• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Brain Tumor Detection and Segmentation Using RCNN

    2022-08-23 02:17:58MahamKhanSyedAdnanShahTenvirAliQuratulainAymenKhanandGyuSangChoi
    Computers Materials&Continua 2022年6期

    Maham Khan,Syed Adnan Shah,Tenvir Ali, Quratulain,Aymen Khan and Gyu Sang Choi

    1Department of Computer Science,University of Engineering&Technology,Taxila,47080,Pakistan

    2Faculty of Computing,The Islamia University of Bahawalpur,Bahawalpur,63100,Pakistan

    3Department of Information and Communication Engineering,Yeungnam University,Gyeongbuk,38541,Korea

    Abstract: Brain tumors are considered as most fatal cancers.To reduce the risk of death, early identification of the disease is required.One of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI).Brain tumor detection and segmentation are tough as brain tumors may vary in size,shape,and location.That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’.So an automated brain tumor detection and segmentation is required.This work suggests a Region-based Convolution Neural Network(RCNN)approach for automated brain tumor identification and segmentation using MR images,which helps solve the difficulties of brain tumor identification efficiently and accurately.Our methodology is based on the accurate and efficient selection of tumorous areas.That reduces computational complexity and time.We have validated the designed experimental setup on a standard dataset,BraTS 2020.We used binary evaluation matrices based on Dice Similarity Coefficient (DSC) and Mean Average Precision (mAP).The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method.The suggested approach attained an average DSC of 0.92 and mAP 0.92 for 10 patients,while on the whole dataset,the scores are DSC 0.89 and mAP 0.90.The following results clearly show the performance efficiency of the proposed methodology.

    Keywords: Brain tumor; MRI; preprocessing; image segmentation; brain tumor localization;medical;ML;RCNN;BraTS 2020;LGG;HGG

    1 Introduction

    A brain tumor is characterized by irregular brain cell growth that can affect the nervous system and be fatal in extreme cases.Brain tumors are known as malignant or benign brain tumors.Malignant is cancerous,while benign is a form of non-cancerous brain tumor.The growth rate of benign brain tumors is less severe than that of malignant tumors, considered the deadliest cancers.Brain tumors can be categorized as either depending on their initial origin, primary brain tumors, or metastatic cells that become malignant in another part of the body and spread to the brain [1,2].Gliomas are more prevalent primary malignant brain tumors.Gliomas are tumors that originate in the glial cells of the brain.Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG)are two types of gliomas.Patients with low-grade gliomas might expect to live for several years.

    On the other hand,high-Grade Gliomas are more severe and cystic,with most patients living less than two years after diagnosis [2–4].MRI is mainly utilized to detect gliomas due to the excellent resolution of multi-planar MR images.For assessing tumor volume fluctuation,segmentation of the MR image is crucial[5].

    MRI is one of the most effective procedures for evaluating brain malignancies.Because brain tumors vary in size,shape,and location,detecting and segmenting them can be difficult[6].As a result,radiologists and physicians have a difficult time manually detecting brain cancers using MRI.Brain tumor identification and segmentation must be automated or semi-automatic techniques [3].These automated techniques can help early find the exact size and location of the tumor that leads to further treatment planning.As a solution, different authors have already proposed multiple segmentation techniques,such as voting strategies,atlas,and machine learning[7].

    In our contributions,we have:

    Proposed a novel framework using deep learning technique to identify and segment brain tumor and through Active contour segmentation.

    Compared the performance of the proposed framework through state-of-the-art methods to validate the findings.

    Proposed framework will help medical practitioners and healthcare professionals for early diagnosis and better treatment of brain tumor.

    Nowadays, researchers regularly work on machine learning-based techniques for tracing and analyzing medical images [8–12].In this work, we proposed a method fordetecting and segmenting brain tumors using a regional convolutional neural network(RCNN).RCNN is a region-based technique of detection and segmentation.That can be proved helpful for smaller and larger datasets in different tasks[13].RCNN can prove helpful even for datasets having an un-sufficient sample count.In such datasets,we can utilize unsupervised pre-training,supervised fine tuning,and localization of regions to achieve the best results.Our proposed RCNN based method is divided into three parts,Preprocessing to enhance the image quality and remove intensity inhomogeneity, brain tumor Localization using RCNN, and Segmentation for accurate extraction of the tumor area.State of the art BraTS 2020 is used to test the proposed methodology.For proving the efficacy of the suggested technique,the results are compared to state-of-the-art methodologies.For ten patients, the proposed method achieved an average DSC of 0.92 and mAP of 0.92,whereas the scores for the entire dataset were DSC 0.89 and mAP 0.90.The following data demonstrate the suggested methodology’s performance efficiency.

    This paper is structured as follows: A brief discussion on multiple previous studies is given in Section 2,Methodology used is presented in Section 3,Results and Discussions are given in Section 4,while Section 5 contains the Conclusions and Future work.

    2 Literature Review

    2.1 Machine Learning

    Several machine learning and computer vision [14,15] based segmentation and classification algorithms exist to diagnose brain tumors,skin cancer,and lung cancer,common dangerous diseases in humans[16].Many machine learning segmentation techniques were used for brain tumor detection;most of them are classification-based.MRI intensities often reflect an a posteriori distribution in Generative techniques, whereas the appearance is represented by the probability function, while the prior reflects the prevalence of a condition,such as location[17–20].

    2.2 Probability

    Prastawa et al.[21] addressed the segmentation of brain tumors as outlier identification, as tumors are regions of abnormal intensity relative to healthy tissues.Other generative techniques use the probabilistic atlas to provide spatial information [18,20].A probabilistic atlas is obtained after a registration stage, modelling the patient’s brain structure.To overcome this issue, Kwon et al.[20]Anticipated a combined registration and segmentation technique,in which a healthy atlas template is transformed using a tumor development model to get the tumor and its sub-regions.Other approaches made advantage of the information from the surrounding tissues[17,18].

    Zhao et al.[22]take tumor data into account at the regional level.A 3D joint histogram is used to evaluate a probability function using a clustering approach based on intensity similarity and context.Menze et al.[3]used MRFs to regularize segmentation in the neighborhood of a voxel,with a different cost for pairs of labels.Li et al.[23] used the MRF after using a dictionary learning technique to achieve an inadequate depiction of the data.A patch-based multi-atlas approach with a stratified voting scheme was introduced by Cordier et al.[24].

    2.3 Support Vector Machine

    The most frequent classifiers for the segmentation of brain tumors [3] and other applications [25–27] are Random Forests [28,29], association mining [30] and Support Vector Machines [31].Bauer et al.[32] Proposed a hierarchical approach for SVMs that employed firstorder intensities and statistics as features.The RF classifier [33] is employed, and the feature vector is extended to include gradient and symmetry information.The input from the generative and discriminative models was merged by Zikic et al.[34].

    Tustison et al.[35]looked into utilizing the performance of a Gaussian Mixture Model as a feature in a two-step classification technique based on RF.Reza and Iftekhar Uddin [36] used point-based characteristics to give a broad summary of brain tissues.The second set of characteristics,primarily texture-based and includes fractal PTPSA, texton, and mBm, described the tumor.By converting the classifier Diverse Adaboost SVM to a Random forest, our technique extends Islam et al.[37].A Random Forest classifier was also used by Festa et al.[38]with a set of characteristics that included context features.Convolutional Neural Networks (CNNs) have had much success in the computer vision field[39–41].RCNN incorporate the idea of a local context by using kernel sets that are extended over pictures or image patches.The use of this characteristic in the segmentation of brain tumors has been studied[42–45].

    2.4 Deep Learning

    Das et al.[46] developed a deep learning-based framework for tumor diagnosis that uses a deep convolution neural network (Deep-CNN) that uses clinical presentations and traditional MRI studies.The Watershed segmentation approach was examined by Qaseem et al.[47]for brain tumor identification.The study used a large data set of pictures and found that the K-NN classification algorithm performed well in detecting brain tumors.Because brain tumors vary in form,structure,and volume,Havaei et al.[42]assessed cluster centroids(ABC populations)and used a level-set approach to resolve contour differences.The results demonstrate that the model is more efficient than it was previously.

    Our lead paper used RCNN,which enhances segmentation efficiency by computing deep features with a fair representation of Melanoma.The RCNN can identify numerous skin diseases in the same patient and diverse illnesses in separate individuals [48].We propose a method of Brain tumor detection and segmentation for detecting and segmenting brain tumors using a deep regional convolutional neural network(RCNN)and the Active contour segmentation approach in this study.For object localization, by combining unsupervised pre-training and supervised fine-tuning areas,RCNN overcomes the problem of inadequate data[49,50].

    3 Methodology

    3.1 Dataset

    The collection of data is called a dataset.The proposed algorithm is trained and tested on MRI brain slices containing tumors accessed from MICCAI BraTS 2020 [3,51,52].The supplied MRI dataset has a multi-model MR meta-images(MHA)file format.The dataset includes ground-truthed MR brain images of several individuals with brain tumors.Because patients are of varying ages and genders,tumor size,location,thickness,and color may differ.In each patient’s brain scan,the number of MRI slices containing tumor varies.There are 369 training, 125 validations, and 169 test multimodal brain MR studies.The MR picture comprises 155 slices of the brain taken along any of the three axes,with each slice measuring 240×240 pixels.After analyzing the medical pictures and obtaining the raw pixel data,we retrieved the 240240 2D slices from the 3D photos.

    3.2 System Overview

    In this paper,we presented a three-phase method,with the first being preprocessing,the second being localization,and the third being segmentation.In the first phase,to reduce the,only those slices are selected which contain tumor.Preprocessing is done where we applied median filter followed by bias field correction for intensity inhomogeneity.In the localization phase, RCNN (Region-based Convolutional Network) is applied for locating and extracting the brain tumor region.RCNN is applied to localize the brain tumor regions because it correctly detects the numerous tumors in the shape of a bounding box.We have used the completed BRAT 2020 experiment,but for the simplicity of the paper,we described various examples from 10 sample images.

    The RCNN considers the brain tumor region a region of interest (ROI) after training, whereas the remainder of the picture is considered background.The RCNN then used a regression layer and selective search approach to locate brain tumor areas using a pre-trained AlexNet model.The suggested model’s performance is measured using the DSC and is tested using a leave-one-out strategy(DSC).Fig.1 depicts the technique described in this study for automated brain tumor identification and segmentation.

    Figure 1: An architecture of proposed methodology for Brain tumor detection and segmentation,divided into three phases,Preprocessing,localization,and segmentation of Brain tumor

    3.3 Image Acquisition and Selection

    In this proposed framework,only the T1c sequence is used rather than considering all sequences for the tumor segmentation because T1c has the highest spatial resolution [50].Then slices having tumors are identified and selected.

    3.4 Preprocessing

    There might be a bias field or intensity inhomogeneity in MR pictures obtained by various MRI machines.It’s an artifact that has to be eliminated since it might skew segmentation findings.For bias field correction, we used the level set technique in the preprocessing stage [53].The median filter is used to remove noise from the image to obtain the improved image.A nonlinear filter called median filtering is an efficient approach for removing noise while retaining edges.A window glides pixel by pixel over the image in this sort of filtering procedure.During this movement,algorithm replaces each pixel’s value with the median value of surrounding pixels.All of the pixels’values are sorted first,and then the median value is used to replace the pixel value to determine the median value.When it comes to removing noise, the median filter outperforms linear filtering [54].After that, the ground truth images are rehabilitated to binary image and region split,and merge segmentation[55]is applied to ensure precise brain tumor detection through RCNN.In region Split and merge Segmentation,similar regions are merged,and different regions are split until there are no more similar regions to merge,or different regions are left to split.

    3.5 Brain Tumor Detection and Localization Using RCNN

    The input imageIp(x,y)proposes region against RCNN.The SoftMax layer procreated the predicted confidence score for classifying each region proposal as a tumor or non-tumorous region[42].Afterwards,we apply the greedy suppression algorithm[56]to exhaustive search the brain tumor regions,and bounding boxes are created over the affected area as

    Box over the image(BOI)the bounded area is centered at(x,y)coordinates and height and width are depicted byw,hof the bounding box.In our proposed methodology, in the training phase of RCNN,training pairs comprise the detected and ground-truth regions.The training pairs are presented as: {(BOIi,GTIi)}i=1.....N, whereishows training samples indexNandGTiis used to represent the ground-truth areas.TheGTI(Ground truth in image prediction)can be represented as:

    3.5.1 Feature Extraction

    CNN has shown a dramatic increase in performance efficiency for object detection as compared with object detection methods.But they are computationally costly because of the sliding window method they utilize for object detection.RCNN presented a selective search algorithm that uses fewer region proposals,saves computation time,and increases the CNN’s efficiency to overcome this limitation.

    Generally,the brain tumor varies in shape,size,and location;therefore,the image pyramid enables RCNN to downsample the input image, and deep feature representations are obtained to detect disease at various aspect ratios.The image pyramidBOIj(x,y,w,h)represents the imageIp(x,y)by frequent smoothing and down-sampling,and various levels of the image pyramid are depicted as the subscript:j= {1, 2....,4}The smoothing and down-sampling complete process can be denoted as

    The selective search step of RCNN collects texture,color,and intensity representation of tumor areas across many layers.We ran RCNN with different parameters values during experimentation found the best values and different selected parameters for precise modelling of brain tumor features as given in[13].

    3.5.2 Regression

    To accurately find the region affected by tumor disease, we used regression.The regression layer creates a bounding box around that detected tumor area.Optimal transformation functions are utilized to prevent any mislocalization.The four transformation functions for error reductionbx(BOI),by(BOI),bw(BOI)andbh(BOI)were used from our previous work[13].The projected tumor area is mapped over the ground truth using these transformation functions.The bx(BOI)and by(BOI)scale-invariant mappings of the center are presented as a box over the image(BOI).The input region suggestions were converted into anticipated ground truth brain tumor regions after learning optimum transformation maps.

    Regression targets are designated as Reg*in the proposed algorithm for training pairs(BOI,GTI)and are numerically described as follows:

    The predictedBOIis then assigned to the ground-truthGTI, yielding an intersection over the union(IoU)score.We’ve defined 0.7 as the intersection over the union threshold;if the IoT score is more than 0.7,it’s deemed a tumor region,while a lower number indicates a non-tumor region.

    3.5.3 Training Parameters for RCNN

    To accurately classify and localize brain tumor regions,AlexNet is fine-tuned by transfer learning using auxiliary labeled dataset CIFAR10[57].After attaining good accuracy compared to the stateof-the-art,Alexnet was tuned,and brain tumor classification was performed[58].

    Ground truth labels and region suggestions obtained by the selective search were utilized for training the network.Optimized weights were obtained using a stochastic gradient descent algorithm to decrease the error rate for brain tumor classification.Throughout the training phase,the learning rate was automatically modified using a piecewise learning scheme.Five hundred epochs were used for the optimized cost function to reduce brain tumor mislocalization.RCNN is performed 500 times during training to improve brain tumor localization.

    3.6 Brain Tumor Detection at the Test Time

    In image testing,selective searches were used to extract region suggestions,then wrapped to make the regions suitable for presentation to Alex Net’s input layer.For the identification of the brain tumor regions,SoftMax cross-entropy probabilities are utilized.Confidence scores are computed when deep convolutional features are fed into the SoftMax layer[42].

    3.7 Brain Tumor Segmentation Using Active Contour

    After the brain tumor has been located,the cropped portion of the MR image is used as the ROI(Region of Interest).The Active Contour Segmentation Algorithm,commonly known as snakes,takes this ROI and segments the tumor area.

    An inactive contour algorithm takes boundary as an initial step.Which are usually in spline curves shape and, depending upon the application underway, it spread out in a specific manner.The curve is created, resulting in an image consisting of several regions.In this process, expansion/contraction operations are implemented based on energy function.

    3.8 Limitation of the Approach

    The efficacy of our approach has been validated by a series of tests using the BraTS 2020 opensource dataset.We utilized unsupervised pre-training, supervised fine tuning, and localization of regions to achieve the best results,which was a time-costly procedure.To overcome this problem,we will apply our methodology to other variants of RCNN, i.e., Fast RCNN.We will extend our work by applying the proposed methodology to all the BraTS dataset variants to observe if the proposed method is efficient for the current BraTS or all.

    4 Results and Discussion

    The findings achieved using the suggested method at each phase of brain tumor identification and segmentation is detailed in this part.

    4.1 Evaluation Metrics

    The evaluation metrics for each phase of the proposed system are presented in this section.

    4.1.1 Results of Preprocessing

    After the image is acquired,preprocessing is applied.For image enhancement,a median filter is applied,and level set method is used for bias field correction[53];then connected component analysis of ground truth is done to find the largest connected object,which gives the location information of tumor,i.e.,(x,y,w,h).

    4.1.2 Brain Tumor Localization

    In preprocessing step,bias field correction and median filter were applied for image enhancement;after that, the enhanced image was then passed to the pre-trained Alex Net model for brain tumor localization.The soft-max layer of this model;classify the tumorous region and non-tumorous region by generating the feature map.We can see the localization results of test images and perceive that RCNN has accurately detected the tumor region in the test MRI scans.For localization of tumor region,convolutional layers assign different probabilities to tumorous and non–tumorous regions.

    Selective search generates region proposals from which RCNN extracts convolutional features,and then the AlexNet model uses them to classify the region proposal.For the localization of brain tumors,training of binary classifier is performed,and the tumorous region is assumed as a positive instance while the non–tumorous region is considered a negative instance.For labelling the region as tumor region,the IoU measure is used,and after several experimental runs,the threshold value was set to 0.7.We performed k-fold validation for each patient,and the value of k is set at 5.Afterwards,we calculated precision for every patient and,based on these precision scores,we accumulated mAP.For example,P4gives the highest precision of 0.99 among all patients after 5 runs,while P8shows the least precision.The IoU overlapping value higher than 0.7 is considered as tumor region detected,and lower than this threshold is declared non-tumorous.Tab.1 Piindicates the patient number and precision of algorithm performance found for each patient after localization of brain tumor regions at the regression layer and achieving mAP of 0.92 for 10 patients.

    Table 1: mAP Score to localize brain tumor using RCNN

    Figure 2:Resulted images after Preprocessing:(a)Input image with artifacts.(b)Image after bias field correction.(c)Median filter applied.(d)Resultant preprocessed image

    4.1.3 Brain Tumor Segmentation Using Active Contours

    The tumor region has to be correctly segmented to compute the segmentation performance of the brain tumor region.Active contour was used to segment the tumor area.In Fig.3,segmentation results are shown after implementing Active Contour and the segmented pictures produced are highly comparable to the ground truth images.

    Figure 3: Segmentation results and localized brain tumor images (a), (c).Localization results using RCNN(b),(d).Segmentation results using active contour segmentation

    The DSC score is used to measure the segmentation phase’s performance.Our method achieved an average DSC score of 0.92, representing good segmentation performance compared with other techniques.Results achieved based on Fig.3 are then compared with state-of-the-art techniques.The proposed method performed well because of the accurate localization results by using RCNN.

    The above box plot shows the DSC score of each patient.It also gives the highest and lowest DSC score range.For P1,which represents Patient 1,the minimum DSC score is 0.3,and the maximum DSC score is more than 0.8.Similarly,it gives the maximum and minimum range of the DSC.However,in a few images,the brain tumor was not localized accurately by RCNN because of the complex brain structure and visual similarity between tumor and non-tumor regions.The False positive MRI slices that were not detected correctly because of the complex brain structure are shown in Fig.2, which illustrates the difficulty in brain tumor detection.In Tab.2, we depict DSC scores for each of the selected ten patients along with true positive and true negatives;at the end of the table,we also represent average results for DSC.The highest DSC score was of P4and P7of 1,while P10shows the least DSC score.Fig.4 represents the boxplot of DSC of all ten patients.Fig.5 shows the heat map of all ten patients with HGG.

    Table 2: DSC Score,true positive,true negative

    Figure 4:Boxplot of 10 patients of HGG(High-Grade Glioma)showing DSC score of each patient

    Figure 5:Heat-map of 10 patients with High-Grade Gliomas(HGG)

    Figure 6:Comparison of proposed method with state-of-the-art techniques

    4.2 Comparison with State-of-the-Art Techniques:

    Compared to our suggested technique,the DSC score value obtained for the BraTS 2020 dataset was 0.92.The suggested approach is compared to state-of-the-art methods in Fig 6.

    Compared to previous techniques [22–24,33,42], Fig.6 demonstrates that the suggested model has greater computational efficiency.However,the task’s complexity is shown by the task’s overall low DSC scores.This research aims to develop a system that can accurately and efficiently preprocess,locate, and segment a brain tumor.Preprocessing, identifying the brain tumor, and subsequently,tumor segmentation is phases in the proposed technique.The effectiveness of a completely automated CAD system for the identification of brain tumors was investigated.BraTS 2020 was the dataset used in this study.

    In Tab.3,we have represented images of 5 phases for each sample patients are given.In the first column,the original image is shown before preprocessing stage.Then preprocessing is done,and the results are given in preprocessing column.In the localized tumor column,results of the localized tumor are shown,and the other two columns show results after applying segmentation and then assigning a color label to a segmented image.

    Table 3: Results of preprocessing,localization,and segmentation

    5 Conclusion and Future Work

    This work proposed a novel technique for practical, precise, and automated brain tumor area segmentation using MR images based on RCNN and Active contour segmentation.Preprocessing,brain tumor region identification and brain tumor segmentation are the three phases in our approach.Compared to state-of-the-art systems, the RCNN can assess deep features with good brain tumor representation,improving segmentation efficiency.In addition,our method may be utilized to address complex medical picture segmentation issues.Several experiments were performed using the BraTS 2020 open-source dataset.In addition,our method may be utilized to address complex medical picture segmentation issues.The efficacy of our approach has been validated by a series of tests using the BraTS 2020 open-source dataset.We utilized unsupervised pre-training,supervised fine tuning,and localization of regions to achieve the best results.We will apply our methodology to other variants of RCNN,i.e.,Fast RCNN,and will extend our work by applying the proposed methodology to all the BraTS dataset variants to observe if the proposed method is efficient for the current BraTS or all.We will extend our work with following[59–62].Later on,this work can be reached out to assess the findings of other BraTS challenge datasets to identify sub-tumor areas,such as complete,focus,and improve tumor.

    Acknowledgement:This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF).

    Funding Statement:This work was funded by the Ministry of Education under Grant NRF-2019R1A2C1006159 and Grant NRF-2021R1A6A1A03039493.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    这个男人来自地球电影免费观看 | 你懂的网址亚洲精品在线观看| 欧美3d第一页| 人人妻人人添人人爽欧美一区卜| 午夜日本视频在线| 18+在线观看网站| 国产亚洲一区二区精品| 人妻人人澡人人爽人人| 国产欧美日韩综合在线一区二区| 精品少妇黑人巨大在线播放| 韩国高清视频一区二区三区| 黑人欧美特级aaaaaa片| 最黄视频免费看| 女人久久www免费人成看片| 欧美成人午夜免费资源| 美女福利国产在线| 欧美性感艳星| 男人操女人黄网站| 精品酒店卫生间| 久久精品国产鲁丝片午夜精品| 最后的刺客免费高清国语| 亚洲精品乱久久久久久| 午夜福利网站1000一区二区三区| 91成人精品电影| 成年人午夜在线观看视频| a级毛片黄视频| 男女边吃奶边做爰视频| 免费人妻精品一区二区三区视频| 赤兔流量卡办理| 狂野欧美白嫩少妇大欣赏| 日本爱情动作片www.在线观看| 最黄视频免费看| 欧美日韩一区二区视频在线观看视频在线| 又黄又爽又刺激的免费视频.| 精品亚洲成a人片在线观看| 女性被躁到高潮视频| 免费av中文字幕在线| 在线观看免费日韩欧美大片 | 久久国产亚洲av麻豆专区| 色婷婷av一区二区三区视频| 一级毛片黄色毛片免费观看视频| tube8黄色片| 亚洲人与动物交配视频| 亚洲欧美一区二区三区国产| 狂野欧美激情性xxxx在线观看| 在线观看免费日韩欧美大片 | 久久ye,这里只有精品| 激情五月婷婷亚洲| 亚洲精品乱码久久久久久按摩| 欧美变态另类bdsm刘玥| 久久久精品区二区三区| 色视频在线一区二区三区| 亚洲婷婷狠狠爱综合网| av免费在线看不卡| 亚洲av欧美aⅴ国产| 欧美老熟妇乱子伦牲交| 精品人妻在线不人妻| 国产亚洲一区二区精品| 卡戴珊不雅视频在线播放| 亚洲av.av天堂| 久久99蜜桃精品久久| 国产熟女欧美一区二区| 亚洲av成人精品一区久久| 久久久久国产精品人妻一区二区| 黄色怎么调成土黄色| 日本av手机在线免费观看| 亚洲天堂av无毛| 久久精品人人爽人人爽视色| 久久久久久久亚洲中文字幕| 人妻少妇偷人精品九色| 最后的刺客免费高清国语| 国产片内射在线| 国产精品一国产av| 免费观看在线日韩| 十八禁网站网址无遮挡| 免费少妇av软件| 麻豆乱淫一区二区| 最后的刺客免费高清国语| 亚洲欧洲精品一区二区精品久久久 | 在线观看三级黄色| 亚洲熟女精品中文字幕| 黑丝袜美女国产一区| 人妻一区二区av| 欧美成人精品欧美一级黄| 五月天丁香电影| 啦啦啦中文免费视频观看日本| 国产免费视频播放在线视频| 777米奇影视久久| 69精品国产乱码久久久| 久久99一区二区三区| 欧美日本中文国产一区发布| 欧美日韩一区二区视频在线观看视频在线| 欧美老熟妇乱子伦牲交| 老女人水多毛片| 另类精品久久| 丝袜美足系列| 最近的中文字幕免费完整| 亚洲婷婷狠狠爱综合网| 青春草亚洲视频在线观看| 久久久久精品久久久久真实原创| 亚洲国产精品一区二区三区在线| av播播在线观看一区| 女性生殖器流出的白浆| 久久精品熟女亚洲av麻豆精品| 纯流量卡能插随身wifi吗| 简卡轻食公司| 美女视频免费永久观看网站| 亚洲精品日本国产第一区| 午夜福利视频精品| 久久久精品94久久精品| 菩萨蛮人人尽说江南好唐韦庄| 久久久久视频综合| 亚洲熟女精品中文字幕| 22中文网久久字幕| 成人亚洲欧美一区二区av| 一区二区日韩欧美中文字幕 | 色视频在线一区二区三区| 国产成人精品婷婷| 又大又黄又爽视频免费| 视频在线观看一区二区三区| 在线看a的网站| 不卡视频在线观看欧美| 在线观看免费视频网站a站| 国产爽快片一区二区三区| 欧美丝袜亚洲另类| av女优亚洲男人天堂| 丝袜美足系列| 久久99热这里只频精品6学生| 国产精品一区二区在线不卡| 国产精品欧美亚洲77777| 一本色道久久久久久精品综合| 美女主播在线视频| 亚洲精品久久久久久婷婷小说| 欧美日韩一区二区视频在线观看视频在线| 成人亚洲欧美一区二区av| 亚洲av二区三区四区| 欧美+日韩+精品| 久热这里只有精品99| 欧美精品人与动牲交sv欧美| 高清视频免费观看一区二区| 欧美老熟妇乱子伦牲交| 久久婷婷青草| av在线老鸭窝| 国产深夜福利视频在线观看| 国产视频内射| 韩国高清视频一区二区三区| 亚洲欧美一区二区三区国产| 高清欧美精品videossex| 一二三四中文在线观看免费高清| 一级二级三级毛片免费看| 国产综合精华液| 在线观看国产h片| 激情五月婷婷亚洲| 九色成人免费人妻av| 精品午夜福利在线看| av电影中文网址| 精品久久久精品久久久| av卡一久久| 人人妻人人添人人爽欧美一区卜| 中文字幕最新亚洲高清| 搡老乐熟女国产| 亚州av有码| 久久久久视频综合| 黑人高潮一二区| 亚洲伊人久久精品综合| 亚洲欧美成人综合另类久久久| 成人毛片a级毛片在线播放| 色94色欧美一区二区| 亚洲中文av在线| 女人久久www免费人成看片| 欧美日韩综合久久久久久| 久久女婷五月综合色啪小说| 伊人久久精品亚洲午夜| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 国产熟女欧美一区二区| 亚洲成人av在线免费| 成人亚洲欧美一区二区av| 精品国产露脸久久av麻豆| 亚洲av日韩在线播放| 老司机影院毛片| 国产成人freesex在线| 国产成人午夜福利电影在线观看| 久久精品国产a三级三级三级| 在线观看人妻少妇| 久久久国产欧美日韩av| 亚洲国产精品国产精品| 飞空精品影院首页| a级毛色黄片| 麻豆乱淫一区二区| 亚洲国产欧美日韩在线播放| 国产精品久久久久久精品古装| 天堂俺去俺来也www色官网| 丝袜脚勾引网站| 色婷婷久久久亚洲欧美| 内地一区二区视频在线| 久久久久久久大尺度免费视频| 中文字幕制服av| 亚洲精品456在线播放app| 黄色视频在线播放观看不卡| 精品亚洲成a人片在线观看| 人体艺术视频欧美日本| 国产精品国产av在线观看| 亚洲,欧美,日韩| 国产在线免费精品| 亚洲经典国产精华液单| 九九在线视频观看精品| 久久热精品热| 成人手机av| 亚洲美女视频黄频| 国产欧美另类精品又又久久亚洲欧美| 日本猛色少妇xxxxx猛交久久| 亚洲国产精品一区三区| 美女内射精品一级片tv| 久久久久网色| 欧美3d第一页| 在线观看人妻少妇| 人妻 亚洲 视频| kizo精华| 色94色欧美一区二区| 国产欧美亚洲国产| 国产免费一级a男人的天堂| 日本与韩国留学比较| 视频区图区小说| 国产成人精品婷婷| 搡老乐熟女国产| 18禁在线无遮挡免费观看视频| 能在线免费看毛片的网站| 成年人午夜在线观看视频| 国产无遮挡羞羞视频在线观看| 久久精品国产鲁丝片午夜精品| 美女中出高潮动态图| 精品少妇黑人巨大在线播放| 全区人妻精品视频| 亚洲精品久久久久久婷婷小说| 国产伦精品一区二区三区视频9| 欧美精品国产亚洲| xxxhd国产人妻xxx| 国产精品久久久久久久久免| 中文字幕亚洲精品专区| 欧美97在线视频| 99re6热这里在线精品视频| 日本黄大片高清| 永久免费av网站大全| 又大又黄又爽视频免费| 久久久精品94久久精品| av国产久精品久网站免费入址| 国产精品一区www在线观看| 欧美日韩亚洲高清精品| 日韩一区二区视频免费看| 伊人久久精品亚洲午夜| av.在线天堂| 五月伊人婷婷丁香| 亚洲欧洲日产国产| 色视频在线一区二区三区| 成年人午夜在线观看视频| a 毛片基地| 九九爱精品视频在线观看| 考比视频在线观看| 熟女电影av网| 国产 一区精品| 精品久久久久久久久av| 亚洲国产最新在线播放| 女性被躁到高潮视频| 自线自在国产av| 人妻 亚洲 视频| av.在线天堂| 美女主播在线视频| 久久ye,这里只有精品| 日韩免费高清中文字幕av| 嘟嘟电影网在线观看| 免费人成在线观看视频色| 久久精品人人爽人人爽视色| 一区二区三区精品91| 日韩中文字幕视频在线看片| 啦啦啦视频在线资源免费观看| 国产黄色视频一区二区在线观看| 亚洲国产精品国产精品| 免费看光身美女| 久久久亚洲精品成人影院| 久久久久久伊人网av| 久久青草综合色| 最近2019中文字幕mv第一页| 一个人免费看片子| 大香蕉久久网| 91aial.com中文字幕在线观看| 免费av中文字幕在线| 一级毛片我不卡| 中文字幕制服av| 国产精品99久久久久久久久| 亚洲av中文av极速乱| 国产欧美日韩综合在线一区二区| 久久久久人妻精品一区果冻| 中文乱码字字幕精品一区二区三区| 亚洲不卡免费看| 欧美日本中文国产一区发布| 亚洲精品久久久久久婷婷小说| 日本猛色少妇xxxxx猛交久久| 一本久久精品| 丝瓜视频免费看黄片| 国产欧美亚洲国产| 亚洲一区二区三区欧美精品| 中文精品一卡2卡3卡4更新| 亚洲国产最新在线播放| 久热久热在线精品观看| 中国美白少妇内射xxxbb| 亚洲精品自拍成人| 一级a做视频免费观看| 国产片特级美女逼逼视频| 91成人精品电影| 国产高清不卡午夜福利| 亚洲欧洲国产日韩| 中文字幕av电影在线播放| 国产一级毛片在线| 91精品三级在线观看| 国产黄频视频在线观看| 狂野欧美激情性xxxx在线观看| 狂野欧美激情性bbbbbb| 日韩欧美一区视频在线观看| 寂寞人妻少妇视频99o| 桃花免费在线播放| 99久久综合免费| 丝瓜视频免费看黄片| 亚洲经典国产精华液单| 有码 亚洲区| 亚洲国产成人一精品久久久| 男的添女的下面高潮视频| 99九九在线精品视频| 成年女人在线观看亚洲视频| 欧美人与性动交α欧美精品济南到 | 欧美 亚洲 国产 日韩一| 中文字幕久久专区| 精品久久久久久久久亚洲| 亚洲欧美清纯卡通| 国产免费又黄又爽又色| 最新的欧美精品一区二区| 99九九线精品视频在线观看视频| 欧美亚洲日本最大视频资源| 不卡视频在线观看欧美| 街头女战士在线观看网站| 最近中文字幕2019免费版| 久久99精品国语久久久| 亚洲色图综合在线观看| 国产视频内射| 午夜精品国产一区二区电影| 免费看不卡的av| 欧美日韩综合久久久久久| 蜜臀久久99精品久久宅男| 精品一区二区免费观看| a 毛片基地| av专区在线播放| 精品一区二区免费观看| 国产精品嫩草影院av在线观看| 中文字幕人妻熟人妻熟丝袜美| 国产黄色免费在线视频| 国产综合精华液| 国产色婷婷99| 亚洲精品aⅴ在线观看| 一本色道久久久久久精品综合| 亚洲人与动物交配视频| 亚洲精品国产av蜜桃| 欧美另类一区| 免费观看的影片在线观看| 亚洲精品一区蜜桃| av有码第一页| 日日撸夜夜添| 一区二区三区乱码不卡18| 亚洲av成人精品一区久久| 女性生殖器流出的白浆| 两个人的视频大全免费| 亚洲国产av新网站| 欧美性感艳星| 国产片特级美女逼逼视频| 国产一区有黄有色的免费视频| 女人久久www免费人成看片| 少妇被粗大猛烈的视频| 久久午夜综合久久蜜桃| 麻豆乱淫一区二区| 国产色婷婷99| 在线观看免费日韩欧美大片 | 久久婷婷青草| 少妇丰满av| 黄片播放在线免费| 妹子高潮喷水视频| 亚洲第一区二区三区不卡| 久久久久久久精品精品| 日本黄色片子视频| 免费高清在线观看日韩| 亚洲精品色激情综合| 久久精品国产亚洲网站| av视频免费观看在线观看| 免费人妻精品一区二区三区视频| 美女cb高潮喷水在线观看| 免费人成在线观看视频色| 视频区图区小说| 嫩草影院入口| 亚洲国产成人一精品久久久| 亚洲欧美精品自产自拍| 黑人高潮一二区| 少妇人妻精品综合一区二区| 久久女婷五月综合色啪小说| 高清av免费在线| 在线观看人妻少妇| 两个人的视频大全免费| 黑人欧美特级aaaaaa片| 欧美97在线视频| 制服丝袜香蕉在线| 天堂8中文在线网| 精品一区二区三卡| 亚洲欧美精品自产自拍| 99热网站在线观看| 亚洲国产成人一精品久久久| 国产成人精品福利久久| 一边亲一边摸免费视频| 男人爽女人下面视频在线观看| 日本-黄色视频高清免费观看| 一级毛片 在线播放| 少妇熟女欧美另类| 免费看不卡的av| 夜夜看夜夜爽夜夜摸| 日韩av在线免费看完整版不卡| 亚洲内射少妇av| 欧美日韩av久久| 大陆偷拍与自拍| 欧美bdsm另类| 91精品国产国语对白视频| 欧美97在线视频| 精品人妻偷拍中文字幕| 亚洲av国产av综合av卡| a级毛片免费高清观看在线播放| 自线自在国产av| 肉色欧美久久久久久久蜜桃| 啦啦啦在线观看免费高清www| 亚洲av综合色区一区| 又黄又爽又刺激的免费视频.| 亚洲,欧美,日韩| 亚洲av中文av极速乱| 视频中文字幕在线观看| 韩国高清视频一区二区三区| 国产一区二区三区综合在线观看 | 欧美变态另类bdsm刘玥| 全区人妻精品视频| 一级毛片 在线播放| 久久综合国产亚洲精品| 韩国av在线不卡| 老女人水多毛片| 久久女婷五月综合色啪小说| 91国产中文字幕| 亚洲欧洲日产国产| 丰满少妇做爰视频| 亚洲国产欧美日韩在线播放| 免费看不卡的av| 22中文网久久字幕| 校园人妻丝袜中文字幕| 精品人妻一区二区三区麻豆| 国产免费福利视频在线观看| 久久久久精品久久久久真实原创| 少妇的逼水好多| kizo精华| 国产又色又爽无遮挡免| 黑丝袜美女国产一区| 成人毛片a级毛片在线播放| av又黄又爽大尺度在线免费看| 日日摸夜夜添夜夜爱| 乱人伦中国视频| 熟女av电影| 国产精品国产三级专区第一集| 精品午夜福利在线看| 99九九在线精品视频| av.在线天堂| 少妇被粗大猛烈的视频| 伦理电影免费视频| 亚洲欧美中文字幕日韩二区| 性色av一级| 久久精品夜色国产| 女人久久www免费人成看片| 亚洲色图 男人天堂 中文字幕 | 日日摸夜夜添夜夜爱| 国产成人aa在线观看| 欧美97在线视频| 狠狠婷婷综合久久久久久88av| 少妇人妻 视频| av国产精品久久久久影院| 国产乱来视频区| 国产精品99久久久久久久久| 十分钟在线观看高清视频www| 九草在线视频观看| 亚洲无线观看免费| 男的添女的下面高潮视频| 亚州av有码| 春色校园在线视频观看| 熟女av电影| 亚洲精品日韩在线中文字幕| 成人漫画全彩无遮挡| 99热全是精品| 欧美日韩综合久久久久久| 十分钟在线观看高清视频www| 亚洲综合精品二区| 日韩 亚洲 欧美在线| 大香蕉久久网| 亚洲av成人精品一二三区| 91久久精品国产一区二区成人| 美女视频免费永久观看网站| 午夜福利在线观看免费完整高清在| 国产精品免费大片| 少妇人妻精品综合一区二区| 久久婷婷青草| 在线观看免费日韩欧美大片 | 亚洲内射少妇av| 中文乱码字字幕精品一区二区三区| 日日摸夜夜添夜夜添av毛片| 大陆偷拍与自拍| 69精品国产乱码久久久| 激情五月婷婷亚洲| 久久久久国产精品人妻一区二区| videosex国产| 国产极品粉嫩免费观看在线 | 99热国产这里只有精品6| 国产熟女午夜一区二区三区 | 三级国产精品片| 久久久久久久久久成人| 国产精品无大码| 人妻人人澡人人爽人人| 欧美变态另类bdsm刘玥| av福利片在线| 97精品久久久久久久久久精品| 九草在线视频观看| 亚洲精品色激情综合| 久久99热6这里只有精品| 亚洲精华国产精华液的使用体验| 欧美精品一区二区大全| 日韩三级伦理在线观看| 国产精品久久久久久久久免| 国产有黄有色有爽视频| 日韩不卡一区二区三区视频在线| 欧美精品国产亚洲| 国产精品无大码| 老司机亚洲免费影院| 精品酒店卫生间| 亚洲欧洲精品一区二区精品久久久 | 午夜免费男女啪啪视频观看| 成人国语在线视频| 久久久久久久国产电影| 国产黄频视频在线观看| 中文乱码字字幕精品一区二区三区| 精品人妻偷拍中文字幕| 国产精品久久久久成人av| 国产精品一区二区三区四区免费观看| av国产久精品久网站免费入址| 纯流量卡能插随身wifi吗| 最新的欧美精品一区二区| 久久久久网色| 成人毛片60女人毛片免费| 免费大片18禁| 亚洲中文av在线| 久久99热这里只频精品6学生| 精品一区二区三卡| 少妇被粗大猛烈的视频| 高清在线视频一区二区三区| 日韩伦理黄色片| 久久精品国产鲁丝片午夜精品| 国产午夜精品久久久久久一区二区三区| 国产精品蜜桃在线观看| 在线看a的网站| 久久国产亚洲av麻豆专区| 精品酒店卫生间| 欧美性感艳星| 亚洲精品乱久久久久久| 三上悠亚av全集在线观看| 日本wwww免费看| 国产视频内射| 又大又黄又爽视频免费| 中文字幕制服av| 亚洲欧美成人综合另类久久久| 人妻系列 视频| 亚洲精品aⅴ在线观看| 韩国av在线不卡| 国产成人精品久久久久久| 日日摸夜夜添夜夜爱| 精品国产一区二区三区久久久樱花| 91精品伊人久久大香线蕉| 国产精品久久久久久久电影| 夫妻性生交免费视频一级片| 99久久中文字幕三级久久日本| 麻豆乱淫一区二区| 五月开心婷婷网| 国产欧美另类精品又又久久亚洲欧美| 中文字幕人妻熟人妻熟丝袜美| 国产午夜精品久久久久久一区二区三区| 啦啦啦中文免费视频观看日本| 亚洲图色成人| 国产黄色免费在线视频| 亚洲国产最新在线播放| 日韩成人伦理影院| 亚州av有码| 狠狠精品人妻久久久久久综合| 亚洲av二区三区四区| 午夜福利视频精品| 另类亚洲欧美激情| 女人精品久久久久毛片| 最近2019中文字幕mv第一页| 免费高清在线观看日韩| av天堂久久9| 国产色爽女视频免费观看| 国产一区二区三区av在线| 色视频在线一区二区三区| 亚洲图色成人| 国产成人精品无人区| 亚洲欧洲精品一区二区精品久久久 | 国产片内射在线| 国产欧美日韩一区二区三区在线 | 日本av手机在线免费观看| 日韩欧美一区视频在线观看| 久久久久视频综合| 黑人巨大精品欧美一区二区蜜桃 | 午夜免费观看性视频|