作者：
Kamezawa, HidemiArimura, HidetakaTeikyo Univ
Fac Fukuoka Med Technol Dept Radiol Technol 6-22 Misaki Machi Omuta Fukuoka 8368505 Japan Kyushu Univ
Fac Med Sci Dept Hlth Sci Div Med Quantum Sci 3-1-1 Maidashi Higashi Ku Fukuoka 8128582 Japan
We investigated an approach for predicting recurrence after radiation therapy using local binary pattern (LBP)-based dosiomics in patients with head and neck squamous cell carcinoma (HNSCC). Recurrence/non-recurrence ...
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We investigated an approach for predicting recurrence after radiation therapy using local binary pattern (LBP)-based dosiomics in patients with head and neck squamous cell carcinoma (HNSCC). Recurrence/non-recurrence data were collected from 131 patients after intensity-modulated radiation therapy. The cases were divided into training (80%) and test (20%) datasets. A total of 327 dosiomics features, including cold spot volume, first-order features, and texture features, were extracted from the original dose distribution (ODD) and LBP on gross tumor volume, clinical target volume, and planning target volume. The CoxNet algorithm was employed in the training dataset for feature selection and dosiomics signature construction. Based on a dosiomics score (DS)-based Cox proportional hazard model, two recurrence prediction models (DSODD and DSLBP) were constructed using the ODD and LBP dosiomics features. These models were used to evaluate the overall adequacy of the recurrence prediction using the concordance index (CI), and the prediction performance was assessed based on the accuracy and area under the receiver operating characteristic curve (AUC). The CIs for the test dataset were 0.71 and 0.76 for DSODD and DSLBP, respectively. The accuracy and AUC for the test dataset were 0.71 and 0.76 for the DSODD model and 0.79 and 0.81 for the DSLBP model, respectively. LBP-based dosiomics models may be more accurate in predicting recurrence after radiation therapy in patients with HNSCC.
Machine Learning (ML) has been widely used for Image processing. The pertinent feature extraction and feature selection techniques can help us to accomplish many complex tasks. This paper presents a framework for the ...
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Machine Learning (ML) has been widely used for Image processing. The pertinent feature extraction and feature selection techniques can help us to accomplish many complex tasks. This paper presents a framework for the classification of emotions using ML. Training and testing have been done using the JAFFE (Japanese Female Facial Expression) dataset. The work proposes a combination of Short-Time Fourier Transform (STFT) and local binary pattern (LBP) for extracting interesting features. Also, a fusion of popular feature reduction techniques namely: Fisher Discriminant Ratio (FDR), variance threshold method and chi-square test has been introduced. The selected relevant features are applied to the Support Vector Machine (SVM) classifier. Performance analysis of the existing techniques and the proposed technique has been carried out where the latter was found efficient. The proposed pipeline performs better in terms of accuracy, specificity and sensitivity as compared to the state of art.
Nowadays, face biometric based access control systems are becoming ubiquitous in our daily life while they are still vulnerable to spoofing attacks. So developing robust and reliable methods to prevent such frauds is ...
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Nowadays, face biometric based access control systems are becoming ubiquitous in our daily life while they are still vulnerable to spoofing attacks. So developing robust and reliable methods to prevent such frauds is unavoidable. As deep learning techniques have achieved satisfactory performances in computer vision, they have also been applied to face spoofing detection. However, the numerous parameters in these deep learning based detection methods cannot be updated to optimum due to limited data. local binary pattern (LBP), effective features for face recognition, have been employed in face spoofing detection and obtained promising results. Considering the similarities between LBP extraction and convolutional neural network (CNN) that the former can be accomplished by using fixed convolutional filters, we propose a novel end-to-end learnable LBP network for face spoofing detection. Our network can significantly reduce the number of network parameters by combing learnable convolutional layers with fixed-parameter LBP layers that are comprised of sparse binary filters and derivable simulated gate functions. Compared with existing deep leaning based detection methods, the parameters in our fully connected layers are up to 64x savings. Conducting extensive experiments on two standard spoofing databases, i.e., Relay-Attack and CASIA-FA, our proposed LBP network substantially outperforms the state-of-the-art methods.
In this paper, we address a real-time object tracking algorithm considering local binary pattern (LBP) as a feature descriptor. In addition to texture feature, Ohta color features are included in the feature vector of...
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To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting ...
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To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases （the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases）. By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis （2DLDA） and local binary pattern （LBP） are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.
Facial recognition technology allows both the government and the general public to identify individuals based on their facial features, even in cases of significant changes. However, low lighting during the facial rec...
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In this chapter, local binary pattern (LBP) based extreme learning machine (ELM) is presented for identification of high-dimensional face images of different resolution. In this scheme, LBP is used for the representat...
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Recently, the use of artificial intelligence to improve the efficiency of Covid-19 diagnosis has become a trend due to the spread and proliferation of Covid-19 and the fact that healthcare professionals alone are no l...
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This paper presents a new feature extraction method in dual-tree complex wavelet transform domain. Given an input image, we obtain all highpass directional subimages and a set of pyramid lowpass subimages with differe...
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This paper presents a new feature extraction method in dual-tree complex wavelet transform domain. Given an input image, we obtain all highpass directional subimages and a set of pyramid lowpass subimages with different resolutions by applying DTCWT decomposition. After that, generalized Gamma density models and local binary pattern are utilized respectively to characterize features of both highpass and lowpass subimages. The two kinds of features are combined for texture classification, and the experimental results on datasets Brodatz, Outex and UMD demonstrate that our proposed method can achieve superior classification accuracy than other state-of-the-art methods.
local binary pattern (LBP) is one of the best descriptors of texture images;however, it elicits information from the pixels' value over each locality and therefore its value is highly sensitive to additive noise. ...
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local binary pattern (LBP) is one of the best descriptors of texture images;however, it elicits information from the pixels' value over each locality and therefore its value is highly sensitive to additive noise. In this research, a robust-to-noise LBP version is proposed, termed Radial Mean local binary pattern (RMLBP), to enhance the quality of extracted features in noisy images. The main trick of RMLBP is to define the mean of points over each radial instead of using angular neighbor points (over a circle). This changing strategy enables RMLBP to extract robust features by removing the effect of noisy neighbors over each radial local patch. To make a fair comparison, the proposed method along with known mean filters, including circular and square mean, were applied to noisy textures. Applying RMLBP and the compared LBP variants to the Outex, CUReT and UIUC datasets demonstrated a significant superiority of the proposed method to its counterparts.
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