Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector

<div><p>Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. In this paper, a new algorithm is developed for the automatic recognition of the fetal facial standard planes (FFSPs) such as the axial, coronal, and sagittal planes. Specifically, densely sampled root scale invariant feature transform (RootSIFT) features are extracted and then encoded by Fisher vector (FV). The Fisher network with multi-layer design is also developed to extract spatial information to boost the classification performance. Finally, automatic recognition of the FFSPs is implemented by support vector machine (SVM) classifier based on the stochastic dual coordinate ascent (SDCA) algorithm. Experimental results using our dataset demonstrate that the proposed method achieves an accuracy of 93.27% and a mean average precision (mAP) of 99.19% in recognizing different FFSPs. Furthermore, the comparative analyses reveal the superiority of the proposed method based on FV over the traditional methods.</p></div

Receiver operation characteristics (ROC) curves of fetal facial standard plane (FFSP) recognition.

<p>Receiver operation characteristics (ROC) curves of fetal facial standard plane (FFSP) recognition.</p

Pipeline of fetal facial standard plane (FFSP) based on Fisher vector (FV).

<p>Dense sampled patches are represented by low level descriptor first and then principle component analysis (PCA) is performed on the descriptors. Spatial layout is used to represent different partitions.</p

Top similar ultrasound images of the input sample images of (a) axial class (b) coronal class (c) sagittal class.

<p>The first one is the input query images and the remaining images are retrieved based on the similarity with the query image.</p

Effect of with and without multiple layer using bag of visual word(BoVW), vector of locally aggreaged descriptor (VLAD) and Fisher vector (FV) encoding method with liner, hell (Hel.) and chi2 kernel algorithm.

<p>Effect of with and without multiple layer using bag of visual word(BoVW), vector of locally aggreaged descriptor (VLAD) and Fisher vector (FV) encoding method with liner, hell (Hel.) and chi2 kernel algorithm.</p

Accuracy, mean average precision (mAP), training and testing time comparison of stochastic gradient decent (SGD) and stochastic dual coordinate ascent (SDCA) method in terms of bag of visual word (BoVW), vector of locally aggregated descriptor (VLAD), Fisher vector (FV) using linear, hell (Hel.) and chi2 methods.

<p>Accuracy, mean average precision (mAP), training and testing time comparison of stochastic gradient decent (SGD) and stochastic dual coordinate ascent (SDCA) method in terms of bag of visual word (BoVW), vector of locally aggregated descriptor (VLAD), Fisher vector (FV) using linear, hell (Hel.) and chi2 methods.</p

Effect of different kinds of spatial layout for multilayer network of Fisher vector (FV).

<p>Effect of different kinds of spatial layout for multilayer network of Fisher vector (FV).</p

Steps for feature vector extraction.

<p>After preprocessing and region of interest (ROI) detection, the root scale invariant feature transform (RootSIFT) is adopted for feature extraction. The extracted features are clustered by Gaussian mixture model (GMM) and then encoded by Fisher vector (FV). The final feature vectors are represented by histogram.</p

Accuracy and mean average precision (mAP) results of linear, hell (Hel.) and chi2 kernel methods with bag of visual word (BoVW), vector of locally aggregated descriptor (VLAD) and Fisher vector (FV).

<p>Accuracy and mean average precision (mAP) results of linear, hell (Hel.) and chi2 kernel methods with bag of visual word (BoVW), vector of locally aggregated descriptor (VLAD) and Fisher vector (FV).</p

Accuracy, false positive rate (FPR), false negative rate (FNR) and mean average precision (mAP) results of bag visual word (BoVW), vector of locally aggregated descriptor (VLAD), Fisher vector (FV) using linear, hell (Hel.), and chi2 method of (a) axial class (b) coronal class (c) non-fetal facial stand plane (non-FFSP) class (d) sagittal class.

<p>Accuracy, false positive rate (FPR), false negative rate (FNR) and mean average precision (mAP) results of bag visual word (BoVW), vector of locally aggregated descriptor (VLAD), Fisher vector (FV) using linear, hell (Hel.), and chi2 method of (a) axial class (b) coronal class (c) non-fetal facial stand plane (non-FFSP) class (d) sagittal class.</p