1. Simultaneous multi-slice imaging provides robust slice-level motion tracking for diffusion-weighted MRI by acquiring multiple coupled image planes simultaneously.
2. A novel registration-based motion tracking technique is proposed that uses simultaneous multi-slice acquisition and slice-to-volume registration to estimate and correct for motion, enabling reconstruction of neural microstructures in moving subjects.
3. The technique detects and rejects motion-corrupted images, tracks motion using simultaneous multi-slice registration, and performs robust reconstruction of diffusion tensors from motion-corrected data.
Iaetsd a modified image fusion approach using guided filterIaetsd Iaetsd
This document proposes a modified image fusion approach using guided filters to combine images. It involves:
1. Decomposing the input images into base and detail layers using simple average filtering.
2. Generating guided weight maps for the base and detail layers of each input image using saliency maps and guided filtering.
3. Reconstructing the fused image by weighted summation of the base and detail layers using the guided weight maps.
The proposed method aims to preserve edge information better than other methods by exploiting spatial context with guided filters during the fusion process. It is compared to other methods based on quality assessment results.
Iaetsd an enhanced circular detection technique rpsw using circular hough t...Iaetsd Iaetsd
This document proposes a new technique for detecting circular features in satellite images that combines Rotational Pixel Swapping (RPSW) and Circular Hough Transform. RPSW enhances circular patterns by multiplying the original image with rotated versions. Circular Hough Transform then identifies the center and radius of circles. Applying both allows detecting simple and complex circles while finding the area, improving on existing methods. RPSW alone only identifies circle centers, so the addition of Circular Hough Transform provides radius and area information. The combined approach accurately detects circular features from planetary images.
This document discusses techniques for detecting digital image forgeries. It begins by defining different types of forgeries such as image retouching, splicing, and cloning. It then discusses mechanisms for forgery detection, distinguishing between active methods that embed hidden information in images and passive methods that analyze image traces. A key technique presented is using rotation angle estimation to detect cloned regions, with details on calculating variance to determine the rotation angle. The document concludes by presenting an algorithm for region duplication detection using hybrid wavelet transforms like DCT, Walsh, and Hadamard transforms.
Region duplication forgery detection in digital imagesRupesh Ambatwad
Region duplication or copy move forgery is a common type of tampering scheme carried out to create a fake image. The field on blind image forensics depends upon the authenticity of the digital image. As in copy move forgery the duplicated region belongs to the same image, the detection of tampering is complex as it does not leave a visual clue. But the tampering gives rise to glitches at pixel level
This document summarizes a research paper on real-time stereo mosaicing using feature tracking. The key points are:
- It proposes aligning video frames in a space-time volume based on efficient feature tracking, specifically kernel tracking, to compute accurate 3D motion faster than previous direct approaches.
- It presents a new "Barcode Blending" approach for efficient pyramid blending of multiple narrow mosaic strips into a single blending step.
- The entire stereo mosaicing process is highly efficient in both computation and memory usage, making it suitable for mobile devices.
Copy-Rotate-Move Forgery Detection Based on Spatial DomainSondosFadl
we propose a method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Iaetsd a modified image fusion approach using guided filterIaetsd Iaetsd
This document proposes a modified image fusion approach using guided filters to combine images. It involves:
1. Decomposing the input images into base and detail layers using simple average filtering.
2. Generating guided weight maps for the base and detail layers of each input image using saliency maps and guided filtering.
3. Reconstructing the fused image by weighted summation of the base and detail layers using the guided weight maps.
The proposed method aims to preserve edge information better than other methods by exploiting spatial context with guided filters during the fusion process. It is compared to other methods based on quality assessment results.
Iaetsd an enhanced circular detection technique rpsw using circular hough t...Iaetsd Iaetsd
This document proposes a new technique for detecting circular features in satellite images that combines Rotational Pixel Swapping (RPSW) and Circular Hough Transform. RPSW enhances circular patterns by multiplying the original image with rotated versions. Circular Hough Transform then identifies the center and radius of circles. Applying both allows detecting simple and complex circles while finding the area, improving on existing methods. RPSW alone only identifies circle centers, so the addition of Circular Hough Transform provides radius and area information. The combined approach accurately detects circular features from planetary images.
This document discusses techniques for detecting digital image forgeries. It begins by defining different types of forgeries such as image retouching, splicing, and cloning. It then discusses mechanisms for forgery detection, distinguishing between active methods that embed hidden information in images and passive methods that analyze image traces. A key technique presented is using rotation angle estimation to detect cloned regions, with details on calculating variance to determine the rotation angle. The document concludes by presenting an algorithm for region duplication detection using hybrid wavelet transforms like DCT, Walsh, and Hadamard transforms.
Region duplication forgery detection in digital imagesRupesh Ambatwad
Region duplication or copy move forgery is a common type of tampering scheme carried out to create a fake image. The field on blind image forensics depends upon the authenticity of the digital image. As in copy move forgery the duplicated region belongs to the same image, the detection of tampering is complex as it does not leave a visual clue. But the tampering gives rise to glitches at pixel level
This document summarizes a research paper on real-time stereo mosaicing using feature tracking. The key points are:
- It proposes aligning video frames in a space-time volume based on efficient feature tracking, specifically kernel tracking, to compute accurate 3D motion faster than previous direct approaches.
- It presents a new "Barcode Blending" approach for efficient pyramid blending of multiple narrow mosaic strips into a single blending step.
- The entire stereo mosaicing process is highly efficient in both computation and memory usage, making it suitable for mobile devices.
Copy-Rotate-Move Forgery Detection Based on Spatial DomainSondosFadl
we propose a method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHODeditorijcres
AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR
Department of Computer Science and Engineering
Babu Banarasi Das University, Lucknow
akhi2232232@gmail.com, deenbandhusingh85@gmail.com, vivek.kumar0091@gmail.com
ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc.
INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.
Andrey Kuznetsov and Vladislav Myasnikov - Using Efficient Linear Local Feat...AIST
The document proposes a new copy-move forgery detection algorithm using efficient linear local features. The key features of the algorithm are 100% recall, high calculation speed for real-time analysis, and 99.9% precision. It works by using a sliding window to analyze image fragments defined by a structural pattern. Hash values are calculated for each fragment based on linear local features and stored in a hash table. Duplicates are identified by finding hash values with a frequency greater than one. Experiments on satellite images show the algorithm has no false negatives and very low false positives, while being much faster than other methods.
This document presents research on detecting image splicing in images shared on the web. The researchers collected a dataset of over 13,000 real-world forged images from 82 verified cases found online. They evaluated existing splicing detection algorithms on this dataset and found that most algorithms could only detect a small fraction of forgeries. The noise-based method of Mahdian et al. was most successful but also prone to false positives. The researchers conclude that forensic traces are often lost due to the many alterations images undergo when shared online, posing challenges for splicing detection in real-world web images.
Blind detection of image manipulation @ PoliMiGiorgio Sironi
The document discusses various techniques for the blind detection of image manipulation without the use of digital watermarks. It outlines pixel-based, format-based, camera-based, physics-based, and geometric-based approaches. It focuses on the use of projective geometry tools and geometric-based techniques like analyzing the assumptions of manual text selection and rectification to known fonts or objects to detect tampering. Key steps involve finding keypoints with SIFT feature detection, matching keypoint pairs with RANSAC, and comparing rectified images to reference samples to judge manipulation.
The document proposes a new method for efficient high resolution image reconstruction based on compressed sensing and the Modified Frame Reconstruction Iterative Thresholding Algorithm (MFR ITA). The method involves three phases: 1) input images are processed using multilook processing and discrete wavelet transform to minimize noise, 2) measurements are obtained from sparse coefficients using a proposed fusion method, and 3) a fast compressed sensing method based on MFR ITA is used to reconstruct the high resolution image using total variation. Simulation results show the proposed method achieves better PSNR and SSIM values compared to other traditional methods, and validates its effectiveness in reconstructing images in the presence of noise.
Tissue Segmentation Methods Using 2D Histogram Matching in a Sequence of MR B...Vladimir Kanchev
This presentation provides detailed description of the methodology of the segmentation method of brain tissues in MR image sequences using 2D histogram matching.
This document describes a framework for 2D pose estimation using active shape models and learned entropy field approximations. A dataset of manually annotated poses was created from NBA footage to train the models. Active shape models use principal component analysis to represent poses as a linear combination of modes of variation learned from the training data. To evaluate pose likelihood, image entropy is proposed as a texture similarity measure and regression is used to learn a function mapping poses to entropy fields, which can be compared to the image entropy. Current results are presented and future work to improve and speed up the approach is discussed.
3D Reconstruction from Multiple uncalibrated 2D Images of an ObjectAnkur Tyagi
3D reconstruction is the process of capturing the shape and appearance of real objects. In this project we are using passive methods which only use sensors to measure the radiance reflected or emitted by the objects surface to infer its 3D structure.
FAN search for image copy-move forgery-amalta 2014SondosFadl
1) The document proposes a fast fan search method for detecting copy-move image forgery. It divides images into blocks, extracts features from blocks, and uses a fan search algorithm to detect duplicated blocks more efficiently than previous methods.
2) Experimental results show the proposed method can detect copy-move forgery 75% faster than other methods, with 99% precision and 98% recall.
3) Future work will improve the method to detect duplications under geometric transformations like rotation and scaling.
Analysis and Detection of Image Forgery Methodologiesijsrd.com
"Forgery" is a subjective word. An image can become a forgery based upon the context in which it is used. An image altered for fun or someone who has taken a bad photo, but has been altered to improve its appearance cannot be considered a forgery even though it has been altered from its original capture. The other side of forgery are those who perpetuate a forgery for gain and prestige. They create an image in which to dupe the recipient into believing the image is real and from this they are able to gain payment and fame. Detecting these types of forgeries has become serious problem at present. To determine whether a digital image is original or doctored is a big challenge. To find the marks of tampering in a digital image is a challenging task. Now these marks of tampering can be done by various operations such as rotation, scaling, JPEG compression, Gaussian noise etc. called as attacks. There are various methods proposed in this field in recent years to detect above mentioned attacks. This paper provides a detailed analysis of different approaches and methodologies used to detect image forgery. It is also analysed that block-based features methods are robust to Gaussian noise and JPEG compression and the key point-based feature methods are robust to rotation and scaling.
[PDF] Automatic Image Co-segmentation Using Geometric Mean Saliency (Top 10% ...Koteswar Rao Jerripothula
Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing single-image saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.
This document summarizes techniques for detecting tampered digital images. It discusses passive ("blind") methods that detect forgeries by analyzing the statistical properties and digital fingerprints of images without prior knowledge. These techniques examine inconsistencies introduced during tampering that alter the image's noise, compression, color, and other attributes. The document also outlines different types of forgeries like copy-move, splicing, retouching, and techniques using JPEG compression and lighting analysis. It reviews papers on demosaicing regularity detection and noise variation analysis for passive forgery identification.
This document describes a project to calibrate a camera using a calibration rig. Intrinsic and extrinsic camera parameters were calculated. Image and world coordinates of points on the calibration rig were collected. A projection matrix was calculated from the coordinates and used to determine the intrinsic parameters like focal length and extrinsic parameters like rotation and translation. The estimated image coordinates from the projection matrix were compared to measured coordinates to calculate errors, which improved when more points were used.
Two Dimensional Image Reconstruction Algorithmsmastersrihari
This document discusses two-dimensional image reconstruction algorithms. It begins with an introduction to image projections and reconstruction. It then describes different types of projections like parallel beam, fan beam, and truncated projections. It discusses the convolution back projection algorithm and its digital implementation. Results are shown for different filters. Applications include medical imaging. Present research focuses on limited data reconstruction. The document concludes that image reconstruction is an ill-posed problem.
Lec11: Active Contour and Level Set for Medical Image SegmentationUlaş Bağcı
ActiveContour(Snake) • LevelSet
• Applications
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energyfunctional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
This document discusses image segmentation techniques. It begins by introducing the goal of image segmentation as clustering pixels into salient image regions. Segmentation can be used for tasks like object recognition, image compression, and image editing. The document then discusses several bottom-up image segmentation approaches, including clustering pixels in feature space using mixtures of Gaussians models or K-means, mean-shift segmentation which models feature density non-parametrically, and graph-based segmentation methods which construct similarity graphs between pixels. It provides examples and discusses assumptions and limitations of each approach. The key approaches discussed are clustering in feature space, mean-shift segmentation, and graph-based similarity methods like the local variation algorithm.
The students can learn about basics of image processing using matlab.
It explains the image operations with the help of examples and Matlab codes.
Students can fine sample images and .m code from the link given in slides.
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
This document proposes an object detection technique for aerial videos based on motion vector compensation and statistical analysis. It begins with an introduction to the importance of object detection in aerial surveillance. It then describes the characteristics of aerial video images and a preprocessing method using Bayesian wavelet denoising. A compensation of motion vectors is performed using camera motion estimation. Statistical analysis and clustering of compensated motion vectors is used to detect objects and eliminate isolated vectors. The method is tested on a road surveillance video, showing it can effectively detect objects after noise removal and motion vector processing.
Este documento describe diferentes tipos de familias y cómo influyen en el desarrollo de los niños. Describe cuatro modelos familiares (permisivo, autoritario, democrático y negligente) y sus características. También discute factores como la estructura familiar, el género de los padres, la separación de padres, la interculturalidad y la adopción que pueden afectar el desarrollo infantil.
La danza es una forma de arte que expresa ideas y emociones a través de la interacción de fuerzas creadas para nuestra percepción, resultando en imágenes dinámicas. Aunque se basa en elementos físicos como el cuerpo y la gravedad, la danza los convierte en aspectos inmateriales para deleitar al espectador con una expresión del conocimiento humano sobre los sentimientos.
FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHODeditorijcres
AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR
Department of Computer Science and Engineering
Babu Banarasi Das University, Lucknow
akhi2232232@gmail.com, deenbandhusingh85@gmail.com, vivek.kumar0091@gmail.com
ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc.
INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.
Andrey Kuznetsov and Vladislav Myasnikov - Using Efficient Linear Local Feat...AIST
The document proposes a new copy-move forgery detection algorithm using efficient linear local features. The key features of the algorithm are 100% recall, high calculation speed for real-time analysis, and 99.9% precision. It works by using a sliding window to analyze image fragments defined by a structural pattern. Hash values are calculated for each fragment based on linear local features and stored in a hash table. Duplicates are identified by finding hash values with a frequency greater than one. Experiments on satellite images show the algorithm has no false negatives and very low false positives, while being much faster than other methods.
This document presents research on detecting image splicing in images shared on the web. The researchers collected a dataset of over 13,000 real-world forged images from 82 verified cases found online. They evaluated existing splicing detection algorithms on this dataset and found that most algorithms could only detect a small fraction of forgeries. The noise-based method of Mahdian et al. was most successful but also prone to false positives. The researchers conclude that forensic traces are often lost due to the many alterations images undergo when shared online, posing challenges for splicing detection in real-world web images.
Blind detection of image manipulation @ PoliMiGiorgio Sironi
The document discusses various techniques for the blind detection of image manipulation without the use of digital watermarks. It outlines pixel-based, format-based, camera-based, physics-based, and geometric-based approaches. It focuses on the use of projective geometry tools and geometric-based techniques like analyzing the assumptions of manual text selection and rectification to known fonts or objects to detect tampering. Key steps involve finding keypoints with SIFT feature detection, matching keypoint pairs with RANSAC, and comparing rectified images to reference samples to judge manipulation.
The document proposes a new method for efficient high resolution image reconstruction based on compressed sensing and the Modified Frame Reconstruction Iterative Thresholding Algorithm (MFR ITA). The method involves three phases: 1) input images are processed using multilook processing and discrete wavelet transform to minimize noise, 2) measurements are obtained from sparse coefficients using a proposed fusion method, and 3) a fast compressed sensing method based on MFR ITA is used to reconstruct the high resolution image using total variation. Simulation results show the proposed method achieves better PSNR and SSIM values compared to other traditional methods, and validates its effectiveness in reconstructing images in the presence of noise.
Tissue Segmentation Methods Using 2D Histogram Matching in a Sequence of MR B...Vladimir Kanchev
This presentation provides detailed description of the methodology of the segmentation method of brain tissues in MR image sequences using 2D histogram matching.
This document describes a framework for 2D pose estimation using active shape models and learned entropy field approximations. A dataset of manually annotated poses was created from NBA footage to train the models. Active shape models use principal component analysis to represent poses as a linear combination of modes of variation learned from the training data. To evaluate pose likelihood, image entropy is proposed as a texture similarity measure and regression is used to learn a function mapping poses to entropy fields, which can be compared to the image entropy. Current results are presented and future work to improve and speed up the approach is discussed.
3D Reconstruction from Multiple uncalibrated 2D Images of an ObjectAnkur Tyagi
3D reconstruction is the process of capturing the shape and appearance of real objects. In this project we are using passive methods which only use sensors to measure the radiance reflected or emitted by the objects surface to infer its 3D structure.
FAN search for image copy-move forgery-amalta 2014SondosFadl
1) The document proposes a fast fan search method for detecting copy-move image forgery. It divides images into blocks, extracts features from blocks, and uses a fan search algorithm to detect duplicated blocks more efficiently than previous methods.
2) Experimental results show the proposed method can detect copy-move forgery 75% faster than other methods, with 99% precision and 98% recall.
3) Future work will improve the method to detect duplications under geometric transformations like rotation and scaling.
Analysis and Detection of Image Forgery Methodologiesijsrd.com
"Forgery" is a subjective word. An image can become a forgery based upon the context in which it is used. An image altered for fun or someone who has taken a bad photo, but has been altered to improve its appearance cannot be considered a forgery even though it has been altered from its original capture. The other side of forgery are those who perpetuate a forgery for gain and prestige. They create an image in which to dupe the recipient into believing the image is real and from this they are able to gain payment and fame. Detecting these types of forgeries has become serious problem at present. To determine whether a digital image is original or doctored is a big challenge. To find the marks of tampering in a digital image is a challenging task. Now these marks of tampering can be done by various operations such as rotation, scaling, JPEG compression, Gaussian noise etc. called as attacks. There are various methods proposed in this field in recent years to detect above mentioned attacks. This paper provides a detailed analysis of different approaches and methodologies used to detect image forgery. It is also analysed that block-based features methods are robust to Gaussian noise and JPEG compression and the key point-based feature methods are robust to rotation and scaling.
[PDF] Automatic Image Co-segmentation Using Geometric Mean Saliency (Top 10% ...Koteswar Rao Jerripothula
Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing single-image saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.
This document summarizes techniques for detecting tampered digital images. It discusses passive ("blind") methods that detect forgeries by analyzing the statistical properties and digital fingerprints of images without prior knowledge. These techniques examine inconsistencies introduced during tampering that alter the image's noise, compression, color, and other attributes. The document also outlines different types of forgeries like copy-move, splicing, retouching, and techniques using JPEG compression and lighting analysis. It reviews papers on demosaicing regularity detection and noise variation analysis for passive forgery identification.
This document describes a project to calibrate a camera using a calibration rig. Intrinsic and extrinsic camera parameters were calculated. Image and world coordinates of points on the calibration rig were collected. A projection matrix was calculated from the coordinates and used to determine the intrinsic parameters like focal length and extrinsic parameters like rotation and translation. The estimated image coordinates from the projection matrix were compared to measured coordinates to calculate errors, which improved when more points were used.
Two Dimensional Image Reconstruction Algorithmsmastersrihari
This document discusses two-dimensional image reconstruction algorithms. It begins with an introduction to image projections and reconstruction. It then describes different types of projections like parallel beam, fan beam, and truncated projections. It discusses the convolution back projection algorithm and its digital implementation. Results are shown for different filters. Applications include medical imaging. Present research focuses on limited data reconstruction. The document concludes that image reconstruction is an ill-posed problem.
Lec11: Active Contour and Level Set for Medical Image SegmentationUlaş Bağcı
ActiveContour(Snake) • LevelSet
• Applications
Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energyfunctional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
This document discusses image segmentation techniques. It begins by introducing the goal of image segmentation as clustering pixels into salient image regions. Segmentation can be used for tasks like object recognition, image compression, and image editing. The document then discusses several bottom-up image segmentation approaches, including clustering pixels in feature space using mixtures of Gaussians models or K-means, mean-shift segmentation which models feature density non-parametrically, and graph-based segmentation methods which construct similarity graphs between pixels. It provides examples and discusses assumptions and limitations of each approach. The key approaches discussed are clustering in feature space, mean-shift segmentation, and graph-based similarity methods like the local variation algorithm.
The students can learn about basics of image processing using matlab.
It explains the image operations with the help of examples and Matlab codes.
Students can fine sample images and .m code from the link given in slides.
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
This document proposes an object detection technique for aerial videos based on motion vector compensation and statistical analysis. It begins with an introduction to the importance of object detection in aerial surveillance. It then describes the characteristics of aerial video images and a preprocessing method using Bayesian wavelet denoising. A compensation of motion vectors is performed using camera motion estimation. Statistical analysis and clustering of compensated motion vectors is used to detect objects and eliminate isolated vectors. The method is tested on a road surveillance video, showing it can effectively detect objects after noise removal and motion vector processing.
Este documento describe diferentes tipos de familias y cómo influyen en el desarrollo de los niños. Describe cuatro modelos familiares (permisivo, autoritario, democrático y negligente) y sus características. También discute factores como la estructura familiar, el género de los padres, la separación de padres, la interculturalidad y la adopción que pueden afectar el desarrollo infantil.
La danza es una forma de arte que expresa ideas y emociones a través de la interacción de fuerzas creadas para nuestra percepción, resultando en imágenes dinámicas. Aunque se basa en elementos físicos como el cuerpo y la gravedad, la danza los convierte en aspectos inmateriales para deleitar al espectador con una expresión del conocimiento humano sobre los sentimientos.
This document summarizes research on developing non-pneumatic cellular lunar wheel prototypes. It describes generating wheel concepts with cellular structures for lightweight and variable mechanical properties. Simulations and compression tests were performed on sample prototypes, with errors found between simulated and tested properties. Larger prototypes were manufactured and recommendations made to improve traction, consider non-linear behavior, and investigate high fatigue materials. The best final prototype balanced manufacturability, mechanical properties, weight and deformability.
The document discusses a class with 3 main points. It introduces the topic of the class in the first point. The second point provides further details about the class content. The third point concludes the overview of the class.
Este documento describe varias amenazas a la seguridad informática como pharming, phising, virus, troyanos, gusanos, spam, dialers y spiner. También explica dos tipos de seguridad: la seguridad pasiva, que minimiza los efectos de incidentes, y la seguridad activa, que evita daños mediante contraseñas, encriptación y software de seguridad.
The presenter discusses how they structure a workshop introducing teachers to the Next Generation Science Standards (NGSS). They typically include: 1) motivation for NGSS, 2) components of NGSS, 3) hands-on example of unpacking a performance expectation, 4) strengths, weaknesses, opportunities, and threats (SWOT) analysis of NGSS, 5) developing an NGSS curriculum, and 6) a content-based professional development example like climate change. They then provide background information on the development of science standards in the U.S. and a brief history of standards leading up to NGSS.
Este documento ofrece una variedad de servicios de diseño web, incluyendo tiendas en línea autogestionables, blogs, páginas web, y productos audiovisuales. Se describen las características y beneficios de cada servicio, como tiendas en línea con múltiples idiomas y opciones de acceso seguro para clientes, blogs libres de publicidad externa con posicionamiento SEO, y videos y anuncios de radio y televisión profesionales.
Este documento describe diferentes tipos de familias y cómo influyen en el desarrollo de los niños. Identifica cuatro modelos familiares (permisivo, autoritario, democrático y negligente) y sus características. También discute factores como la estructura familiar, factores de riesgo y protección, y diversidad familiar que afectan el desarrollo infantil.
El documento describe cómo trazar un toro usando el método de revolución en un programa 3D como AutoCAD. También explica cómo dibujar un toro en isometría con datos de planta y alzado, levantando ejes y trazando elipses auxiliares.
El documento describe las políticas de asistencia para estudiantes y cómo usar la plataforma estudiantil Genesis. Los estudiantes pueden perder hasta el 15% de las clases por fallas injustificadas y hasta el 25% por fallas justificadas si presentan un comprobante. Luego, el documento explica los pasos para ingresar a Genesis y revisar horarios, calificaciones y configuración personal.
Este documento describe los diferentes tipos de familia, las características de los descendientes según el estilo parental y los factores que influyen en el desarrollo infantil. Explica que la familia nuclear, extensa, monoparental y homoparental influyen en el desarrollo del niño a través de la adquisición de normas y conductas. Además, factores como el estilo parental, socioeconómicos y genéticos tienen un impacto en los procesos educativos y de aprendizaje del niño.
Este documento presenta la unidad IV sobre el estudio financiero de un proyecto de inversión. Explica conceptos clave como la determinación de costos de producción, administración, venta y financieros. También describe el cronograma de inversiones, cálculo de depreciaciones, amortizaciones y capital de trabajo. El objetivo es analizar la viabilidad financiera de un proyecto mediante la estimación de ingresos, egresos y utilidades pro forma.
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Experienced customer service professional with over 15 years of experience in food service and retail. Skilled at developing rapport with clients, prioritizing tasks, and working well under pressure. Seeks new opportunities in human resources utilizing strong communication, problem-solving, and training experience.
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CMC Africa - transforming lives by imparting skills that University Students will require to either manage their own businesses or land their first jobs after the University. Check out more on our website: http://cmcafrica.com/
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Gabor filter is a powerful way to enhance biometric images like fingerprint images in order to extract correct features from these images, Gabor filter used in extracting features directly asin iris images, and sometimes Gabor filter has been used for texture analysis. In fingerprint images The even symmetric Gabor filter is contextual filter or multi-resolution filter will be used to enhance fingerprint imageby filling small gaps (low-pass effect) in the direction of the ridge (black regions) and to increase the discrimination between ridge and valley (black and white regions) in the direction, orthogonal to the ridge, the proposed method in applying Gabor filter on fingerprint images depending on translated fingerprint image into binary image after applying some simple enhancing methods to partially overcome time consuming problem of the Gabor filter.
Dense Visual Odometry Using Genetic AlgorithmSlimane Djema
Our work aims to estimate the camera motion mounted on the head of a mobile robot or a moving object from RGB-D images in a static scene. The problem of motion estimation is transformed into a nonlinear least squares function. Methods for solving such problems are iterative. Various classic methods gave an iterative solution by linearizing this function. We can also use the metaheuristic optimization method to solve this problem and improve results. In this paper, a new algorithm is developed for visual odometry using a sequence of RGB-D images. This algorithm is based on a genetic algorithm. The proposed iterative genetic algorithm searches using particles to estimate the optimal motion and then compares it to the traditional methods. To evaluate our method, we use the root mean square error to compare it with the based energy method and another metaheuristic method. We prove the efficiency of our innovative algorithm on a large set of images.
Digital images can be manipulated mathematically by treating pixel brightness values as numbers. This document discusses various digital image processing techniques including:
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The document presents a method for detecting copy-move forgery in digital images using center-symmetric local binary pattern (CS-LBP). The key steps are:
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The
Hybrid medical image compression method using quincunx wavelet and geometric ...journalBEEI
The purpose of this article is to find an efficient and optimal method of compression by reducing the file size while retaining the information for a good quality processing and to produce credible pathological reports, based on the extraction of the information characteristics contained in medical images. In this article, we proposed a novel medical image compression that combines geometric active contour model and quincunx wavelet transform. In this method it is necessary to localize the region of interest, where we tried to localize all the part that contain the pathological, using the level set for an optimal reduction, then we use the quincunx wavelet coupled with the set partitioning in hierarchical trees (SPIHT) algorithm. After testing several algorithms we noticed that the proposed method gives satisfactory results. The comparison of the experimental results is based on parameters of evaluation.
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Scaling Transform Methods For Compressing a 2D Graphical imageacijjournal
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coordinates either by adding some values with original coordinates(Translations) or Multiplying some
values with original coordinates(called Linear transformations like rotation, reflection, scaling, and
shearing). In this paper, we compress a two dimensional picture using 2D scaling transformation. In the
several scenarios, the utilization of this technique for image compression resulted in comparable or better
performance, when compared to the Different modes of image transformations. In this paper We tried a
new code for compressing an 2d gray scale image using scaling transform methods. Matlab concepts are
applied to compress the image. We have plan to apply The techniques and develop a code for compressing
a 3d image.
AU QP Answer key NOv/Dec 2015 Computer Graphics 5 sem CSEThiyagarajan G
This document contains a summary of a computer graphics exam with 10 multiple choice questions in Part A and 4 long answer questions in Part B. Some of the key topics covered include: image resolution, scaling matrices, color conversion between RGB and CMY color modes, Bezier curves, projection planes, dithering, animation principles, turtle attributes in graphics, Bresenham's circle algorithm, Liang-Barsky line clipping algorithm, viewing transformations, cubic Bezier curves, and backface detection. Part B also includes questions on orthographic vs axonometric vs oblique projections, ambient lighting models, raster vs keyframe animation, ray tracing, and morphing.
An automatic algorithm for object recognition and detection based on asift ke...Kunal Kishor Nirala
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This document discusses different methods for 2D motion estimation, including pixel-based, block-matching, and mesh-based approaches. Pixel-based methods estimate a motion vector for each pixel by minimizing prediction error over neighborhoods with smoothness constraints. Block-matching divides an image into blocks and estimates a single motion vector per block by exhaustive search. Mesh-based motion representation partitions an image into polygons defined by nodes, estimates motion at nodes, and interpolates interior motion.
A Hybrid Technique for the Automated Segmentation of Corpus Callosum in Midsa...IJERA Editor
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EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE RETRIEVALsipij
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The document discusses key concepts in image processing including image sensing, acquisition, formation, sampling, quantization, and digital representation. It describes how the human eye forms images and contains photoreceptor cells. There are three main types of image sensors: single, line, and array. Sampling converts a continuous image to digital by selecting pixel values at regular intervals while quantization assigns discrete brightness levels. Together they allow images to be represented digitally as matrices of pixel values.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
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dataset. The images in database are loaded. The resultant image is given as input to feature extraction
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splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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will also discuss Algorithm based on Z-buffer method, A-buffer method, and Scan-Line Method.
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The detection of object edges in images is a crucial step employed in a vast amount of computer vision applications, for which a series of different algorithms has been developed in the last decades. This paper proposes a new edge detection method based on quantum information, which is achieved in two main steps: (i) an image enhancement stage that employs the quantum superposition law and (ii) an edge detection stage based on the probability of photon arrival to the camera sensor. The proposed method has been tested on synthetic and real images devoted to agriculture applications, where Fram & Deutsh criterion has been adopted to evaluate its performance. The results show that the proposed method gives better results in terms of detection quality and computation time compared to classical edge detection algorithms such as Sobel, Kayyali, Canny and a more recent algorithm based on Shannon entropy.
This document discusses fractal image compression based on jointly and different partitioning schemes. It proposes partitioning RGB images into range blocks in two ways: 1) Jointly, where the red, green, and blue channels are partitioned together into blocks of the same size and coordinates. 2) Differently, where each channel is partitioned independently, resulting in different block sizes and coordinates for each channel. The document provides background on fractal image compression and the encoding/decoding processes. It analyzes the two partitioning schemes and argues the different scheme is more effective for encoding by allowing each channel to have customized partitioning.
1. Simultaneous multi-slice imaging provides
robust slice-level motion tracking, estimation
and DWI reconstruction by covering the 3D
anatomy at multiple coupled image planes
3.3. Ball-and-Stick Model [2]
Introductio
Motion-Robust Reconstruction based on Simultaneous Multi-Slice
Registration for Diffusion-Weighted MRI of Moving Subjects
Bahram Marami1, Benoit Scherrer, Onur Afacan, Simon K.Warfield, Ali Gholipour2
1,2 {Bahram.Marami,Ali.Gholipour}@childrens.harvard.edu
•Simultaneous multi-slice (SMS) echo-
planar imaging has had a huge impact on the
acceleration of diffusion-weighted MRI
(DWI) in neuroimaging studies.
•These scans are still lengthy with vibration
which are not easily tolerated by non-
cooperative patients, children and newborns.
•A novel registration-based motion tracking
technique is proposed in this paper that takes
advantages of SMS acquisition to enable
robust reconstruction of neural
microstructures in moving subjects.
•Our technique has three main components:
1. Detection and rejection of through-
slice motion-corrupted images
2. Tracking and estimation of motion
using SMS registration
3. Robust reconstruction of diffusion
tensors and multi-compartment models
from motion-corrected DWI data
An SMS DWI scan is performed by
interleaved 2D echo-planar imaging with two
(or more) multi-band excitations. Two rigidly-
coupled slices provide a multi-plane coverage
of the anatomy and mitigate ill-posedness of
slice-to-volume registration.
Fig. 1: Two same-color arrows point at through-slice motion artifacts
seen in simultaneously acquired slices in a 2-band axial SMS DWI
Closing Filter
Input
Image
1. Through-slice motion detection
A morphological closing filter along the slice
select direction is used to detect inter-slice
intensity discontinuity.
Input
Image
Subtractor
Output
Image
Fig. 2: through-slice motion detection
A rule-based motion detection technique:
2. Slice-level motion tracking and
estimation
• Stochastic state-space model:
xk is the vector of motion parameters (three
rotations and three translations)
The distribution of measurement noise is NOT
fixed a priori. It is updated based on the
sequential observations through estimating its
covariance Sk at any time step k.
•multi-slice to volume registration:
yk is the set of simultaneous (coupled) slices
acquired at time step k.
• Multi-slice to volume registration and outlier-robust Kalman filter-based
motion states estimation [1]
3. Robust diffusion model reconstruction
from motion-corrected images
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. , NO., JULY 2015
Algorithm 1 Pseudo-code of the outlier-robust motion esti-
mation for each slice
Input: I3D, yk, xk 1
Output: xk
1: slice-to-volume-registration:
2: zk max
p
f (I2D(p), yk); p0 = xk 1
3: predict states:
4: xk xk 1 , xk xk
5: Pk Pk 1 + Q , Pk Pk
6: repeat
7: update noise:
8: = zk xk
9: ⇤k = (sR + T
+ Pk)/(s + 1)
10: update states:
11: Kk = (Pk + ⇤k) 1
Pk
12: xk = xk + KT
k (zk xk )
13: Pk = KT
k ⇤kKk + (I Kk)T
Pk (I Kk)
14: until converged
15: return xk
system ⌦0(x, y, z) fixed to the base b=0 image. Similarly,
⌦0,i(x, y, z) represents the coordinate system of the ith b=0
image (1iNb=0). To reconstruct the ˜B0 in ⌦0(x, y, z), any
point in the coordinates of every b=0 image, i.e. ⌦0,i(x, y, z),
is mapped to the coordinate of the ˜B0 through registering ˜B0
to each b=0 image slice using the explained outlier-robust
motion estimation method.
The isotropic 3D base image ˜B0 is reconstructed in an
iterative process. Since there is no ˜B0 for the registration
at the beginning, we choose the b=0 image volume with
least motion as the reference image and register this image
to slices in other b=0 image volumes. Then, an isotropic 3D
image is reconstructed in the coordinates of the selected b=0
image using image information of all b=0 based on scattered
data interpolation (SDI). The reconstructed ˜B0 image at each
iteration, is used as the reference image in the next iteration
to reconstruct a final ˜B0 image. Our experiments on various
image data showed that the iterative reconstruction algorithm
converges after 2-3 iterations. As shown in Fig. 2 in a 2D
representations, in order to compute the image intensity at the
center of the regular grid points of the ˜B0 image, all scattered
points around the center grid in a 3⇥3⇥3 neighborhood
are considered. The scattered points in the neighborhood
are mapped from the coordinates of the all b=0 images
(⌦0,i(x, y, z), 1iNb=0) to the coordinates of the ˜B0 image
(⌦0(x, y, z)).The gray-scale intensity at each center gird is
computed as
˜B0,regular =
Pn
i=1 wiB0,scattered
Pn
i=1 wi
(10)
where wi is a weight computed based on a Gaussian kernel
ri
Fig. 2: Scattered data interpolation in the 3
reconstruction
centered at the regular grid point (star in Fig.
wi =
1
p
2⇡
e
1
2 (
ri )2
where ri determines the distance between th
point and the center grid point, and n is the t
scattered points in the 3⇥3⇥3 neighborhood.
called the standard deviation and determines th
bell-shaped Gaussian function. Larger values f
weights to the points far from the center gird
a smoother reconstructed base image. For the
this paper, we set = 0.5.
Three orthogonal image planes from an a
who deliberately moved during the scan are s
and compared to the reconstructed ˜B0 image (
stationary gold standard image in which the v
asked to be still (Fig. 3c). In this scan 6 out
were b=0, i.e. Nb=0 = 6 and the subject delib
including making head rotations up to 30 . Ther
slice motion in 3 out of 6 b=0 volumes on
shown in Fig. 3a. It should be noted that intr
corrupted slices was automatically detected and
reconstruction of the ˜B0 image. Normalized roo
errors (NRMSE) between the gold standard
reconstructed ˜B0 image was, 0.0357, 0.0306, a
the first, second and the third iteration show
improvement in terms of NRMSE after the sec
2) Registration of diffusion sensitized imag
image: The reconstructed ˜B0 image was e
reference image to bring all diffusion sensitized
the same coordinate by registering all slices to B
the motion estimation algorithm explained in
Similar to the motion estimation in b=0 ima
motion-corrupted slices were automatically ide
parameters corresponding to the time step o
corrupted slice were considered equal to those
time step. Estimated motion parameters for e
stored for the reconstruction of diffusion tens
steps.
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. , NO., OCT 2015
Then, similar to (6), given n 6 diffusion-sensitized poi
with noncollinear gradient directions, diffusion tensors
computed by minimizing the following objective function
f( ) =
1
2
nX
i=1
w2
i ↵2
i
0
@ln
Bi,irregular
˜B0,irregular
! 6X
j=1
Mi,j j
1
A
2
where ↵i = Bi,irregular, Bi,irregular is the observ
diffusion-sensitized signal (gray-scale value) at any moti
corrected point location, and ˜B0,irregular is the correspond
non-diffusion weighted signal, which is interpolated from
˜B0 image at that irregular point location. Finally, wi i
weighting factor that is computed as
wi =
1
p
2⇡
e
1
2 (
ri )2
(
where ri is the distance between the ith point and the cen
of the grid point, is the standard deviation of a bell-shap
Gaussian function, and n is the total number of points in
neighborhood. The weights (wis) balance the contribution
multiple observed points based on their distance to the locat
where the tensor is computed.
III. ALGORITHM DESIGN AND IMPLEMENTATION
The slice-level registration-based motion tracking explain
in the previous section is a generic motion correction meth
which can be used for processing any set of sequentially
quired 2D MR images (structural, functional or diffusion M
from a dynamically moving subject. Our focus in this stu
has been on DW brain MRI, which involves relatively lo
duration scans with varying contrast that makes it particula
challenging for such an image registration-based approach
The flowchart of our motion-robust DW brain MRI rec
struction technique is shown in Fig. 1. Given a set of D
brain MRI volumes, first, slices corrupted by through-pl
motion were automatically detected using image featur
These motion-corrupted slices were excluded from mot
estimation and DTI reconstruction. Then, an isotropic
image was reconstructed from multiple b=0 images. T
proposed filtering-based motion estimation method was u
to correct for the slice-level motion in all b=0 images and
reconstruct a 3D base image (i.e., ˜B0) which is a weigh
average of all b=0 images. In the next step, temporal h
motion was estimated using the approach proposed in Sect
II-A through registering all diffusion-sensitized image sli
to the reconstructed ˜B0 image. Finally, diffusion tensors w
computed in the ˜B0 coordinates using image information fr
all gradient directions. In the following subsections these st
are explained in detail.
A. Identification of Motion-Corrupted Slices
In routine slice image acquisitions, diffusion-weighted sli
are obtained in an interleaved order to avoid slice cross-t
and spin history artifacts. In an interleaved acquisition adjac
slices are obtained at different time points and patient’s h
motion results in corruption of image slices independently.
this scheme, artifacts caused by motion can be easily identifi
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. , NO., OCT 2015
Then, similar to (6), given n 6 diffusion-sensitized points
with noncollinear gradient directions, diffusion tensors are
computed by minimizing the following objective function
f( ) =
1
2
nX
i=1
w2
i ↵2
i
0
@ln
Bi,irregular
˜B0,irregular
! 6X
j=1
Mi,j j
1
A
2
(9)
where ↵i = Bi,irregular, Bi,irregular is the observed
diffusion-sensitized signal (gray-scale value) at any motion-
corrected point location, and ˜B0,irregular is the corresponding
non-diffusion weighted signal, which is interpolated from the
˜B0 image at that irregular point location. Finally, wi is a
weighting factor that is computed as
wi =
1
p
2⇡
e
1
2 (
ri )2
(10)
where ri is the distance between the ith point and the center
of the grid point, is the standard deviation of a bell-shaped
Gaussian function, and n is the total number of points in the
neighborhood. The weights (wis) balance the contribution of
multiple observed points based on their distance to the location
where the tensor is computed.
III. ALGORITHM DESIGN AND IMPLEMENTATION
The slice-level registration-based motion tracking explained
in the previous section is a generic motion correction method,
which can be used for processing any set of sequentially ac-
quired 2D MR images (structural, functional or diffusion MRI)
from a dynamically moving subject. Our focus in this study
has been on DW brain MRI, which involves relatively long
duration scans with varying contrast that makes it particularly
challenging for such an image registration-based approach.
The flowchart of our motion-robust DW brain MRI recon-
struction technique is shown in Fig. 1. Given a set of DW
brain MRI volumes, first, slices corrupted by through-plane
motion were automatically detected using image features.
These motion-corrupted slices were excluded from motion
estimation and DTI reconstruction. Then, an isotropic 3D
image was reconstructed from multiple b=0 images. The
proposed filtering-based motion estimation method was used
to correct for the slice-level motion in all b=0 images and to
reconstruct a 3D base image (i.e., ˜B0) which is a weighted
average of all b=0 images. In the next step, temporal head
motion was estimated using the approach proposed in Section
II-A through registering all diffusion-sensitized image slices
to the reconstructed ˜B0 image. Finally, diffusion tensors were
computed in the ˜B0 coordinates using image information from
all gradient directions. In the following subsections these steps
are explained in detail.
A. Identification of Motion-Corrupted Slices
In routine slice image acquisitions, diffusion-weighted slices
are obtained in an interleaved order to avoid slice cross-talk
and spin history artifacts. In an interleaved acquisition adjacent
slices are obtained at different time points and patient’s head
motion results in corruption of image slices independently. In
this scheme, artifacts caused by motion can be easily identified
Input: I3D, yk, ˆxk 1
Output: ˆxk
1: slice-to-volume registration:
2: zk max
p
f (I2D(p), yk); p0 = ˆxk 1
3: predict states:
4: ˆxk ˆxk 1 , ˆxk ˆxk
5: Pk Pk 1 + Q , Pk Pk
6: repeat
7: update noise:
8: = zk ˆxk
9: ⇤k = (sR + T + Pk)/(s + 1)
10: update states:
11: Kk = (Pk + ⇤k) 1Pk
12: ˆxk = ˆxk + KT
k (zk ˆxk )
13: Pk = KT
k ⇤kKk + (I Kk)T Pk (I Kk)
14: until converged
15: return ˆxk
B. Image Reconstruction
Estimated motion parameters for each diffusion-sensitized
image slice allow us to bring all DWI data to the same coor-
dinates and perform further analysis. Although more complex
models of the diffusion signal could be considered to benefit
from motion correction, for the sake of simplicity and to focus
on the effect of motion correction, we consider reconstruction
of the diffusion tensor matrix based on the Stejskal-Tanner
(ST) equation. Having a base ˜B0 image and Nb6=0 diffusion-
sensitized volumes, a diffusion tensor can be estimated at any
point of the regular grid in ˜B0 by solving ST equation as
Bi,regular = ˜B0,regulare bgT
i Dgi
1 i Nb6=0 (5)
where ˜B0,regular is the intensity at the regular grid location
in ˜B0 and Bi,regular is the corresponding intensity at the ith
diffusion-sensitized image; gi = [gix, giy, giz] represents the
ith diffusion gradient direction in the ˜B0 coordinates and b is
an imaging sequence dependent constant, which specifies the
diffusion sensitivity. Furthermore, D is a 3⇥3 symmetric pos-
itive definite matrix representing a diffusion tensor expressed
in ˜B0 coordinates with elements that are then rearranged in a
parameter vector = [Dxx, Dxy, Dxz, Dyy, Dyz, Dzz].
Given n 6 diffusion-sensitized images with noncollinear
gradient directions and a constant b-value, diffusion tensors
can be estimated by minimizing an objective function as [22]
f( ) =
1
2
nX
i=1
↵2
i
0
@ln
Bi,regular
˜B0,regular
! 6X
j=1
Mi,j j
1
A
2
(6)
where n = Nb6=0, and
M = b
2
6
4
g2
1x 2g1xg1y 2g1xg1z g2
1y 2g1yg1z g2
1z
...
...
...
...
...
...
g2
nx 2gnxgny 2gnxgnz g2
ny 2bgnygnz g2
nz
3
7
5 (7)
is an n⇥6 design matrix. Moreover, ↵is are weights for
different gradient directions in the objective function. Koay et
method. Sim
of the desig
guaranteed
typical solu
D = UT
U
zero diagon
In order
intensities o
locations id
case, howe
image inte
points in
approach t
corrected D
to reconstr
common re
these imag
This app
and motion
be available
grid points
or larger) is
locations. H
out the reco
of the final
window in
image poin
artifacts in
approach d
motion on
To mitig
interpolatio
and directly
using data
this end, w
locations an
a local neig
characteriz
value, b va
Gradient
are in the sc
directions i
of the acqu
has to be c
slice with
estimation
the rotation
and Rm is
the slice-to
the diffusi
coordinates
where g0i i
in the scan
3.2. Diffusion Tensor Model
Each scattered point is associated with a
gradient direction, motion parameters, b0 and
bi intensities
3.1. Constructing an
isotropic 3D base image
with desired resolution
from multiple b0 images
of the grid point, is the standard deviation of a bell-shaped
Gaussian function, and n is the total number of points in the
neighborhood. The weights (wis) balance the contribution of
multiple observed points based on their distance to the location
where the tensor is computed.
III. ALGORITHM DESIGN AND IMPLEMENTATION
The slice-level registration-based motion tracking explained
in the previous section is a generic motion correction method,
which can be used for processing any set of sequentially ac-
quired 2D MR images (structural, functional or diffusion MRI)
from a dynamically moving subject. Our focus in this study
has been on DW brain MRI, which involves relatively long
duration scans with varying contrast that makes it particularly
challenging for such an image registration-based approach.
The flowchart of our motion-robust DW brain MRI recon-
struction technique is shown in Fig. 1. Given a set of DW
brain MRI volumes, first, slices corrupted by through-plane
motion were automatically detected using image features.
These motion-corrupted slices were excluded from motion
estimation and DTI reconstruction. Then, an isotropic 3D
image was reconstructed from multiple b=0 images. The
proposed filtering-based motion estimation method was used
to correct for the slice-level motion in all b=0 images and to
reconstruct a 3D base image (i.e., ˜B0) which is a weighted
average of all b=0 images. In the next step, temporal head
motion was estimated using the approach proposed in Section
II-A through registering all diffusion-sensitized image slices
to the reconstructed ˜B0 image. Finally, diffusion tensors were
computed in the ˜B0 coordinates using image information from
all gradient directions. In the following subsections these steps
are explained in detail.
A. Identification of Motion-Corrupted Slices
In routine slice image acquisitions, diffusion-weighted slices
are obtained in an interleaved order to avoid slice cross-talk
and spin history artifacts. In an interleaved acquisition adjacent
slices are obtained at different time points and patient’s head
motion results in corruption of image slices independently. In
this scheme, artifacts caused by motion can be easily identified
intensity in the difference image. Volumes with more than
15% corrupted slices were excluded from the analysis.
B. Constructing an Isotropic 3D b=0 Image
Multiple b=0 images are often acquired along with
diffusion-sensitized images. We reconstructed a base image
namely ˜B0 in an isotropic regular 3D grid from multiple
b=0 images through an iterative process. We chose the first
b=0 volume or a b=0 with least amount of motion as the
reference image at the beginning and registered this image to
slices in all other b=0 image volumes. Then, an isotropic 3D
image was reconstructed in the coordinates of the selected b=0
image using image information of all b=0 images. Iterations of
motion correction and reconstruction were performed similar
to the previous works [16], [17]; The reconstructed ˜B0 image
at each iteration, was used as the reference image in the next
iteration to reconstruct a final ˜B0 image. Our experiments
on various image data showed that the algorithm converged
after 2-3 iterations. The image intensity at the center of the
regular grid points of the ˜B0 image, was computed based on
all (n) irregular points around the center grid in a 3⇥3⇥3
neighborhood using
˜B0,regular =
Pn
i=1 wiB0,irregular
Pn
i=1 wi
(11)
where wi is a distance weight based on a Gaussian kernel
centered at the regular grid point, computed using (10), where
ri determines the distance between the ith point and the center
grid point. For the experiments in this study, we set = 0.5.
C. Motion Estimation and Reconstruction
The reconstructed ˜B0 image was then used as a reference
volume to bring all diffusion-sensitized images b6=0 to the
same coordinates and calculate diffusion tensors. This was
achieved through the techniques explained in Section II. For
all practical purposes slice-to-volume registration (SVR) was
performed through registering the reference volume to the slice
and inverting the transformation. Estimated motion parameters
for each slice were stored for the reconstruction of diffusion
tensors in the next step. Similar to the motion estimation
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. , NO., OCT 2015
Then, similar to (6), given n 6 diffusion-sensitized points
with noncollinear gradient directions, diffusion tensors are
computed by minimizing the following objective function
f( ) =
1
2
nX
i=1
w2
i ↵2
i
0
@ln
Bi,irregular
˜B0,irregular
! 6X
j=1
Mi,j j
1
A
2
(9)
where ↵i = Bi,irregular, Bi,irregular is the observed
diffusion-sensitized signal (gray-scale value) at any motion-
corrected point location, and ˜B0,irregular is the corresponding
non-diffusion weighted signal, which is interpolated from the
˜B0 image at that irregular point location. Finally, wi is a
weighting factor that is computed as
wi =
1
p
2⇡
e
1
2 (
ri )2
(10)
where ri is the distance between the ith point and the center
of the grid point, is the standard deviation of a bell-shaped
Gaussian function, and n is the total number of points in the
neighborhood. The weights (wis) balance the contribution of
multiple observed points based on their distance to the location
where the tensor is computed.
III. ALGORITHM DESIGN AND IMPLEMENTATION
The slice-level registration-based motion tracking explained
in the previous section is a generic motion correction method,
which can be used for processing any set of sequentially ac-
quired 2D MR images (structural, functional or diffusion MRI)
from a dynamically moving subject. Our focus in this study
has been on DW brain MRI, which involves relatively long
duration scans with varying contrast that makes it particularly
challenging for such an image registration-based approach.
The flowchart of our motion-robust DW brain MRI recon-
struction technique is shown in Fig. 1. Given a set of DW
brain MRI volumes, first, slices corrupted by through-plane
motion were automatically detected using image features.
These motion-corrupted slices were excluded from motion
estimation and DTI reconstruction. Then, an isotropic 3D
image was reconstructed from multiple b=0 images. The
proposed filtering-based motion estimation method was used
to correct for the slice-level motion in all b=0 images and to
reconstruct a 3D base image (i.e., ˜B0) which is a weighted
average of all b=0 images. In the next step, temporal head
motion was estimated using the approach proposed in Section
II-A through registering all diffusion-sensitized image slices
to the reconstructed ˜B0 image. Finally, diffusion tensors were
computed in the ˜B0 coordinates using image information from
all gradient directions. In the following subsections these steps
are explained in detail.
A. Identification of Motion-Corrupted Slices
In routine slice image acquisitions, diffusion-weighted slices
are obtained in an interleaved order to avoid slice cross-talk
and spin history artifacts. In an interleaved acquisition adjacent
slices are obtained at different time points and patient’s head
motion results in corruption of image slices independently. In
this scheme, artifacts caused by motion can be easily identified
i
c
t
t
s
c
a
i
i
i
A
i
m
i
a
t
i
1
B
d
n
b
b
r
s
i
i
m
t
a
i
o
a
r
a
n
w
c
r
g
C
v
s
a
a
p
a
f
t
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. , NO., OCT 2015
Then, similar to (6), given n 6 diffusion-sensitized points
with noncollinear gradient directions, diffusion tensors are
computed by minimizing the following objective function
f( ) =
1
2
nX
i=1
w2
i ↵2
i
0
@ln
Bi,irregular
˜B0,irregular
! 6X
j=1
Mi,j j
1
A
2
(9)
where ↵i = Bi,irregular, Bi,irregular is the observed
diffusion-sensitized signal (gray-scale value) at any motion-
corrected point location, and ˜B0,irregular is the corresponding
non-diffusion weighted signal, which is interpolated from the
˜B0 image at that irregular point location. Finally, wi is a
weighting factor that is computed as
wi =
1
p
2⇡
e
1
2 (
ri )2
(10)
where ri is the distance between the ith point and the center
of the grid point, is the standard deviation of a bell-shaped
Gaussian function, and n is the total number of points in the
neighborhood. The weights (wis) balance the contribution of
multiple observed points based on their distance to the location
where the tensor is computed.
III. ALGORITHM DESIGN AND IMPLEMENTATION
The slice-level registration-based motion tracking explained
in the previous section is a generic motion correction method,
which can be used for processing any set of sequentially ac-
quired 2D MR images (structural, functional or diffusion MRI)
from a dynamically moving subject. Our focus in this study
has been on DW brain MRI, which involves relatively long
duration scans with varying contrast that makes it particularly
challenging for such an image registration-based approach.
The flowchart of our motion-robust DW brain MRI recon-
struction technique is shown in Fig. 1. Given a set of DW
brain MRI volumes, first, slices corrupted by through-plane
motion were automatically detected using image features.
These motion-corrupted slices were excluded from motion
estimation and DTI reconstruction. Then, an isotropic 3D
image was reconstructed from multiple b=0 images. The
proposed filtering-based motion estimation method was used
to correct for the slice-level motion in all b=0 images and to
reconstruct a 3D base image (i.e., ˜B0) which is a weighted
average of all b=0 images. In the next step, temporal head
motion was estimated using the approach proposed in Section
II-A through registering all diffusion-sensitized image slices
to the reconstructed ˜B0 image. Finally, diffusion tensors were
computed in the ˜B0 coordinates using image information from
all gradient directions. In the following subsections these steps
are explained in detail.
A. Identification of Motion-Corrupted Slices
In routine slice image acquisitions, diffusion-weighted slices
are obtained in an interleaved order to avoid slice cross-talk
and spin history artifacts. In an interleaved acquisition adjacent
slices are obtained at different time points and patient’s head
motion results in corruption of image slices independently. In
this scheme, artifacts caused by motion can be easily identified
in
co
th
th
sit
cl
af
im
im
i.e
A
in
m
im
al
to
in
15
B.
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b=
re
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im
im
m
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ite
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af
re
al
ne
w
ce
ri
gr
C.
vo
sa
ac
al
pe
an
fo
te
fj 2 [0, 1] sum-of-unity fractions of occupancy
Materials and Methods
Results
Results
Fig. 3: DTI in adult volunteers
Orig VVR SVR MT-SMR GS
Orig: original images with motion
VVR: volume-to-volume registration
SVR: slice-to-volume registration
MT-SMR: motion tracking based on
simultaneous multi-slice registration
GS: gold standard, images with no motion
Materials and Methods
is computed in the image domain
considering slice profile.
Algorithm 1 Pseudo-code of the ORKF-based motion estimation
slice.
Input: I3D, yk, ˆxk 1
Output: ˆxk
1: slice-to-volume registration:
2: zk max
p
f (I2D(p), yk); p0 = ˆxk 1
3: predict states:
4: ˆxk ˆxk 1 , ˆxk ˆxk
5: Pk Pk 1 + Q , Pk Pk
6: repeat
7: update noise:
8: = zk ˆxk
9: ⇤k = (sR + T + Pk)/(s + 1)
10: update states:
11: Kk = (Pk + ⇤k) 1Pk
12: ˆxk = ˆxk + KT
k (zk ˆxk )
13: Pk = KT
k ⇤kKk + (I Kk)T Pk (I Kk)
14: until converged
15: return ˆxk
B. Image Reconstruction
Estimated motion parameters for each diffusion-se
image slice allow us to bring all DWI data to the sam
dinates and perform further analysis. Although more c
models of the diffusion signal could be considered to
from motion correction, for the sake of simplicity and t
on the effect of motion correction, we consider recons
of the diffusion tensor matrix based on the Stejskal
(ST) equation. Having a base ˜B0 image and Nb6=0 dif
sensitized volumes, a diffusion tensor can be estimated
point of the regular grid in ˜B0 by solving ST equatio
Bi,regular = ˜B0,regulare bgT
i Dgi
1 i Nb6=
where ˜B0,regular is the intensity at the regular grid l
in ˜B0 and Bi,regular is the corresponding intensity at
diffusion-sensitized image; gi = [gix, giy, giz] represe
ith diffusion gradient direction in the ˜B0 coordinates a
an imaging sequence dependent constant, which speci
diffusion sensitivity. Furthermore, D is a 3⇥3 symmet
itive definite matrix representing a diffusion tensor ex
in ˜B0 coordinates with elements that are then rearrang
parameter vector = [Dxx, Dxy, Dxz, Dyy, Dyz, Dzz]
Given n 6 diffusion-sensitized images with nonc
gradient directions and a constant b-value, diffusion
can be estimated by minimizing an objective function
f( ) =
1
2
nX
i=1
↵2
i
0
@ln
Bi,regular
˜B0,regular
! 6X
j=1
Mi,j j
1
A
where n = Nb6=0, and
M = b
2
6
4
g2
1x 2g1xg1y 2g1xg1z g2
1y 2g1yg1z g2
1z
...
...
...
...
...
...
g2
nx 2gnxgny 2gnxgnz g2
ny 2bgnygnz g2
nz
3
7
5
is an n⇥6 design matrix. Moreover, ↵is are weig
different gradient directions in the objective function. K
• DTI in 21 children (3 to 12 years old), multiple bi values and 90 directions
CC Cin LIC Pons
FA
0.3
0.4
0.5
0.6
0.7
0.8
Orig VVR SVR MT-SMR
Fig. 5: Boxplot analysis of FA values in regions of interest
degrees
-10
0
10
20 θx
θy
θz
mm
-10
0
10
tx
ty
tz
degrees
-10
0
10
20
mm
-10
0
10
slice number
1 157 313 469 625 781 937 1093 1249 1405
degrees
-10
0
10
20
slice number
1 157 313 469 625 781 937 1093 1249 1405
mm
-10
0
10
VVR
MT-SMR
SVR
Fig. 7: comparison of estimated motion parameters
corona radiata
CC
cingulum
Fig. 6: Whole brain tractography results
Fig. 8: Crossing fibers in the ball-and-stick model
Conclusions
Orig VVR SVR MT-SMR
Orig VVR SVR MT-SMR
amount of motion
4 6 8 10 12 14 16 18
∆FA
inCC
0.1
0.2
0.3
0.4
0.5
0.6
Orig VVR SVR MT-SMR
amount of motion
4 6 8 10 12 14 16 18
∆FA
incingulum
0.1
0.2
0.3
0.4
0.5
0.6
Orig VVR SVR MT-SMR
Fig. 4: Average FA value differences between the GS and each method in corpus callosum (CC) and cingulum
• DTI in adult volunteers 30 bi =1000 and 6 b0 images in14 motion cases
References
[1] B. Marami, B. Scherrer, O. Afacan, B. Erem, S. Warfield, A. Gholipour,
Motion-robust diffusion- weighted brain MRI reconstruction through slice-
level registration-based motion tracking, Med Imaging, IEEE T, 2016.
[2] T. Behrens, M. Woolrich, H. Johansen-Berg, R. Nunes, P. Matthews, J.
Brady, S. Smith, Characterization and propagation of uncertainty in diffusion-
weighted MR imaging, Magnet Reson Med 50 (2003) 1077–88
• Motion estimation through simultaneous
multi-slice-to-volume registration
outperformed slice-to-volume, and volume-
to-volume registration.
• MT-SMR leverages estimation power of
the outlier-robust Kalman filter and the 3D
coverage of the anatomy at multiple image
planes provided by SMS DWI.
•This technique can extend the use of SMS
DWI to populations that continuously move
during lengthy DWI scans by real-time
prospective motion tracking and correction.
Algorithm 1 Pseudo-code of the ORKF-based motion estimation for ea
slice.
Input: I3D, yk, ˆxk 1
Output: ˆxk
1: slice-to-volume registration:
2: zk max
p
f (I2D(p), yk); p0 = ˆxk 1
3: predict states:
4: ˆxk ˆxk 1 , ˆxk ˆxk
5: Pk Pk 1 + Q , Pk Pk
6: repeat
7: update noise:
8: = zk ˆxk
9: ⇤k = (sR + T + Pk)/(s + 1)
10: update states:
11: Kk = (Pk + ⇤k) 1Pk
12: ˆxk = ˆxk + KT
k (zk ˆxk )
13: Pk = KT
k ⇤kKk + (I Kk)T Pk (I Kk)
14: until converged
15: return ˆxk
B. Image Reconstruction
Estimated motion parameters for each diffusion-sensitiz
image slice allow us to bring all DWI data to the same coo
dinates and perform further analysis. Although more compl
models of the diffusion signal could be considered to bene
from motion correction, for the sake of simplicity and to foc
on the effect of motion correction, we consider reconstructi
of the diffusion tensor matrix based on the Stejskal-Tann
(ST) equation. Having a base ˜B0 image and Nb6=0 diffusio
sensitized volumes, a diffusion tensor can be estimated at a
point of the regular grid in ˜B0 by solving ST equation as
Bi,regular = ˜B0,regulare bgT
i Dgi
1 i Nb6=0 (
where ˜B0,regular is the intensity at the regular grid locati
in ˜B0 and Bi,regular is the corresponding intensity at the i
diffusion-sensitized image; gi = [gix, giy, giz] represents t
ith diffusion gradient direction in the ˜B0 coordinates and b
an imaging sequence dependent constant, which specifies t
diffusion sensitivity. Furthermore, D is a 3⇥3 symmetric po
itive definite matrix representing a diffusion tensor express
in ˜B0 coordinates with elements that are then rearranged in
parameter vector = [Dxx, Dxy, Dxz, Dyy, Dyz, Dzz].
Given n 6 diffusion-sensitized images with noncolline
gradient directions and a constant b-value, diffusion tenso
can be estimated by minimizing an objective function as [2
f( ) =
1
2
nX
i=1
↵2
i
0
@ln
Bi,regular
˜B0,regular
! 6X
j=1
Mi,j j
1
A
2
(
where n = Nb6=0, and
M = b
2
6
4
g2
1x 2g1xg1y 2g1xg1z g2
1y 2g1yg1z g2
1z
...
...
...
...
...
...
g2
nx 2gnxgny 2gnxgnz g2
ny 2bgnygnz g2
nz
3
7
5 (
is an n⇥6 design matrix. Moreover, ↵is are weights f
different gradient directions in the objective function. Koay