary pattern (PCBP) is an oriented local texture descriptor that combines the phase congruency (PC) approach with the local binary pattern (LBP). The support vector machine (SVM) is further applied for the tumor classification. To verify the efficiency of the proposed PCBP texture descriptor, we compare the PCBP with other three state-of-art texture descriptors, and experiments are carried out on a BUS image database including 138 cases. The receiver operating characteristic (ROC) analysis is firstly performed and seven criteria are utilized to evaluate the classification performance using different texture descriptors. Then, in order to verify the robustness of the PCBP against illumination variations, we train the SVM classifier on texture features obtained from the original BUS images, and use this classifier to deal with the texture features extracted from BUS images with different illumination conditions (i.e., contrast-improved, gamma-corrected and histogram-equalized). The area under ROC curve (AUC) index is used as the figure of merit to evaluate the classification performances.

Results and conclusions: The proposed PCBP texture descriptor achieves the highest values (i.e. 0.894) and the least variations in respect of the AUC index, regardless of the gray-scale variations. It's revealed in the experimental results that classifications of BUS images with the proposed PCBP texture descriptor are efficient and robust, which may be potentially useful for breast ultrasound CADs.

No MeSH data available.


Related in: MedlinePlus

The performance evaluation (AUC values) of the cross-contrast training/testing scheme with the LOO-CV method.
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Fig8: The performance evaluation (AUC values) of the cross-contrast training/testing scheme with the LOO-CV method.

Mentions: Afterwards, texture features are extracted from the CI, GC and HE databases, respectively. A cross-contrast training/testing scheme is then employed for performing the classification. In this scheme, the training phase is carried out on the original BUS image database while the testing phase is performed on the gray-scale transformed databases (i.e., CI, GC and HE respectively), excluded in the training phase. The AUC index is used as the figure of merit for evaluation. Experiments are firstly conducted using LOO-CV method, and the results are illustrated in Figure 8. in form of bar plot. It is noted that AUC values presented in last subsection (i.e., in Table 1 and Table 3) are also included as Origin, and AUC values related to the Origin, CI, GC, HE databases are expressed as {Origin, CI, GC, HE} for simplicity.Figure 8


Robust phase-based texture descriptor for classification of breast ultrasound images.

Cai L, Wang X, Wang Y, Guo Y, Yu J, Wang Y - Biomed Eng Online (2015)

The performance evaluation (AUC values) of the cross-contrast training/testing scheme with the LOO-CV method.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4376500&req=5

Fig8: The performance evaluation (AUC values) of the cross-contrast training/testing scheme with the LOO-CV method.
Mentions: Afterwards, texture features are extracted from the CI, GC and HE databases, respectively. A cross-contrast training/testing scheme is then employed for performing the classification. In this scheme, the training phase is carried out on the original BUS image database while the testing phase is performed on the gray-scale transformed databases (i.e., CI, GC and HE respectively), excluded in the training phase. The AUC index is used as the figure of merit for evaluation. Experiments are firstly conducted using LOO-CV method, and the results are illustrated in Figure 8. in form of bar plot. It is noted that AUC values presented in last subsection (i.e., in Table 1 and Table 3) are also included as Origin, and AUC values related to the Origin, CI, GC, HE databases are expressed as {Origin, CI, GC, HE} for simplicity.Figure 8

Bottom Line: Then, in order to verify the robustness of the PCBP against illumination variations, we train the SVM classifier on texture features obtained from the original BUS images, and use this classifier to deal with the texture features extracted from BUS images with different illumination conditions (i.e., contrast-improved, gamma-corrected and histogram-equalized).The area under ROC curve (AUC) index is used as the figure of merit to evaluate the classification performances.The proposed PCBP texture descriptor achieves the highest values (i.e. 0.894) and the least variations in respect of the AUC index, regardless of the gray-scale variations.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. 12210720095@fudan.edu.cn.

ABSTRACT

Background: Classification of breast ultrasound (BUS) images is an important step in the computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel phase-based texture descriptor is proposed for efficient and robust classifiers to discriminate benign and malignant tumors in BUS images.

Method: The proposed descriptor, namely the phased congruency-based binary pattern (PCBP) is an oriented local texture descriptor that combines the phase congruency (PC) approach with the local binary pattern (LBP). The support vector machine (SVM) is further applied for the tumor classification. To verify the efficiency of the proposed PCBP texture descriptor, we compare the PCBP with other three state-of-art texture descriptors, and experiments are carried out on a BUS image database including 138 cases. The receiver operating characteristic (ROC) analysis is firstly performed and seven criteria are utilized to evaluate the classification performance using different texture descriptors. Then, in order to verify the robustness of the PCBP against illumination variations, we train the SVM classifier on texture features obtained from the original BUS images, and use this classifier to deal with the texture features extracted from BUS images with different illumination conditions (i.e., contrast-improved, gamma-corrected and histogram-equalized). The area under ROC curve (AUC) index is used as the figure of merit to evaluate the classification performances.

Results and conclusions: The proposed PCBP texture descriptor achieves the highest values (i.e. 0.894) and the least variations in respect of the AUC index, regardless of the gray-scale variations. It's revealed in the experimental results that classifications of BUS images with the proposed PCBP texture descriptor are efficient and robust, which may be potentially useful for breast ultrasound CADs.

No MeSH data available.


Related in: MedlinePlus