This paper proposes a method to display lung PET/CT scans in 3D using a perspective projection technique. The method analyzes DICOM files to calculate standardized uptake values and generate a body mask. Two views are generated using a perspective projection and parallax adjustment to create a stereoscopic 3D effect when viewed with shutter glasses. An experiment shows various views from 0 to 280 degrees. The 3D display expands vision from 2D and helps distinguish depth. Future work involves a medical analysis system with real-time stereoscopic display and auto-stereoscopic capabilities.
Automated fundus image quality assessment and segmentation of optic disc usin...IJECEIAES
An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIMONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics.
Development of a Location Invariant Crack Detection and Localisation Model (L...CSCJournals
Computer vision (CV) -based techniques are being deployed to solve the problem of Crack Detection in metallic and concrete surfaces. This is because the Human-oriented inspections being used have drawbacks in the area of cost and manpower. One of the deployed CV techniques is the Deep Convolutional Neural Network (DCNN). Existing DCNN based crack detection models have a challenge of performing poorly when tested on images taken at a different location from the training images, hence crack localization is required. Thus, this research develops a location invariant crack detection and localization (LICDAL) model in unconstrained oil pipeline images using DCNN. LICDAL is developed by applying transfer learning on the Faster Region based - CNN (Faster R-CNN). The model is made location invariant by gathering images of cracked oil pipeline from various locations. The collected images are split into a 70%:30% ratio for training and testing set. LICDAL is evaluated using the mean Average Precision (mAP). The results on testing LICDAL shows the detected and localised cracks with a mAP of 97.3% on a set of 10 new test images taken from different locations; the highest Average Precision at 99% and the lowest Average Precision at 86%. The performance of LICDAL is compared to an existing crack detection model which detects cracks alone. LICDAL adequately localizes the detected cracks, thus improving crack identification. Secondly, there is no drastic reduction in performance for the test images taken at different locations from the training images, thus making LICDAL location invariant.
For non-grid 3D images like point clouds and meshes, and inherently graph-based data.
Inherently graph-based data include for example brain connectivity analysis, scientific article citation networks, (social) network analysis, etc.
Alternative download link:
https://www.dropbox.com/s/2o3cofcd6d6e2qt/geometricGraph_deepLearning.pdf?dl=0
Image restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
Multimodal RGB-D+RF-based sensing for human movement analysisPetteriTeikariPhD
Combining RGB-D based computer vision with commodity Wifi for pose estimation and human movement analysis for action recognition.
Think of applications especially in healthcare settings, where existing Wifi Access Point already exist and adding USB Wifi dongles to Raspberry Pi (or dedicated chips) is a very easy way to create "operational awareness" of all your patients.
Alternative download link:
https://www.dropbox.com/s/awkqqfhibesjcb9/multimodal_remote_MovementSensing.pdf?dl=0
Automated fundus image quality assessment and segmentation of optic disc usin...IJECEIAES
An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIMONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics.
Development of a Location Invariant Crack Detection and Localisation Model (L...CSCJournals
Computer vision (CV) -based techniques are being deployed to solve the problem of Crack Detection in metallic and concrete surfaces. This is because the Human-oriented inspections being used have drawbacks in the area of cost and manpower. One of the deployed CV techniques is the Deep Convolutional Neural Network (DCNN). Existing DCNN based crack detection models have a challenge of performing poorly when tested on images taken at a different location from the training images, hence crack localization is required. Thus, this research develops a location invariant crack detection and localization (LICDAL) model in unconstrained oil pipeline images using DCNN. LICDAL is developed by applying transfer learning on the Faster Region based - CNN (Faster R-CNN). The model is made location invariant by gathering images of cracked oil pipeline from various locations. The collected images are split into a 70%:30% ratio for training and testing set. LICDAL is evaluated using the mean Average Precision (mAP). The results on testing LICDAL shows the detected and localised cracks with a mAP of 97.3% on a set of 10 new test images taken from different locations; the highest Average Precision at 99% and the lowest Average Precision at 86%. The performance of LICDAL is compared to an existing crack detection model which detects cracks alone. LICDAL adequately localizes the detected cracks, thus improving crack identification. Secondly, there is no drastic reduction in performance for the test images taken at different locations from the training images, thus making LICDAL location invariant.
For non-grid 3D images like point clouds and meshes, and inherently graph-based data.
Inherently graph-based data include for example brain connectivity analysis, scientific article citation networks, (social) network analysis, etc.
Alternative download link:
https://www.dropbox.com/s/2o3cofcd6d6e2qt/geometricGraph_deepLearning.pdf?dl=0
Image restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
Multimodal RGB-D+RF-based sensing for human movement analysisPetteriTeikariPhD
Combining RGB-D based computer vision with commodity Wifi for pose estimation and human movement analysis for action recognition.
Think of applications especially in healthcare settings, where existing Wifi Access Point already exist and adding USB Wifi dongles to Raspberry Pi (or dedicated chips) is a very easy way to create "operational awareness" of all your patients.
Alternative download link:
https://www.dropbox.com/s/awkqqfhibesjcb9/multimodal_remote_MovementSensing.pdf?dl=0
Purkinje imaging for crystalline lens density measurementPetteriTeikariPhD
Brief introduction for the non-invasive, inexpensive and fast Purkinje image -based method for measuring the spectral transmittance of the human crystalline lens density in vivo.
Alternative download link:
https://www.dropbox.com/s/588y7epy13n34xo/purkinje_imaging.pdf?dl=0
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Short intro for some design considerations around hyperspectral retinal imaging. Both for research-grade desktop setups built around supercontinuum laser and AOTF tunable filter, and for mobile low-cost retinal imagers.
Available also from:
https://www.dropbox.com/s/5brchl9ntqno0i9/hyperspectral_retinal_imaging.pdf?dl=0
Practical Considerations in the design of Embedded Ophthalmic DevicesPetteriTeikariPhD
Practical level introduction for ophthalmic device design.
How in the future, multimodal measurement devices will be replacing unimodal devices with simple decision tree indices.
Some examples of embedded cameras, measurement illumination, stimulus presentation illumination are shown.
Alternative download link:
https://www.dropbox.com/s/lt76ohoeusopkoo/practicalConsiderations_embeddedOphthalmicDevices.pdf?dl=0
Using Mask R CNN to Isolate PV Panels from Background Object in Imagesijtsrd
Identifying foreground objects in an image is one of the most common operations used in image processing. In this work, Mask R CNN algorithm is used to identify solar photovoltaic PV panels in aerial images and create a mask that can be used to remove the background from the images. This allows processing the PV panels separately. Using ML to solve this problem can generate more accurate results in comparison to more traditional image processing techniques like using edge detection or Gaussian filtering especially in images where the view might not be easily separable from the objects of interest. The trained model was found to be successful in detecting the PV panels and selecting the pixels that belong to them while ignoring the background pixels. This kind of work can be useful in collecting information about PV installation present in aerial or satellite imagery, or in analyzing the health and integrity of PV modules in large scale installations e.g., in a solar power plant. The results show that this method is effective with a high potential for improved results if the model is trained using larger and more diverse datasets. Muhammet Sait | Atilla Erguzen | Erdal Erdal "Using Mask R-CNN to Isolate PV Panels from Background Object in Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38173.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38173/using-mask-rcnn-to-isolate-pv-panels-from-background-object-in-images/muhammet-sait
Possible future avenues for ophthalmic imaging combining advanced techniques and deep learning. "Bubbling under the surface, and inspiration from ‘bioimaging’ in general"
Machine Learning for Medical Image Analysis:What, where and how?Debdoot Sheet
A great career advice for EECS (Electrical, electronics and computer science) graduates interested in machine vision and some advice for a PhD career in Medical Image Analysis.
From unimodal image classification to integrative multimodal deep learning pipelines in disease classification, disease management and predictive personalised healthcare.
지난주말에 있었던 제 4회 대한신경집중치료학회 편집위원회 워크샵에서 발표했던 내용중에 발췌한 것입니다. 원래 제목은 "인공지능 관련 연구: 논문 작성과 심사에 관한 요령" 입니다. 최근에 deep learning in medical imaging으로 2편의 리뷰와 논문 1편, CADD 논문, 앙상블 논문 1편이 되면서 요청이 온것 같습니다.부족한 제가 하기 어려운 주제를 맡았는데, 혹시 도움이 되실 분이 있으면 도움을 되시라고 올려드립니다. 결론은 인공지능 연구라고 특별히 다르지는 않지만, 공학 연구와 의학연구가 다르고, 인공지능 특성을 잘 이해해야 한다 정도 될것 같습니다. (상당부분 저희병원 박성호 교수님의 radiology 논문 Methodology for Evaluation of Clinical Performance and Impact of Artificial Intelligence Technology for Medical Diagnosis and Prediction을 참고했습니다.)
Most of the existing image recognitions systems are based on physical parameters of the images whereas image processing methodologies relies on extraction of color, shape and edge features. Thus Transfer Learning is an efficient approach of solving classification problem with little amount of data. There are many deep learning algorithms but most tested one is AlexNet. It is well known Convolution Neural Network AlexNet CNN for recognition of images using deep learning. So for recognition and detection of the image we have proposed Deep Learning approach in this project which can analyse thousands of images which may take a lot for a human to do. Pretrained convolutional neural network i.e. AlexNet is trained by using the features such as textures, colors and shape. The model is trained on more than 1000 images and can classify images into categories which we have defined. The trained model is tested on various standard and own recorded datasets consist of rotational, translated and shifted images. Thus when a image is passed to the system it will apply AlexNet and return the results with a image category in which the image lies with high accuracy. Thus our project tends to reduce time and cost of image recognition systems using deep learning. Dr. Sachin K. Korde | Manoj J. Munda | Yogesh B. Chintamani | Yasir L. Pirjade | Akshay V. Gurme "Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31653.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31653/image-classification-using-deep-learning/dr-sachin-k-korde
Time-resolved biomedical sensing through scattering mediumPetteriTeikariPhD
Time-resolved biomedical sensing through scattering medium | Case study with pupillometry through closed eyelids for neurological monitoring
Download link: https://www.dropbox.com/s/x0f5q6cz5ax33s4/timeResolvedSensing.pdf?dl=0
Hardware landscape from computer vision to wearable sensors, and a light intro for UX requirements to ensure adherence and engagement.
At the intersection of new sensors, big data, deep learning, gamification, behavioral medicine and human factors.
Applications benefiting from "quantitative sensorimotor training", "precision exercise", "precision physiotherapy" or whatever you are calling this, include weight and strength training, powerlifting, bodybuilding, martial arts, yoga, dance, musical instrument training, post-surgery rehabilitation for ACL tears, etc.
Alternative download link:
https://www.dropbox.com/s/wcfrzdjkn58xjdq/physio_pipeline_hw.pdf?dl=0
Purkinje imaging for crystalline lens density measurementPetteriTeikariPhD
Brief introduction for the non-invasive, inexpensive and fast Purkinje image -based method for measuring the spectral transmittance of the human crystalline lens density in vivo.
Alternative download link:
https://www.dropbox.com/s/588y7epy13n34xo/purkinje_imaging.pdf?dl=0
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Short intro for some design considerations around hyperspectral retinal imaging. Both for research-grade desktop setups built around supercontinuum laser and AOTF tunable filter, and for mobile low-cost retinal imagers.
Available also from:
https://www.dropbox.com/s/5brchl9ntqno0i9/hyperspectral_retinal_imaging.pdf?dl=0
Practical Considerations in the design of Embedded Ophthalmic DevicesPetteriTeikariPhD
Practical level introduction for ophthalmic device design.
How in the future, multimodal measurement devices will be replacing unimodal devices with simple decision tree indices.
Some examples of embedded cameras, measurement illumination, stimulus presentation illumination are shown.
Alternative download link:
https://www.dropbox.com/s/lt76ohoeusopkoo/practicalConsiderations_embeddedOphthalmicDevices.pdf?dl=0
Using Mask R CNN to Isolate PV Panels from Background Object in Imagesijtsrd
Identifying foreground objects in an image is one of the most common operations used in image processing. In this work, Mask R CNN algorithm is used to identify solar photovoltaic PV panels in aerial images and create a mask that can be used to remove the background from the images. This allows processing the PV panels separately. Using ML to solve this problem can generate more accurate results in comparison to more traditional image processing techniques like using edge detection or Gaussian filtering especially in images where the view might not be easily separable from the objects of interest. The trained model was found to be successful in detecting the PV panels and selecting the pixels that belong to them while ignoring the background pixels. This kind of work can be useful in collecting information about PV installation present in aerial or satellite imagery, or in analyzing the health and integrity of PV modules in large scale installations e.g., in a solar power plant. The results show that this method is effective with a high potential for improved results if the model is trained using larger and more diverse datasets. Muhammet Sait | Atilla Erguzen | Erdal Erdal "Using Mask R-CNN to Isolate PV Panels from Background Object in Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38173.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38173/using-mask-rcnn-to-isolate-pv-panels-from-background-object-in-images/muhammet-sait
Possible future avenues for ophthalmic imaging combining advanced techniques and deep learning. "Bubbling under the surface, and inspiration from ‘bioimaging’ in general"
Machine Learning for Medical Image Analysis:What, where and how?Debdoot Sheet
A great career advice for EECS (Electrical, electronics and computer science) graduates interested in machine vision and some advice for a PhD career in Medical Image Analysis.
From unimodal image classification to integrative multimodal deep learning pipelines in disease classification, disease management and predictive personalised healthcare.
지난주말에 있었던 제 4회 대한신경집중치료학회 편집위원회 워크샵에서 발표했던 내용중에 발췌한 것입니다. 원래 제목은 "인공지능 관련 연구: 논문 작성과 심사에 관한 요령" 입니다. 최근에 deep learning in medical imaging으로 2편의 리뷰와 논문 1편, CADD 논문, 앙상블 논문 1편이 되면서 요청이 온것 같습니다.부족한 제가 하기 어려운 주제를 맡았는데, 혹시 도움이 되실 분이 있으면 도움을 되시라고 올려드립니다. 결론은 인공지능 연구라고 특별히 다르지는 않지만, 공학 연구와 의학연구가 다르고, 인공지능 특성을 잘 이해해야 한다 정도 될것 같습니다. (상당부분 저희병원 박성호 교수님의 radiology 논문 Methodology for Evaluation of Clinical Performance and Impact of Artificial Intelligence Technology for Medical Diagnosis and Prediction을 참고했습니다.)
Most of the existing image recognitions systems are based on physical parameters of the images whereas image processing methodologies relies on extraction of color, shape and edge features. Thus Transfer Learning is an efficient approach of solving classification problem with little amount of data. There are many deep learning algorithms but most tested one is AlexNet. It is well known Convolution Neural Network AlexNet CNN for recognition of images using deep learning. So for recognition and detection of the image we have proposed Deep Learning approach in this project which can analyse thousands of images which may take a lot for a human to do. Pretrained convolutional neural network i.e. AlexNet is trained by using the features such as textures, colors and shape. The model is trained on more than 1000 images and can classify images into categories which we have defined. The trained model is tested on various standard and own recorded datasets consist of rotational, translated and shifted images. Thus when a image is passed to the system it will apply AlexNet and return the results with a image category in which the image lies with high accuracy. Thus our project tends to reduce time and cost of image recognition systems using deep learning. Dr. Sachin K. Korde | Manoj J. Munda | Yogesh B. Chintamani | Yasir L. Pirjade | Akshay V. Gurme "Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31653.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31653/image-classification-using-deep-learning/dr-sachin-k-korde
Time-resolved biomedical sensing through scattering mediumPetteriTeikariPhD
Time-resolved biomedical sensing through scattering medium | Case study with pupillometry through closed eyelids for neurological monitoring
Download link: https://www.dropbox.com/s/x0f5q6cz5ax33s4/timeResolvedSensing.pdf?dl=0
Hardware landscape from computer vision to wearable sensors, and a light intro for UX requirements to ensure adherence and engagement.
At the intersection of new sensors, big data, deep learning, gamification, behavioral medicine and human factors.
Applications benefiting from "quantitative sensorimotor training", "precision exercise", "precision physiotherapy" or whatever you are calling this, include weight and strength training, powerlifting, bodybuilding, martial arts, yoga, dance, musical instrument training, post-surgery rehabilitation for ACL tears, etc.
Alternative download link:
https://www.dropbox.com/s/wcfrzdjkn58xjdq/physio_pipeline_hw.pdf?dl=0
Interest in immersive media increased significantly over recent years. Besides applications in entertainment, culture, health, industry, etc., telepresence and remote collaboration gained importance due to the pandemic and climate crisis. Immersive media have the potential to increase social integration and to reduce greenhouse gas emissions. As a result, technologies along the whole pipeline from capture to display are maturing and applications are becoming available, creating business opportunities. One aspect of immersive technologies that is still relatively undeveloped is the understanding of perception and quality, including subjective and objective assessment. The interactive nature of immersive media poses new challenges to estimation of saliency or visual attention, and to the development of quality metrics. The V-SENSE lab of Trinity College Dublin addresses these questions in current research. This talk will highlight corresponding examples in 360 VR video, light fields, volumetric video and XR.
Recent advances in diagnosis and treatment planning1 /certified fixed orthod...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
Slides by VMD lead developer Mr. John Stone, a pioneer in the field of MD Visualization. Visualization is essential to unlocking key insights from the results of MD simulations. Mr. Stone explains the many GPU-accelerated features of VMD. You can learn how these features can help you speed up a wide range of simulation preparation, analyses, and visualization tasks.
Recent advances in diagnosis and treatment planning1 /certified fixed orthod...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
author: Dr.Hasan A.Ali
content:
introduction
terminology
- advantages and disadvantages
- types of digital radiography
- types of sensors
- uses of computer in digital imaging
- other features of digital imaging
Volumetric medical images contain an enormous amount of visual information that can discourage the exhaustive use of local descriptors for image analysis, comparison and retrieval. Distinctive features and patterns that need to be analyzed for nding diseases are most often local or regional, often in only very small parts of the image. Separating the large amount of image data that might contain little important information is an important task as it could reduce the current information overload of physicians and make clinical work more ecient. In this paper a novel method for detecting key-regions is introduced as a way of extending the concept of keypoints often used in 2D image analysis. In this way also computation is reduced as important visual features are only
extracted from the detected key regions.
The region detection method is integrated into a platform{independent, web-based graphical interface for medical image visualization and retrieval in three dimensions. This web- based interface makes it easy to deploy on existing infrastructures in both small and large-scale clinical environments.
By including the region detection method into the interface, manual annotation is reduced and time is saved, making it possible to integrate the presented interface and methods into clinical routine and work ows, analyzing image data at a large scale.
Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Att...ShuvamRoy12
Roy, A. and Bagade, P. (2024). Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Attention and Reduced ELAN-Based YOLOv7. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-679-8, ISSN 2184-4321, pages 492-499.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
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Stereoscopic Display of Lung PET/CT DICOM Scans using Perspective
1. Stereoscopic Display of Lung PET/
CT DICOM Scans using Perspective
使用透視法於肺部 PET/CT DICOM 影像之三維立體顯示
Student:Yueh-Ju Chen
Advisor: Dr. Tang-Kai Yin
Institute of Computer Science and Information Engineering
National University of Kaohsiung
2. • Introduction
• Motivation
• Related Works
• Proposed Approach
• DICOM Analysis
• Stereoscopic
• Result
• Conclusion and Future Work
Institute of Computer Science and Information Engineering
2 National University of Kaohsiung
3. Introduction
Institute of Computer Science and Information Engineering
National University of Kaohsiung
4. Background
• Computed tomography:
• A medical imaging procedure
• Utilizing computer-processed
X-rays to produce tomographic
images or 'slices' of specific
areas of the body
• Stereoscopy:
• The illusion 3D depth from images on a 2D plane
Institute of Computer Science and Information Engineering
4 National University of Kaohsiung
5. Motivation
• 3D effect get popular
• Not a new technology
• Medical diagnosis:
• Improve the efficiency of diagnosis
• Surgical simulation
• Education training
Institute of Computer Science and Information Engineering
5 National University of Kaohsiung
6. Algorithm
Institute of Computer Science and Information Engineering
National University of Kaohsiung
7. Related Works
Institute of Computer Science and Information Engineering
National University of Kaohsiung
8. Medical Scans
• DICOM file:
• File format
• DICOM Standards Committee
• Widely used by hospitals
• Dividing into two parts:
• Image
• Header (metadata) files
Institute of Computer Science and Information Engineering
8 National University of Kaohsiung
9. Maximum Intensity
Projection (MIP)
• Volume rendering method
• Voxels with maximum intensity
• Orthographic projection
• Cannot distinguish between left or right,
front or back
• Detection of lung nodules
Institute of Computer Science and Information Engineering
9 National University of Kaohsiung
10. Standardized Uptake
Value (SUV)
• Distinguish between malignant lesions and
benign tumor
• Enhances the 3D nature of nodules
• Pulmonary bronchi and vasculature
• Cut-off value for malignant lesion is 2.5
Institute of Computer Science and Information Engineering
10 National University of Kaohsiung
11. Stereoscopic
• Illusion of 3D depth from images on a 2D
plane
• 3D viewer technology:
• Anaglyph
• Active shutter systems
• Polarization systems
Institute of Computer Science and Information Engineering
11 National University of Kaohsiung
12. NVIDIA 3D Vision
• Shutter glasses and driver software
• Direct3D software
• Mainstream consumers and PC gamers
• Requirement:
• 120 Hz LCD or CRT
monitors, DLP-projectors
• NVIDIA graphics card
Institute of Computer Science and Information Engineering
12 National University of Kaohsiung
13. Proposed Approach
Institute of Computer Science and Information Engineering
National University of Kaohsiung
14. DICOM Analysis
Institute of Computer Science and Information Engineering
National University of Kaohsiung
15. Calculation of SUV
• Referred to as the dose uptake ratio
• Main calculate source: PET DICOM
• The related attributed tags:
• Rescale Slope tag
• Rescale Intercept tag
Institute of Computer Science and Information Engineering
15 National University of Kaohsiung
16. Step 1: Convert Pixel Value to
Activity Concentration
• Rescale Slope tag are vary for every image
slice
Institute of Computer Science and Information Engineering
16 National University of Kaohsiung
17. Step 2: Decay Calibration Factor
Institute of Computer Science and Information Engineering
17 National University of Kaohsiung
18. Step 3: Calculate SUV
Institute of Computer Science and Information Engineering
18 National University of Kaohsiung
19. SUV
Number 147 and 195 slice (a)original PET (b)SUV (c) threshold with 2.5
Institute of Computer Science and Information Engineering
National University of Kaohsiung
20. Non-body Pixels
Non-body
Original CT
pixels removed
Institute of Computer Science and Information Engineering
20 National University of Kaohsiung
21. Body Mask
Institute of Computer Science and Information Engineering
21 National University of Kaohsiung
22. Body Mask (cont.)
Institute of Computer Science and Information Engineering
22 National University of Kaohsiung
23. Stereoscopic
Institute of Computer Science and Information Engineering
National University of Kaohsiung
24. Real 3D vs. Fake 3D
• Fake 3D
• Converting 2D films into 3D
• Depth map
• Real 3D
• Two views
• Two different cameras (or projections)
Institute of Computer Science and Information Engineering
24 National University of Kaohsiung
25. Stereoscopic Principle
• D: distance between the
viewpoint and the screen
• R: distance of stereo pair
of images on the screen
• S: distance between the
perceived object and the
screen
Institute of Computer Science and Information Engineering
25 National University of Kaohsiung
26. Optical Angle
• Best viewing range:
70~500cm
• Distance between the
two eyes: 6.5~7cm
•
Institute of Computer Science and Information Engineering
26 National University of Kaohsiung
27. Eye Separation
• The most suitable for the 3D effect:
• Distance: 140~210 cm
• Optical angle: 2.86~1.91 degree
Institute of Computer Science and Information Engineering
27 National University of Kaohsiung
28. Perspective
Institute of Computer Science and Information Engineering
28 National University of Kaohsiung
29. Perspective
Institute of Computer Science and Information Engineering
29 National University of Kaohsiung
30. Perspective
Institute of Computer Science and Information Engineering
30 National University of Kaohsiung
31. Perspective
Institute of Computer Science and Information Engineering
31 National University of Kaohsiung
32. Parallax
Institute of Computer Science and Information Engineering
National University of Kaohsiung
33. Positive and Zero
Parallax
• Positive Parallax
• Cross point of each eye sight with the
screen
• Behind the screen
• Zero Parallax
• Overlap on the screen
• No 3D effect
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33 National University of Kaohsiung
34. Negative Parallax
• Focus heating-point in front of the screen
• Out of screen
• The closer the screen is the smaller depth of
field will be generated
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34 National University of Kaohsiung
35. Parallax Adjustment
Institute of Computer Science and Information Engineering
National University of Kaohsiung
36. Result
Institute of Computer Science and Information Engineering
National University of Kaohsiung
37. NVIDIA 3D
Vision
1. OpenGL QuadBuffer
2. NVAPI
3. 3D Video
Institute of Computer Science and Information Engineering
37 National University of Kaohsiung
38. Experiment
Environment
ASUS G53J
Platform: Win7 64bit
CPU: Intel i7-7400QM @1.73GHz
RAM: 8GM
Graphic Card: NVIDIA GTX 460M
1.5G
Institute of Computer Science and Information Engineering
38 National University of Kaohsiung
39. MIP
0 degree of MIP
100 degree of MIP
Institute of Computer Science and Information Engineering
39 National University of Kaohsiung
40. MIP
240 degree of MIP
280 degree of MIP
Institute of Computer Science and Information Engineering
40 National University of Kaohsiung
41. Perspective of Mean Volume
Rendering
0 degree of Perspective of Mean Volume Rendering
100 degree of Perspective of Mean Volume Rendering
Institute of Computer Science and Information Engineering
National University of Kaohsiung
42. Perspective of Mean Volume
Rendering
240 degree of Perspective of Mean Volume Rendering
280 degree of Perspective of Mean Volume Rendering
Institute of Computer Science and Information Engineering
National University of Kaohsiung
43. Perspective with Bilinear
Interpolation
0 degree of Perspective with Bilinear Interpolation
100 degree of Perspective with Bilinear Interpolation
Institute of Computer Science and Information Engineering
National University of Kaohsiung
44. Perspective with Bilinear
Interpolation
240 degree of Perspective with Bilinear Interpolation
280 degree of Perspective with Bilinear Interpolation
Institute of Computer Science and Information Engineering
National University of Kaohsiung
45. Conclusion
&
Future Work
Institute of Computer Science and Information Engineering
National University of Kaohsiung
46. Conclusion
• The stereoscopic image created by a perspective
projection and applied with SUV calculation of PET
scans in 3D shutter system.
• Expending vision from 2D to 3D and the biggest
difference between them is the depth information.
• 2D images cannot distinguish three points which is
in the front or which is in the back, but after we
applied the stereoscopic techniques it is more
obvious and clear to distinguish points’ relation and
distance between them
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46 National University of Kaohsiung
47. Future Work
• A medical analyze system with stereoscopic
display
• More medical analyze functions
• Real time volume rendering
• Auto-stereoscopic
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47 National University of Kaohsiung
48. Demo
Institute of Computer Science and Information Engineering
National University of Kaohsiung
49. Q &A
Institute of Computer Science and Information Engineering
National University of Kaohsiung