Naoki Matsushiro

Quantitative assessment of facial paralysis using local binary patterns and Gabor filters

Facial paralysis is a common clinical condition with the rate from 20 to 25 patients per 100,000 people per year. An objectively quantitative tool to support for medical diagnostics is very necessary and important. This paper proposes a very robust method that overcomes the drawbacks of other techniques to develop this tool. In our research, we use a combination of local binary patterns (LBP) and Gabor filters to calculate the features that are used for training and testing. Experiments show that our results outperform other techniques testing on a dynamic facial expression database.

Quantitative analysis of facial paralysis based on three-dimensional features

Objective evaluation of disease is one of the desirable goals in medicine. This paper presents a technique for the objective evaluation of facial paralysis, in which features are extracted based on landmarks positions in three-dimensional space (3D-landmarks). The landmarks are initialized manually in the first frontal frame and are tracked in the subsequent frontal frames. Then, the landmarks positions are reconstructed in 3D-space using multiview images and a camera self-calibration technique. From the 3D-landmarks, the features are extracted in 3D-space (called 3D-features) and used for classification. These 3D-features may contain enhanced information such as depth information and, therefore, may help improve the accuracy rates of predicted scores. In addition, our method uses the camera self-calibration technique for estimating the cameras parameters, and does not use laser scanning for 3D reconstruction, so it is more flexible to set up and safer for the patient. For overall evaluation, experiments showed that our technique achieved superior results to other methods.

Semi-automatic Segmentation of Paranasal Sinuses from CT Images Using Active Contour with Group Similarity Constraints

Computerized tomographic (CT) scanning has dramatically improved the imaging of paranasal sinus anatomy as compared to sinus radiographs. Increasingly, subtle bony anatomic variations and mucosal abnormalities of this region are being detected. The morphological knowledge of nasal cavity and paranasal sinuses has an important clinical value. It is used for the detection of sinus pathologies, for determination of therapy, planning of endoscopic surgeries and for surgical simulations. Current research and industry assisting systems need a workspace definition of the paranasal sinuses, which is realized by segmentation. This paper presents a semi-automatic segmentation method for the paranasal sinuses which allows us to locate structures. In general, the traditional active contour methods like Snake, Levelset can resolve the CT images of paranasal sinuses normal without any anatomic variations caused by sinusitis. However, in the clinical practice, the diseased radiological image has more significances so that these classical methods can not work satisfied very well as the boundaries of sinuses has been covered by impurity inflammation produced. At this point, we proposed a novel method group similarity based on Low Rank to repair the lost part of the boundary. The experiment results proved that our proposed method outperformed conventional algorithms especially in abnormal images.

Quantitative analysis of facial paralysis based on filters of concentric modulation

Facial paralysis is a common disease occurring with annual patient rate of 25 to 35/100 000. The symptom of the disease is that the patients loose or decrease facial movement ability. It is useful if there is an effective method to objective evaluation of facial paralysis degree. This paper presents a method to develop this tool based on filtered images. In our work, we propose a filter to extract useful isotropic frequencies of images in local spaces and remove unnecessary frequency components before feature extraction. The filter function is the modulation of an isotropic Gaussian function by a radial sinusoidal function. The interesting characteristic of this filter is that the passbands are the same for all orientations. This may be useful in some cases such as quantifying the degree of facial palsy. In this work, the measurement of symmetry and asymmetry between two sides of the face is performed on filtered images, and then the measured information is used for classification. Experiments have shown that with the use of our filtered technique, it gives superior results than the other methods testing on an available database of Osaka Police Hospital.

Automatic feature point detection using deep convolutional networks for quantitative evaluation of facial paralysis

Feature point detection is an important pre-processing step for quantitative evaluation of facial paralysis. Since the conventional methods such as active shape model (ASM) or active appearance model (AAM) are trained by using normal face and they are not possible to detect the feature points accurately for the face with paralysis. In this paper, we propose an automatic and accurate feature point detection method for quantitative evaluation of facial paralysis using deep convolutional neural networks (DCNN). The proposed method consists of two steps. We first use AAM for initial feature point detection. In the second step, a patch with the detected point at the center is used as an input of DCNN for refinement. Experiments demonstrated that the proposed method can significantly improve the detection accuracy of the conventional AAM.

Quantitative analysis of facial paralysis based on limited-orientation modified circular Gabor filters

The diagnosis of disease with the aid of computer programs has been developing more and more in recent years. This paper presents an approach which is based on frequency technique for the objective quantitative analysis of facial paralysis. In this method, limited-orientation modified circular Gabor filters (LO-MCGFs) are used to enhance the desirable frequencies in images. Then, features are extracted from the filtered images for classification. The first advantage of the LO-MCGF is that its inner passbands are uniform, so it helps remove noise and control frequencies more effectively. The second benefit is that the LO-MCGF utilizes the existing robust characteristics of circular Gabor filter for rotation invariant texture regions. Hence, the LO-MCGF-based technique improves remarkably the accuracies of score estimation for some expressions whose local textures are invariant in rotation. Finally, the limited filtered regions, or limited propagation orientations, help the LO-MCGF focus on only some specific spaces. Therefore, the LO-MCGF can avoid the influences of irrelevant regions. In other words, it improves the spatial localization. For overall evaluation, experiments show that our proposed method is superior to other contemporary techniques tested on a dynamic facial expression database.

Quantitative evaluation of facial paralysis using tracking method

Facial paralysis is a common clinical condition with the rate from 20 to 25 patients per 100,000 people per year. An objectively quantitative tool to support for medical diagnostics is very necessary and important. This paper proposes a very simple, visual, and highly efficient method that overcomes the drawbacks of other methods to develop this tool. In our research, we use the tracking of interest points to measure the features that are used for training and testing. Experiments show that our method outperforms other techniques testing on a dynamic facial expression database.

Facial paralysis modeling based on image morphing

Facial Paralysis is a serious disease that involves the paralysis of any structures innervated by the facial nerve and requires immediate treatment. Yanagihara method is a popular method for the measurement of facial paralysis in Japan. In this method, the patient is asked to make 10 different expressions and the doctor applies the score to each expression. Typical sample expression facial images for different scores are important for educations and references. Since it is not possible to use patients images as typical sample images, we propose a facial paralysis modeling method based on image warping to generate a virtual expression image of facial paralysis as same as real expression of facial paralysis. We transform the movement or expression of the patient to an average facial image by using both B-spline warping and piece-wise affine transform.