The Experts below are selected from a list of 63753 Experts worldwide ranked by ideXlab platform
Andrei Faraon - One of the best experts on this subject based on the ideXlab platform.
-
Single-shot quantitative phase Gradient microscopy using a system of multifunctional metasurfaces
Nature Photonics, 2020Co-Authors: Hyounghan Kwon, Ehsan Arbabi, Seyedeh Mahsa Kamali, Mohammadsadegh Faraji-dana, Andrei FaraonAbstract:Quantitative phase imaging (QPI) of transparent samples plays an essential role in multiple biomedical applications, and miniaturizing these systems will enable their adoption into point-of-care and in vivo applications. Here, we propose a compact quantitative phase Gradient microscope (QGPM) based on two dielectric metasurface layers, inspired by a classical differential interference contrast (DIC) microscope. Owing to the multifunctionality and compactness of the dielectric metasurfaces, the QPGM simultaneously captures three DIC Images to generate a quantitative phase Gradient Image in a single shot. The volume of the metasurface optical system is on the order of 1 mm^3. Imaging experiments with various phase resolution samples verify the capability to capture quantitative phase Gradient data, with phase Gradient sensitivity better than 92.3 mrad μm^−1 and single-cell resolution. The results showcase the potential of metasurfaces for developing miniaturized QPI systems for label-free cellular imaging and point-of-care devices. Using two dielectric metasurface layers, a compact quantitative phase Gradient microscope that can capture quantitative phase Gradient Images in a single shot is reported with phase Gradient sensitivity better than 92.3 mrad μm^−1 and single-cell resolution.
P W Huang - One of the best experts on this subject based on the ideXlab platform.
-
automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs
Computer Methods and Programs in Biomedicine, 2017Co-Authors: P Y Huang, P W HuangAbstract:Abstract Background and objective Periodontitis involves progressive loss of alveolar bone around the teeth. Hence, automatic alveolar bone loss measurement in periapical radiographs can assist dentists in diagnosing such disease. In this paper, we propose an automatic length-based alveolar bone loss measurement system with emphasis on a cementoenamel junction (CEJ) localization method: CEJ_LG. Method The bone loss measurement system first adopts the methods TSLS and ABLifBm, which we presented previously, to extract teeth contours and bone loss areas from periodontitis radiograph Images. It then applies the proposed methods to locate the positions of CEJ, alveolar crest (ALC), and apex of tooth root (APEX), respectively. Finally the system computes the ratio of the distance between the positions of CEJ and ALC to the distance between the positions of CEJ and APEX as the degree of bone loss for that tooth. The method CEJ_LG first obtains the Gradient of the tooth Image then detects the border between the lower enamel and dentin (EDB) from the Gradient Image. Finally, the method identifies a point on the tooth contour that is horizontally closest to the EDB. Results Experimental results on 18 tooth Images segmented from 12 periodontitis periapical radiographs, including 8 views of upper-jaw teeth and 10 views of lower-jaw teeth, show that 53% of the localized CEJs are within 3 pixels deviation (∼ 0.15 mm) from the positions marked by dentists and 90% have deviation less than 9 pixels (∼ 0.44 mm). For degree of alveolar bone loss, more than half of the measurements using our system have deviation less than 10% from the ground truth, and all measurements using our system are within 25% deviation from the ground truth. Conclusion Our results suggest that the proposed automatic system can effectively estimate degree of horizontal alveolar bone loss in periodontitis radiograph Images. We believe that our proposed system, if implemented in routine clinical practice, can serve as a valuable tool for early and accurate diagnosis of alveolar bone loss in periodontal diseases and also for assessing the status of alveolar bone following various types of non surgical and surgical and regenerative therapy. For overall system improvement, a more objective comparison by using transgingival bone measurement with a periodontal probe as the ground truth and enhancing the localization algorithms of these three critical points are the two major tasks.
P Y Huang - One of the best experts on this subject based on the ideXlab platform.
-
automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs
Computer Methods and Programs in Biomedicine, 2017Co-Authors: P Y Huang, P W HuangAbstract:Abstract Background and objective Periodontitis involves progressive loss of alveolar bone around the teeth. Hence, automatic alveolar bone loss measurement in periapical radiographs can assist dentists in diagnosing such disease. In this paper, we propose an automatic length-based alveolar bone loss measurement system with emphasis on a cementoenamel junction (CEJ) localization method: CEJ_LG. Method The bone loss measurement system first adopts the methods TSLS and ABLifBm, which we presented previously, to extract teeth contours and bone loss areas from periodontitis radiograph Images. It then applies the proposed methods to locate the positions of CEJ, alveolar crest (ALC), and apex of tooth root (APEX), respectively. Finally the system computes the ratio of the distance between the positions of CEJ and ALC to the distance between the positions of CEJ and APEX as the degree of bone loss for that tooth. The method CEJ_LG first obtains the Gradient of the tooth Image then detects the border between the lower enamel and dentin (EDB) from the Gradient Image. Finally, the method identifies a point on the tooth contour that is horizontally closest to the EDB. Results Experimental results on 18 tooth Images segmented from 12 periodontitis periapical radiographs, including 8 views of upper-jaw teeth and 10 views of lower-jaw teeth, show that 53% of the localized CEJs are within 3 pixels deviation (∼ 0.15 mm) from the positions marked by dentists and 90% have deviation less than 9 pixels (∼ 0.44 mm). For degree of alveolar bone loss, more than half of the measurements using our system have deviation less than 10% from the ground truth, and all measurements using our system are within 25% deviation from the ground truth. Conclusion Our results suggest that the proposed automatic system can effectively estimate degree of horizontal alveolar bone loss in periodontitis radiograph Images. We believe that our proposed system, if implemented in routine clinical practice, can serve as a valuable tool for early and accurate diagnosis of alveolar bone loss in periodontal diseases and also for assessing the status of alveolar bone following various types of non surgical and surgical and regenerative therapy. For overall system improvement, a more objective comparison by using transgingival bone measurement with a periodontal probe as the ground truth and enhancing the localization algorithms of these three critical points are the two major tasks.
-
Automatic Methods for Alveolar Bone Loss Area Localization and Degree Measurement in Periodontitis Periapical Radiographs
2024Co-Authors: P Y HuangAbstract:Periodontitis is a set of inflammatory diseases affecting the periodontium, the tissues that surround and support the teeth. It is caused by microorganisms that adhere to and grow on the tooth's surfaces. Periodontitis involves progressive loss of the alveolar bone around the teeth and its diagnosis can be established from a) clinical examination by inspecting the soft gum tissues around the teeth with a probe, and b) radiographic examination by evaluating the patient's X-ray films (radiographs) to determine the amount of alveolar bone loss around the teeth. For diagnosing the degree of alveolar bone-loss, periapical radiograph that is a close-up view of a few individual teeth is the best choice, as bone loss usually occur around tooth boundaries and can only be detected in close-up views. Due to large amount of Images, dentists may possibly make some mistakes or misjudgment under long working hours. For automatic measuring the degree of alveolar bone-loss, alveolar bone-loss areas in the radiograph and three critical positions (CEJ, BLC, and APEX) of each infective tooth within the radiograph must firstly be identified. Since APEX is the apex of tooth contour, CEJ is at the location that divides the tooth into crown and root parts, and BLC is located at the intersection of the alveolar bone-loss area and tooth contour, automatic teeth segmentation for periapical radiographs, localization of alveolar bone-loss areas, and CEJ detection are three essential and critical tasks. In this dissertation, we propose three effective methods: TSLS, ABLIFBM, and CEJTG for each of the three aforementioned critical tasks, respectively. Our teeth segmentation method TSLS consists of four stages: Image enhancement using adaptive power law transformation, local singularity analysis using Holder exponent, tooth recognition using Otsu's thresholding and connected component analysis, and tooth delineation using snake boundary tracking and morphological operations. The experimental results showed that TSLS can achieve accuracy of approximately 99% for tooth isolation and (90%, 0.9%) for tooth segmentation in terms of (TPVF, FPVF), respectively. The proposed alveolar bone-loss area localization method ABLIFBM is a thresholded segmentation method using a hybrid feature obtained from a weighted average of both intensity and the H-value of fractional Brownian motion model FBM-H. Adopting leave-one-out cross validation (LOOCV) training and testing mechanism, we train a pair of weights for both features using Bayesian classifier and transform the radiograph Image into a feature Image using weighted average of both features. Finally, by Otsu's thresholding, we segment the feature Image into normal and bone-loss regions. The experimental results on 28 periodontitis radiograph Images showed that ABLIFBM can achieve accuracy of approximately (92.5%, 12.8%) for bone-loss area detection in terms of (TPVF, FPVF). As for the proposed CEJ detection method (CEJTG), we first preprocessed the Image based on bilateral filter to remove noise while preserving edge information and power law transformation to stretch contrast. Then, we calculate Gradient Image by using the Sobel operator to obtain horizontal changes. Finally, we track on Gradient Image to find the CEJ position. The experimental results showed that out of 30 detected CEJs, CEJTG has mean pixels distance of 4.3 when compared to the ground truth.牙周炎(牙周病)是種影響牙周組織的慢性炎症，其成因為微生物附著在牙齒表面生長，導致牙齒周邊的齒槽骨喪失。其診斷可透過臨床檢測牙齒周邊牙齦軟組織以及透過X光片評估病人的齒槽骨喪失程度來確認。由於根尖X光片可對個別牙齒進行近照觀察，而牙周炎之齒槽骨喪失區域發生在牙齒周邊，因此根尖X光片最適用於診斷牙周炎之骨喪失。然而牙醫師因長時間的工作後又要瀏覽大量的影像，可能做出不準確的判斷。因此本論文提出根尖X光片自動化牙周炎齒槽骨喪失偵測與程度評估之研究。本研究包含三個部分；牙齒切割、齒槽骨喪失區域偵測以及琺瑯質牙骨質交界(Cemento–Enamel Junction, CEJ)偵測。 由於牙周炎發生在牙齒輪廓周圍，而牙齒根尖X光片經常面臨雜訊、低對比以及光照不均的問題，為了有效偵測牙周炎，我們提出兩個根尖X光片牙齒切割方法，分別為基於邊緣地圖(Edge Map)的牙齒切割方法(TSEM)以及基於區域奇異性(Local Singularity)之牙齒切割法(TSLS)。TSLS改善了TSEM牙齒輪廓切割不準確之問題。齒槽骨喪失區域偵測研究，本論文提出一個以權重方式混合灰階亮度值(Intensity)與分數布朗運動(fractional Brownian motion)之紋理分析H值為特徵之閥值切割法(ABLIFBM)。ABLIFBM方法能夠改善單一特徵對於照度不均以及複雜組織切割不準確的問題。 齒槽骨喪失程度評估可依據牙根尖位置、齒槽骨喪失區域在牙齒輪廓上之最低點與琺瑯質牙骨質交界(CEJ)三個位置進行比例計算。CEJ偵測，本研究提出一個透過追蹤梯度影像(Gradient Image)的CEJ偵測方法(CEJTG)。最後結合TSLS、ABLIFBM與CEJTG方法找到牙根尖位置、齒槽骨喪失在牙齒輪廓上之最低點與琺瑯質牙骨質交界(CEJ)進行齒槽骨喪失程度評估。1. Introduction………………………………………………………………… 1 1.1. Problem Descriptions………………………………………………….. 1 1.2. Organization of the Dissertation………………………………………. 5 2. Background………………………………………………………………… 6 2.1. Texture Analysis……………………………………………………….. 6 2.1.1. Gray-Level Co-occurrence Matrix (GLCM)……………………… 6 2.1.2. Local Singularity………………………………………………….. 7 2.1.3. Fractional Brownian Motion (FBM) Models…………..…………. 8 2.2. Classification Methods………………………………………………… 10 2.2.1. Bayesian Classifier………………………………………………... 10 2.2.2. k-NN Classifier…………………………………………………… 10 2.2.3. SVM Classifier……………………………………………………. 11 2.3. Segmentation Methods………………………………………………… 12 2.3.1. Otsu's Method…………………………………………………….. 12 2.3.2. Level Set Function (LSF)…………………………………………. 13 2.4. Bilateral Filter…………………………………………………………. 14 2.5 Receiver Operating Characteristic Curve………………………………. 15 3. Teeth Segmentation for Dental Periapical Radiographs…………………… 17 3.1. Teeth Segmentation Method Using Edge Map: TSEM………………... 19 3.1.1. Algorithm: TSEM…………………………………………………. 19 3.2. Teeth Segmentation Method Using Local Singularity: TSLS…………. 21 3.2.1. Image Enhancement Using Adaptive Power-Law Transformation.. 21 3.2.2. Local Singularity Analysis………………………………………... 23 3.2.3. Bilateral Filtering…………………………………………………. 25 3.2.4. Teeth Recognition (Coarse Tooth Segmentation)………………… 26 3.2.5. Tooth Delineation (Fine Segmentation)…………………………... 27 3.3. Experimental Results and Assessments……………………………….. 29 3.3.1. Experiments and Results………………………………………….. 29 3.3.2. Performance Assessment and Analysis…………………………… 33 3.3.2.1. Isolation Accuracy……………………………………………. 33 3.3.2.2. Segmentation Accuracy………………………………………. 34 3.3.2.3. Analysis………………………………………………………. 36 3.4. Comparison and Discussion…………………………………………… 37 4. Alveolar Bone-Loss Area Localization in Periodontitis Radiographs……... 38 4.1. Investigation and Comparison of Classification Effectiveness of Features for Periodontitis Images……………………………………... 41 4.2. Alveolar Bone-Loss Area Detection Method: ABLIFBM…………….. 42 4.2.1. ROI Identification………………………………………………… 43 4.2.2. Fusion of Intensity and Texture…………………………………… 43 4.2.2.1. Weighted Average of Intensity Image and FBM-H Image………………………………………………………... 44 4.2.2.2. Weight Assignment…………………………………………... 44 4.2.3. Coarse Segmentation……………………………………………… 47 4.2.4. Fine Segmentation………………………………………………… 47 4.3. Experimental Results and Comparison………………………………... 48 4.3.1. Performance Assessment………………………………………….. 48 4.3.2. Results and Analysis……………………………………………… 49 4.4. Comparisons and Discussion………………………………………….. 55 4.4.1. Comparison with LSF Segmentation Method…………………….. 56 4.4.2. Comparison with the Same Segmentation Method Using Different Weight Combinations……………………………………………... 56 4.4.3. Comparison with the Segmentation Method Using Weights Trained Based on 2-fold Cross Validation………………………... 57 5. Measuring Alveolar Bone-Loss in Periodontitis Radiographs……………... 59 5.1. ISO 3950 Notation Tooth Numbering System………………………… 59 5.2. Cemento–Enamel Junction (CEJ) Detection…………………………... 61 5.2.1. CEJ detection (CEJTG)…………………………………………… 62 5.2.2. Experimental Results and Performance Assessment……………... 65 5.3. Alveolar Bone-Loss Degree Measurement……………………………. 68 5.3.1. Apex and Alveolar Crest Detection……………………………….. 68 5.3.2. Alveolar Bone-Loss Degree Measurement Procedure……………. 70 5.3.3. Experimental results………………………………………………. 70 5.3.4. Performance Assessment and Analysis…………………………… 71 6. Conclusions………………………………………………………………… 73 6.1. Major Contribution……………………………………………………. 73 6.2. Suggestions for Future Research………………………………………. 75 Acknowledgments…………………………………………………………….. 76 References…………………………………………………………………….. 7
Hyounghan Kwon - One of the best experts on this subject based on the ideXlab platform.
-
Single-shot quantitative phase Gradient microscopy using a system of multifunctional metasurfaces
Nature Photonics, 2020Co-Authors: Hyounghan Kwon, Ehsan Arbabi, Seyedeh Mahsa Kamali, Mohammadsadegh Faraji-dana, Andrei FaraonAbstract:Quantitative phase imaging (QPI) of transparent samples plays an essential role in multiple biomedical applications, and miniaturizing these systems will enable their adoption into point-of-care and in vivo applications. Here, we propose a compact quantitative phase Gradient microscope (QGPM) based on two dielectric metasurface layers, inspired by a classical differential interference contrast (DIC) microscope. Owing to the multifunctionality and compactness of the dielectric metasurfaces, the QPGM simultaneously captures three DIC Images to generate a quantitative phase Gradient Image in a single shot. The volume of the metasurface optical system is on the order of 1 mm^3. Imaging experiments with various phase resolution samples verify the capability to capture quantitative phase Gradient data, with phase Gradient sensitivity better than 92.3 mrad μm^−1 and single-cell resolution. The results showcase the potential of metasurfaces for developing miniaturized QPI systems for label-free cellular imaging and point-of-care devices. Using two dielectric metasurface layers, a compact quantitative phase Gradient microscope that can capture quantitative phase Gradient Images in a single shot is reported with phase Gradient sensitivity better than 92.3 mrad μm^−1 and single-cell resolution.
Ehsan Arbabi - One of the best experts on this subject based on the ideXlab platform.
-
Single-shot quantitative phase Gradient microscopy using a system of multifunctional metasurfaces
Nature Photonics, 2020Co-Authors: Hyounghan Kwon, Ehsan Arbabi, Seyedeh Mahsa Kamali, Mohammadsadegh Faraji-dana, Andrei FaraonAbstract:Quantitative phase imaging (QPI) of transparent samples plays an essential role in multiple biomedical applications, and miniaturizing these systems will enable their adoption into point-of-care and in vivo applications. Here, we propose a compact quantitative phase Gradient microscope (QGPM) based on two dielectric metasurface layers, inspired by a classical differential interference contrast (DIC) microscope. Owing to the multifunctionality and compactness of the dielectric metasurfaces, the QPGM simultaneously captures three DIC Images to generate a quantitative phase Gradient Image in a single shot. The volume of the metasurface optical system is on the order of 1 mm^3. Imaging experiments with various phase resolution samples verify the capability to capture quantitative phase Gradient data, with phase Gradient sensitivity better than 92.3 mrad μm^−1 and single-cell resolution. The results showcase the potential of metasurfaces for developing miniaturized QPI systems for label-free cellular imaging and point-of-care devices. Using two dielectric metasurface layers, a compact quantitative phase Gradient microscope that can capture quantitative phase Gradient Images in a single shot is reported with phase Gradient sensitivity better than 92.3 mrad μm^−1 and single-cell resolution.
