The purpose of this study is to present a method of three-dimensional computed tomographic (3D-CT) cephalometrics and its use to study cranio/maxilla-facial malformations. We propose a system for automatic localization of cephalometric landmarks using reeb graphs. Volumetric images of a patient were reconstructed into 3D mesh. The proposed method is carried out in three steps: we begin by applying 3d mesh skull simplification, this mesh was reconstructed from a head volumetric medical image, and then we extract a reeb graph. Reeb graph mesh extraction represents a skeleton composed in a number of nodes and arcs. We are interested in the node position; we noted that some reeb nodes could be considered as cephalometric landmarks under specific conditions. The third step is to identify these nodes automatically by using elastic mesh registration using “thin plate” transformation and clustering. Preliminary results show a landmarks recognition rate of more than 90%, very close to the manually provided landmarks positions made by a medical stuff.
Proposition of local automatic algorithm for landmark detection in 3D cephalo...journalBEEI
This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...CSCJournals
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
Iris Localization - a Biometric Approach Referring Daugman's AlgorithmEditor IJCATR
In general, there are many methods of biometric identification. But the Iris
recognition is most accurate and secure means of biometric identification. Iris has
many properties which makes it ideal biometric identification. There are many
methods used to identify the Iris location. To locate Iris many traditional methods are
used. In this we proposed such methods which can identify Iris Center(IC) as well as
localize its center. In this paper we are proposing a method which can use novel IC
localization method on the fact that the elliptical shape (ES) of Iris varies according to
the rotation of eye movement. In this paper various IC locations are generated and
stored in database. Finally the location of IC is detected by matching the ES of the Iris
of input eye image withes candidates in DB. In this paper we are comparing different
methods for Iris localization.
ROLE OF HYBRID LEVEL SET IN FETAL CONTOUR EXTRACTIONsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
This document summarizes a research paper on segmenting and classifying brain tumors in MRI images using cellular automata and neural networks. The researchers first use co-occurrence matrices and run length features to automatically select seed points in abnormal tumor regions. A cellular automata algorithm then performs seeded segmentation on the images to detect and highlight the tumor region. Finally, the images are classified into normal, benign, or malignant categories using texture features and a radial basis function neural network. The neural network approach provides fast and accurate tumor classification compared to other methods. In summary, this paper presents an automatic method for segmenting and classifying brain tumors in MRI images based on cellular automata for segmentation and neural networks for classification.
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
The document describes a new automated method for detecting the optic disc in retinal images. The method uses a line operator designed to capture the circular brightness structure of the optic disc. It evaluates image variation along multiple oriented line segments and locates the disc based on the orientation with maximum variation. The method was tested on a dataset and achieved a 96% success rate in detecting the optic disc.
Detection of Cranial- Facial Malformations: Towards an Automatic MethodCSCJournals
This paper presents an original method to detect malformations in cranial facial 3D images. This is a hard task because of the complex structure of the cranium. The method is composed by several stages containing essentially a segmentation phase to localize the bone structure, a 3D image characterization to delineate automatically geometrical characteristics from the segmented images. These need to possess a mathematical definition in order to be calculated and an anatomical pertinence to be representative of the structures’ shape. So, the characteristics retained are the crest lines. And finally a registration phase to identify the deformations place. This method is tested on real and synthetic data and has proved his performance.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Proposition of local automatic algorithm for landmark detection in 3D cephalo...journalBEEI
This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...CSCJournals
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
Iris Localization - a Biometric Approach Referring Daugman's AlgorithmEditor IJCATR
In general, there are many methods of biometric identification. But the Iris
recognition is most accurate and secure means of biometric identification. Iris has
many properties which makes it ideal biometric identification. There are many
methods used to identify the Iris location. To locate Iris many traditional methods are
used. In this we proposed such methods which can identify Iris Center(IC) as well as
localize its center. In this paper we are proposing a method which can use novel IC
localization method on the fact that the elliptical shape (ES) of Iris varies according to
the rotation of eye movement. In this paper various IC locations are generated and
stored in database. Finally the location of IC is detected by matching the ES of the Iris
of input eye image withes candidates in DB. In this paper we are comparing different
methods for Iris localization.
ROLE OF HYBRID LEVEL SET IN FETAL CONTOUR EXTRACTIONsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
This document summarizes a research paper on segmenting and classifying brain tumors in MRI images using cellular automata and neural networks. The researchers first use co-occurrence matrices and run length features to automatically select seed points in abnormal tumor regions. A cellular automata algorithm then performs seeded segmentation on the images to detect and highlight the tumor region. Finally, the images are classified into normal, benign, or malignant categories using texture features and a radial basis function neural network. The neural network approach provides fast and accurate tumor classification compared to other methods. In summary, this paper presents an automatic method for segmenting and classifying brain tumors in MRI images based on cellular automata for segmentation and neural networks for classification.
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
The document describes a new automated method for detecting the optic disc in retinal images. The method uses a line operator designed to capture the circular brightness structure of the optic disc. It evaluates image variation along multiple oriented line segments and locates the disc based on the orientation with maximum variation. The method was tested on a dataset and achieved a 96% success rate in detecting the optic disc.
Detection of Cranial- Facial Malformations: Towards an Automatic MethodCSCJournals
This paper presents an original method to detect malformations in cranial facial 3D images. This is a hard task because of the complex structure of the cranium. The method is composed by several stages containing essentially a segmentation phase to localize the bone structure, a 3D image characterization to delineate automatically geometrical characteristics from the segmented images. These need to possess a mathematical definition in order to be calculated and an anatomical pertinence to be representative of the structures’ shape. So, the characteristics retained are the crest lines. And finally a registration phase to identify the deformations place. This method is tested on real and synthetic data and has proved his performance.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Literature survey for 3 d reconstruction of brain mri imageseSAT Journals
Abstract
Since Doctors had only the 2D Image Data to visualize the tumors in the MRI images, which never gave the actual feel of how the tumor would exactly look like . The doctors were deprived from the exact visualization of the tumor the amount of the tumor to be removed by operation was not known, which caused a lot of deformation in the faces and structure of the patients face or skull. The diversity and complexity of tumor cells makes it very challenging to visualize tumor present in magnetic resonance image (MRI) data. Hence to visualize the tumor properly 2D MRI image has to be converted to 3D image. With the development of computer image processing technology, three-dimensional (3D) visualization has become an important method of the medical diagnose, it offers abundant and accurate information for medical experts. Three-dimensional (3-D) reconstruction of medical images is widely applied to tumor localization; surgical planning and brain electromagnetic field computation etc. The brain MR images have unique characteristics, i.e., very complicated changes of the gray-scales and highly irregular boundaries. Traditional 3-D reconstruction algorithms are challenged in solving this problem. Many reconstruction algorithms, such as marching cubes and dividing cubes, need to establish the topological relationship between the slices of images. The results of these traditional approaches vary depending on the number of input sections, their positions, the shape of the original body and the applied interpolation technique. These make the task tedious and time-consuming. Moreover, satisfied reconstruction result may not even be obtained when the highly irregular objects such as the encephalic tissues are considered. Due to complexity and irregularity of each encephalic tissue boundary, three-dimensional (3D) reconstruction for MRI image is necessary. A Literature survey is done to study different methods of 3D reconstruction of brain images from MRI images. Keywords: 3-D reconstruction, region growing, segmentation method, immune algorithm (IA), one class support vector machine (OCSVM) and sphere shaped support vector machine (SSSVM).
The document describes a novel method for localizing the pupil in eye gaze tracking systems. The proposed method fuses existing pupil localization techniques, including Hough transform, gray projection, and coarse positioning. This fusion approach aims to improve localization accuracy while lowering false detections. The method first preprocesses the eye image using filtering, edge detection, and thresholding/binarization. It then applies the fused techniques of Hough transform, gray projection, and coarse positioning to the preprocessed image to accurately detect the pupil boundary and center coordinates in a robust manner. The results are expected to provide better pupil localization compared to existing individual techniques.
Intelligent computing techniques on medical image segmentation and analysis a...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A robust iris recognition method on adverse conditionsijcseit
As a stable biometric system, iris has recently attracted great attention among the researchers. However,
research is still needed to provide appropriate solutions to ensure the resistance of the system against error
factors. The present study has tried to apply a mask to the image so that the unexpected factors affecting
the location of the iris can be removed. So, pupil localization will be faster and robust. Then to locate the
exact location of the iris, a simple stage of boundary displacement due to the Canny edge detector has been
applied. Then, with searching left and right IRIS edge point, outer radios of IRIS will be detect. Through
the process of extracting the iris features, it has been sought to obtain the distinctive iris texture features by
using a discrete stationary wavelets transform 2-D (DSWT2). Using DSWT2 tool and symlet 4 wavelet,
distinctive features are extracted. To reduce the computational cost, the features obtained from the
application of the wavelet have been investigated and a feature selection procedure, using similarity
criteria, has been implemented. Finally, the iris matching has been performed using a semi-correlation
criterion. The accuracy of the proposed method for localization on CASIA-v1, CASIA-v3 is 99.73%,
98.24% and 97.04%, respectively. The accuracy of the feature extraction proposed method for CASIA3 iris
images database is 97.82%, which confirms the efficiency of the proposed method.
Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Brea...TELKOMNIKA JOURNAL
Breast cancer is the most commonly diagnosed cancer among females worldwide. Computer aided diagnosis (CAD) was developed to assist radiologists in detecting and evaluating nodules so it can improve diagnostic accuracy, avoid unnecessary biopsies, reduce anxiety and control costs. This research proposes a method of CAD for breast ultrasound images based on margin and posterior acoustic features. It consists of preprocessing, segmentation using active contour without edge (ACWE) and morphological, feature extraction and classification. Texture and geometry analysis was used to determine the characteristics of the posterior acoustic and margin nodules. Support vector machines (SVM) provided better performance than multilayer perceptron (MLP). The performance of proposed method achieved the accuracy of 91.35%, sensitivity of 92.00%, specificity of 89.66%, PPV of 95.83%, NPV of 81.26% and Kappa of 0.7915. These results indicate that the developed CAD has potential to be implemented for diagnosis of breast cancer using ultrasound images.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science, Engineering and Information Technology. The Journal looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
A new hybrid method for the segmentation of the brain mrissipij
The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue
characterization, presenting an interest in the follow-up of various pathologies such as the multiple
sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The
extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is
proposed; finally the last section is organized around an experimental part allowing the study of the
behavior of our model on textured images. In the aim to validate our model, different segmentations were
down on pathological Brain MRI, the obtained results have been compared to the results obtained by
another models. This results show the effectiveness and the robustness of the suggested approach.
Detection of uveal melanoma using fuzzy and neural networks classifiersTELKOMNIKA JOURNAL
The use of image processing is increasingly utilized for disease detection. In this article, an algorithm is proposed to detect uveal melanoma (UM) which is a type of intraocular cancer. The proposed method integrates algorithms related to iris segmentation and proposes a novel algorithm for the detection of UM from the approach of fuzzy logic and neural networks. The study case results show 76% correct classification in the fuzzy logic system and 96.04% for the artificial neural networks.
Automatic whole-body bone scan image segmentation based on constrained local ...journalBEEI
In Indonesia, cancer is very burdensome financially for sufferers as well as for the country. Increasing the access to early detection of cancer can be a solution to prevent the situation from worsening. Regarding the problem of cancer lesion detection, a whole-body bone scan image is the primary modality of nuclear medicine for the detection of cancer lesions on a bone. Therefore, high segmentation accuracy of the whole-body bone scan image is a crucial step in building the shape model of some predefined regions in the bone scan image where metastasis was predicted to appear frequently. In this article, we proposed an automatic whole-body bone scan image segmentation based on constrained local model (CLM). We determine 111 landmark points on the bone scan image as the input for the model building step. The resulting shape and texture model are further used in the fitting step to estimate the landmark points of predefined regions. We use the CLM-based approach using regularized landmark mean-shift (RLMS) to lessen the effect of ambiguity, which was struggled by the CLM-based approach. From the experimental result, we successfully show that our proposed image segmentation system achieves higher performance than the general CLM-based approach.
The document discusses a proposed approach for personal identification using palmprint biometrics based on principal line extraction. It begins with an abstract summarizing preprocessing using Gaussian filtering and ROI extraction to smooth images and isolate the palm region. Canny edge detection is then used to extract principal lines by analyzing edge direction and gradient strength. The lines are traced and non-maximum edges suppressed to extract key lines. Templates are generated by dividing images into blocks and identifying those containing lines. Identification is performed by comparing templates to stored ones using distance matching. The document then provides context on palmprint identification and prior approaches before detailing the proposed Canny edge detection based method.
A SIMPLE IMAGE PROCESSING APPROACH TO ABNORMAL SLICES DETECTION FROM MRI TUMO...ijma
This paper proposed a method for brain tumor detection from the magnetic resonance imaging (MRI) of
human head scans. The proposed work explained the tumor detection process by means of image
processing transformations and thresholding technique. The MRI images are preprocessed by
transformation techniques and thus enhance the tumor region. Then the images are checked for
abnormality using fuzzy symmetric measure (FSM). If abnormal, then Otsu’s thresholding is used to extract
the tumor region. Experiments with the proposed method were done on 17 datasets. Various evaluation
parameters were used to validate the proposed method. The predictive accuracy (PA) and dice coefficient
(DC) values of proposed method reached maximum.
Mri image registration based segmentation framework for whole hearteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentatio...IJECEIAES
Fetal biometric size such as abdominal circumference (AC) is used to predict fetal weight or gestational age in ultrasound images. The automatic biometric measurement can improve efficiency in the ultrasonography examination workflow. The unclear boundaries of the abdomen image and the speckle noise presence are the challenges for the automated AC measurement techniques. The main problem to improve the accuracy of the automatic AC segmentation is how to remove noise while retaining the boundary features of objects. In this paper, we proposed a hybrid ultrasound image denoising framework which was a combination of spatial-based filtering method and multiresolution based method. In this technique, an ultrasound image was decomposed into subbands using wavelet transform. A thresholding technique and the anisotropic diffusion method were applied to the detail subbands, at the same time the bilateral filtering modified the approximation subband. The proposed denoising approach had the best performance in the edge preservation level and could improve the accuracy of the abdominal circumference segmentation.
EV-SIFT - An Extended Scale Invariant Face Recognition for Plastic Surgery Fa...IJECEIAES
This paper presents a new technique called Entropy based SIFT (EV-SIFT) for accurate face recognition after the plastic surgery. The corresponding feature extracts the key points and volume of the scale-space structure for which the information rate is determined. This provides least effect on uncertain variations in the face since the entropy is the higher order statistical feature. The corresponding EV-SIFT features are applied to the Support vector machine for classification. The normal SIFT feature extracts the key points based on the contrast of the image and the V- SIFT feature extracts the key points based on the volume of the structure. However, the EV- SIFT method provides both the contrast and volume information. Thus EV-SIFT provide better performance when compared with PCA, normal SIFT and VSIFT based feature extraction.
This document summarizes an article that presents two methods for iris segmentation: 1) An automatic segmentation method that uses circular Hough transform to locate the iris boundaries and normalize the iris region using rubber sheet modeling. This method achieves 84% accuracy. 2) A window technique that first finds the pupil center using thresholding and chain coding, then extracts information along radial lines from the pupil center to locate edges, achieving 99% accuracy for pupil boundary detection and 79% for iris edges. The document provides details on each step of the automatic segmentation method and explains the window technique at a high level.
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.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document provides an overview of medical image segmentation techniques. It discusses fundamental medical image processing steps such as image capture, enhancement, segmentation, and feature extraction. Various segmentation methods are reviewed, including thresholding, region-based, edge detection, and hybrid techniques. The document emphasizes the need for developing robust segmentation methods that can recognize malignant growths early to improve cancer treatment outcomes.
This paper proposes a novel technique for detecting point landmarks in 3D medical images based on phase congruency (PC). A bank of 3D log-Gabor filters is used to compute energy maps from the images. These energy maps are combined to form the PC measure, which is invariant to intensity variations and provides good feature localization. Significant 3D point landmarks are detected by analyzing the eigenvectors of PC moments computed at each point. The method is demonstrated on head and neck images for radiation therapy planning.
Discover all Bretagne has to offer at in-Cosmetics 2016Laurence KNECHT
This document lists exhibitors at the IN-COSMETICS trade show in Paris from 12-14 April 2016. It provides the names of 154 exhibitor companies in the Bretagne Pavilion at Hall 1 of the event, as well as their booth numbers. The Bretagne Commerce International and Cosmetic Valley France Pavillion are also listed as accompanying certain exhibitors.
Sara Abo Shady has over 9 years of experience in flavor development. She has worked as a Senior Flavorist and R&D Manager for a French company in Egypt, developing new products for savory, sweet, chocolate, and beverage applications. Sara has extensive experience matching and creating flavors for snacks, meat, dairy, and tobacco products. She has traveled extensively for training programs and laboratory testing. Sara holds a B.Sc. in Agriculture from Cairo University and is proficient in English, German, and Microsoft Office applications.
Embassy of the United States
Tunisia.usembassy.gov
June - July 2016
Volume 3
Issue 1
Highlights:
• Picks
• Economic Support Fund/Middle East Partnership Initiative Alumni Corner
• Spotlight
• Snapshot
• Announcements
Inside this issue:
• Youth council in Mahdia mobilizes 100 people to spur community improvement
• 29 Civil society organizations trained to act as watchdogs on budget transparency
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For a Better Future
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Literature survey for 3 d reconstruction of brain mri imageseSAT Journals
Abstract
Since Doctors had only the 2D Image Data to visualize the tumors in the MRI images, which never gave the actual feel of how the tumor would exactly look like . The doctors were deprived from the exact visualization of the tumor the amount of the tumor to be removed by operation was not known, which caused a lot of deformation in the faces and structure of the patients face or skull. The diversity and complexity of tumor cells makes it very challenging to visualize tumor present in magnetic resonance image (MRI) data. Hence to visualize the tumor properly 2D MRI image has to be converted to 3D image. With the development of computer image processing technology, three-dimensional (3D) visualization has become an important method of the medical diagnose, it offers abundant and accurate information for medical experts. Three-dimensional (3-D) reconstruction of medical images is widely applied to tumor localization; surgical planning and brain electromagnetic field computation etc. The brain MR images have unique characteristics, i.e., very complicated changes of the gray-scales and highly irregular boundaries. Traditional 3-D reconstruction algorithms are challenged in solving this problem. Many reconstruction algorithms, such as marching cubes and dividing cubes, need to establish the topological relationship between the slices of images. The results of these traditional approaches vary depending on the number of input sections, their positions, the shape of the original body and the applied interpolation technique. These make the task tedious and time-consuming. Moreover, satisfied reconstruction result may not even be obtained when the highly irregular objects such as the encephalic tissues are considered. Due to complexity and irregularity of each encephalic tissue boundary, three-dimensional (3D) reconstruction for MRI image is necessary. A Literature survey is done to study different methods of 3D reconstruction of brain images from MRI images. Keywords: 3-D reconstruction, region growing, segmentation method, immune algorithm (IA), one class support vector machine (OCSVM) and sphere shaped support vector machine (SSSVM).
The document describes a novel method for localizing the pupil in eye gaze tracking systems. The proposed method fuses existing pupil localization techniques, including Hough transform, gray projection, and coarse positioning. This fusion approach aims to improve localization accuracy while lowering false detections. The method first preprocesses the eye image using filtering, edge detection, and thresholding/binarization. It then applies the fused techniques of Hough transform, gray projection, and coarse positioning to the preprocessed image to accurately detect the pupil boundary and center coordinates in a robust manner. The results are expected to provide better pupil localization compared to existing individual techniques.
Intelligent computing techniques on medical image segmentation and analysis a...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A robust iris recognition method on adverse conditionsijcseit
As a stable biometric system, iris has recently attracted great attention among the researchers. However,
research is still needed to provide appropriate solutions to ensure the resistance of the system against error
factors. The present study has tried to apply a mask to the image so that the unexpected factors affecting
the location of the iris can be removed. So, pupil localization will be faster and robust. Then to locate the
exact location of the iris, a simple stage of boundary displacement due to the Canny edge detector has been
applied. Then, with searching left and right IRIS edge point, outer radios of IRIS will be detect. Through
the process of extracting the iris features, it has been sought to obtain the distinctive iris texture features by
using a discrete stationary wavelets transform 2-D (DSWT2). Using DSWT2 tool and symlet 4 wavelet,
distinctive features are extracted. To reduce the computational cost, the features obtained from the
application of the wavelet have been investigated and a feature selection procedure, using similarity
criteria, has been implemented. Finally, the iris matching has been performed using a semi-correlation
criterion. The accuracy of the proposed method for localization on CASIA-v1, CASIA-v3 is 99.73%,
98.24% and 97.04%, respectively. The accuracy of the feature extraction proposed method for CASIA3 iris
images database is 97.82%, which confirms the efficiency of the proposed method.
Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Brea...TELKOMNIKA JOURNAL
Breast cancer is the most commonly diagnosed cancer among females worldwide. Computer aided diagnosis (CAD) was developed to assist radiologists in detecting and evaluating nodules so it can improve diagnostic accuracy, avoid unnecessary biopsies, reduce anxiety and control costs. This research proposes a method of CAD for breast ultrasound images based on margin and posterior acoustic features. It consists of preprocessing, segmentation using active contour without edge (ACWE) and morphological, feature extraction and classification. Texture and geometry analysis was used to determine the characteristics of the posterior acoustic and margin nodules. Support vector machines (SVM) provided better performance than multilayer perceptron (MLP). The performance of proposed method achieved the accuracy of 91.35%, sensitivity of 92.00%, specificity of 89.66%, PPV of 95.83%, NPV of 81.26% and Kappa of 0.7915. These results indicate that the developed CAD has potential to be implemented for diagnosis of breast cancer using ultrasound images.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science, Engineering and Information Technology. The Journal looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
A new hybrid method for the segmentation of the brain mrissipij
The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue
characterization, presenting an interest in the follow-up of various pathologies such as the multiple
sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The
extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is
proposed; finally the last section is organized around an experimental part allowing the study of the
behavior of our model on textured images. In the aim to validate our model, different segmentations were
down on pathological Brain MRI, the obtained results have been compared to the results obtained by
another models. This results show the effectiveness and the robustness of the suggested approach.
Detection of uveal melanoma using fuzzy and neural networks classifiersTELKOMNIKA JOURNAL
The use of image processing is increasingly utilized for disease detection. In this article, an algorithm is proposed to detect uveal melanoma (UM) which is a type of intraocular cancer. The proposed method integrates algorithms related to iris segmentation and proposes a novel algorithm for the detection of UM from the approach of fuzzy logic and neural networks. The study case results show 76% correct classification in the fuzzy logic system and 96.04% for the artificial neural networks.
Automatic whole-body bone scan image segmentation based on constrained local ...journalBEEI
In Indonesia, cancer is very burdensome financially for sufferers as well as for the country. Increasing the access to early detection of cancer can be a solution to prevent the situation from worsening. Regarding the problem of cancer lesion detection, a whole-body bone scan image is the primary modality of nuclear medicine for the detection of cancer lesions on a bone. Therefore, high segmentation accuracy of the whole-body bone scan image is a crucial step in building the shape model of some predefined regions in the bone scan image where metastasis was predicted to appear frequently. In this article, we proposed an automatic whole-body bone scan image segmentation based on constrained local model (CLM). We determine 111 landmark points on the bone scan image as the input for the model building step. The resulting shape and texture model are further used in the fitting step to estimate the landmark points of predefined regions. We use the CLM-based approach using regularized landmark mean-shift (RLMS) to lessen the effect of ambiguity, which was struggled by the CLM-based approach. From the experimental result, we successfully show that our proposed image segmentation system achieves higher performance than the general CLM-based approach.
The document discusses a proposed approach for personal identification using palmprint biometrics based on principal line extraction. It begins with an abstract summarizing preprocessing using Gaussian filtering and ROI extraction to smooth images and isolate the palm region. Canny edge detection is then used to extract principal lines by analyzing edge direction and gradient strength. The lines are traced and non-maximum edges suppressed to extract key lines. Templates are generated by dividing images into blocks and identifying those containing lines. Identification is performed by comparing templates to stored ones using distance matching. The document then provides context on palmprint identification and prior approaches before detailing the proposed Canny edge detection based method.
A SIMPLE IMAGE PROCESSING APPROACH TO ABNORMAL SLICES DETECTION FROM MRI TUMO...ijma
This paper proposed a method for brain tumor detection from the magnetic resonance imaging (MRI) of
human head scans. The proposed work explained the tumor detection process by means of image
processing transformations and thresholding technique. The MRI images are preprocessed by
transformation techniques and thus enhance the tumor region. Then the images are checked for
abnormality using fuzzy symmetric measure (FSM). If abnormal, then Otsu’s thresholding is used to extract
the tumor region. Experiments with the proposed method were done on 17 datasets. Various evaluation
parameters were used to validate the proposed method. The predictive accuracy (PA) and dice coefficient
(DC) values of proposed method reached maximum.
Mri image registration based segmentation framework for whole hearteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentatio...IJECEIAES
Fetal biometric size such as abdominal circumference (AC) is used to predict fetal weight or gestational age in ultrasound images. The automatic biometric measurement can improve efficiency in the ultrasonography examination workflow. The unclear boundaries of the abdomen image and the speckle noise presence are the challenges for the automated AC measurement techniques. The main problem to improve the accuracy of the automatic AC segmentation is how to remove noise while retaining the boundary features of objects. In this paper, we proposed a hybrid ultrasound image denoising framework which was a combination of spatial-based filtering method and multiresolution based method. In this technique, an ultrasound image was decomposed into subbands using wavelet transform. A thresholding technique and the anisotropic diffusion method were applied to the detail subbands, at the same time the bilateral filtering modified the approximation subband. The proposed denoising approach had the best performance in the edge preservation level and could improve the accuracy of the abdominal circumference segmentation.
EV-SIFT - An Extended Scale Invariant Face Recognition for Plastic Surgery Fa...IJECEIAES
This paper presents a new technique called Entropy based SIFT (EV-SIFT) for accurate face recognition after the plastic surgery. The corresponding feature extracts the key points and volume of the scale-space structure for which the information rate is determined. This provides least effect on uncertain variations in the face since the entropy is the higher order statistical feature. The corresponding EV-SIFT features are applied to the Support vector machine for classification. The normal SIFT feature extracts the key points based on the contrast of the image and the V- SIFT feature extracts the key points based on the volume of the structure. However, the EV- SIFT method provides both the contrast and volume information. Thus EV-SIFT provide better performance when compared with PCA, normal SIFT and VSIFT based feature extraction.
This document summarizes an article that presents two methods for iris segmentation: 1) An automatic segmentation method that uses circular Hough transform to locate the iris boundaries and normalize the iris region using rubber sheet modeling. This method achieves 84% accuracy. 2) A window technique that first finds the pupil center using thresholding and chain coding, then extracts information along radial lines from the pupil center to locate edges, achieving 99% accuracy for pupil boundary detection and 79% for iris edges. The document provides details on each step of the automatic segmentation method and explains the window technique at a high level.
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.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document provides an overview of medical image segmentation techniques. It discusses fundamental medical image processing steps such as image capture, enhancement, segmentation, and feature extraction. Various segmentation methods are reviewed, including thresholding, region-based, edge detection, and hybrid techniques. The document emphasizes the need for developing robust segmentation methods that can recognize malignant growths early to improve cancer treatment outcomes.
This paper proposes a novel technique for detecting point landmarks in 3D medical images based on phase congruency (PC). A bank of 3D log-Gabor filters is used to compute energy maps from the images. These energy maps are combined to form the PC measure, which is invariant to intensity variations and provides good feature localization. Significant 3D point landmarks are detected by analyzing the eigenvectors of PC moments computed at each point. The method is demonstrated on head and neck images for radiation therapy planning.
Discover all Bretagne has to offer at in-Cosmetics 2016Laurence KNECHT
This document lists exhibitors at the IN-COSMETICS trade show in Paris from 12-14 April 2016. It provides the names of 154 exhibitor companies in the Bretagne Pavilion at Hall 1 of the event, as well as their booth numbers. The Bretagne Commerce International and Cosmetic Valley France Pavillion are also listed as accompanying certain exhibitors.
Sara Abo Shady has over 9 years of experience in flavor development. She has worked as a Senior Flavorist and R&D Manager for a French company in Egypt, developing new products for savory, sweet, chocolate, and beverage applications. Sara has extensive experience matching and creating flavors for snacks, meat, dairy, and tobacco products. She has traveled extensively for training programs and laboratory testing. Sara holds a B.Sc. in Agriculture from Cairo University and is proficient in English, German, and Microsoft Office applications.
Embassy of the United States
Tunisia.usembassy.gov
June - July 2016
Volume 3
Issue 1
Highlights:
• Picks
• Economic Support Fund/Middle East Partnership Initiative Alumni Corner
• Spotlight
• Snapshot
• Announcements
Inside this issue:
• Youth council in Mahdia mobilizes 100 people to spur community improvement
• 29 Civil society organizations trained to act as watchdogs on budget transparency
• Improved court administration in Tunisia
• USAID/UNIDO helping Nacira grow her business through the Mashrou3i project
• Interesting links
• Marketplace
For a Better Future
Contact us:
Les Berges du Lac 2045 - Tunisia
Phone: 71 107 000
Fax: 71 107 090
E-mail: ForeignAssistanceTunis@state.gov
Sara Abo Shady is a Senior Flavor Manager at Royal Pack Company in Cairo, Egypt with over 9 years of experience in flavor development for snacks, meats, dairy products, and beverages. She has extensive experience matching and creating flavors in liquid, powder, and spray dried forms. Sara is responsible for new product development, customer support, flavor library management, and ensuring products meet international quality standards. She holds a B.Sc. in Agriculture from Cairo University and is fluent in Arabic, English, and German.
This document discusses various 3D geometric transformations including translation, scaling, rotation, and coordinate transformations. It provides details on:
1) How translation, uniform scaling, and relative scaling transformations work in 3D space.
2) How rotations around the x, y, z axes as well as general 3D rotations around arbitrary axes are performed.
3) How quaternions can be used to represent rotations and how rotation matrices are derived from quaternions.
4) How reflections, shears, and different coordinate systems require coordinate transformations between systems.
Notes 2D-Transformation Unit 2 Computer graphicsNANDINI SHARMA
Notes of 2D Transformation including Translation, Rotation, Scaling, Reflection, Shearing with solved problem.
Clipping algorithm like cohen-sutherland-hodgeman, midpoint-subdivision with solved problem.
Brain tumor visualization for magnetic resonance images using modified shape...IJECEIAES
3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
This document provides a literature survey on methods for detecting brain tumors from MRI images. It discusses several segmentation techniques that have been used for this purpose, including thresholding, edge-based, region-based, k-means clustering, fuzzy c-means clustering, and optimization methods like ant colony optimization, genetic algorithms, and particle swarm optimization. The document reviews related work comparing these methods and evaluates their performance based on metrics like PSNR and RMSE. It concludes that while no single universal method exists, fuzzy c-means is well-suited for medical image segmentation tasks due to its simplicity and ability to provide faster clustering.
Literature Survey on Detection of Brain Tumor from MRI Images IOSR Journals
This document provides a literature survey on methods for detecting brain tumors from MRI images. It discusses several segmentation and clustering techniques that have been used for this purpose, including thresholding, edge-based segmentation, region-based segmentation, fuzzy c-means clustering, and k-means clustering. The document also reviews related work applying these methods and evaluates their effectiveness at automatically detecting and segmenting brain tumors from MRI data.
Brain Tumor Area Calculation in CT-scan image using Morphological Operationsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document presents a method for calculating the area of brain tumors in CT scan images using morphological operations. The proposed method involves 10 steps: 1) inputting a CT scan image, 2) cropping the image, 3) converting to grayscale, 4) applying morphological gradient filtering, 5) histogram equalization, 6) selecting the region of interest, 7) subtracting and thresholding, 8) morphological closing, and 9) calculating the tumor area. The method is tested on different tumor images and more accurately calculates tumor area compared to radiologists who assume tumor shapes. The algorithm provides automated tumor highlighting and area calculation to assist physicians.
Survey on Segmentation Techniques for Spinal Cord ImagesIIRindia
Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.
Three-dimensional imaging techniques: A literature review By; Orhan Hakki Ka...Dr. Yahya Alogaibi
This literature review discusses various 3D imaging techniques used in dentistry and orthodontics. It begins by providing background on the development of 3D imaging since the discovery of X-rays. It then discusses disadvantages of traditional 2D cephalometry. The bulk of the review covers different 3D imaging modalities including CT, CBCT, MCT, 3D laser scanning, and their uses and advantages/disadvantages in orthodontics. Key applications discussed are impacted teeth detection, airway analysis, TMJ evaluation, and cleft lip/palate treatment planning.
1) The document presents an integrated technique for detecting brain tumors in MRI images that combines modified texture-based region growing segmentation and edge detection.
2) The technique first performs pre-processing on MRI images, then uses modified texture-based region growing to segment regions. It then applies edge detection to extract the tumor region.
3) Experimental results show the integrated technique provides more accurate tumor detection compared to individual segmentation methods and manual segmentation.
Left Ventricle Volume Measurement on Short Axis MRI Images Using a Combined R...IDES Editor
Segmentation and volume measurement of the
cardiac ventricles is an important issue in cardiac disease
diagnosis and function assessment. Cardiac Magnetic
Resonance Imaging (CMRI) is the current reference standard
for the assessment of both left and right ventricular volumes
and mass. Several methods have been proposed for
segmentation and measurement of cardiac volumes like
deformable models, active appearance, shape models, atlas
based methods, etc. In this paper a novel method is proposed
based on a parametric superellipse model fitting for
segmentation and measurement volume of the left ventricle
on cardiac Cine MRI images. Superellipses can be used to
represent in a large variety of shapes. For fitting superellipse
on MR images, a set of data points have been needed as a
partial data. This data points resulted from a semi-automatic
region growing method that segment the homogenous region
of the left ventricle. Because of ellipsoid shape of left ventricle,
fitting superellipse on cardiac cine MRI images has excellent
accuracy. The results show better fitting and also less
computation and time consuming compared to active contour
methods, which is commonly used method for left ventricle
segmentation.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2iaemedu
The document summarizes a proposed method for detecting brain tumors in MRI images in 4 steps:
1. Skull stripping and smoothing are performed to isolate the brain region.
2. Histogram thresholding is used to detect if the brain is normal or abnormal by comparing histograms of halves of the brain image.
3. For abnormal brains, a modified gradient vector flow (GVF) model is used to create a force field and detect the tumor contour.
4. The area of the tumor is then calculated. The method aims to minimize segmentation time by skipping segmentation for normal brains detected in Step 2. Validation with expert segmentation is performed.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2IAEME Publication
The document describes a new method for detecting and segmenting brain tumors in MRI images. It combines histogram thresholding, modified gradient vector flow, and morphological operators. Histogram thresholding is used to detect if the brain is normal or abnormal. If abnormal, modified GVF is used to segment the tumor contour. Otherwise, segmentation is skipped to minimize computation time. The method was tested on many MRI brain images and tumor detection and dimensions were validated against expert segmentation. It provides an efficient and computationally inexpensive approach for brain tumor detection and segmentation in MRI images.
Detection of Prostate Cancer Using Radial/Axial Scanning of 2D Trans-rectal U...journalBEEI
The search for improvement in the result of segmentation of regions of interest in medical images has continued to be a source of challenge to researchers. Several research efforts have gone in to delineate regions of interest in the prostate gland from Trans-rectal ultrasound (TRUS) 2D-images. In this work, we develop a fast algorithm based on radial/axial scanning of the pixels of the prostate gland image with the goal of detecting hyper-echoic pixels that are bound within the boundaries of the gland TRUS 2D-images. The algorithm implements expert knowledge and utilizes the features extracted from the intensity of the TRUS images, primarily the relative intensity and gradient to delineate region of interest. It employs radial/axial scanning of the image from common seed point automatically selected to detect the region of the gland and subsequently hyper-echoic pixels which indicate suspected cancerous tissue cites. Evaluation of the algorithm performance was done by comparing detection result with that of expert radiologists. The detection algorithm gave an average accuracy of 88.55% and sensitivity of 71.65%.
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.
BFO – AIS: A Framework for Medical Image Classification Using Soft Computing ...ijsc
Medical images provide diagnostic evidence/information about anatomical pathology. The growth in database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI), Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work. CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio therapy. Medical information systems goals are to deliver information to right persons at the right time and place to improve care process quality and efficiency. This paper proposes an Artificial Immune System (AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO) with Local Search (LS) for medical image classification.
BFO – AIS: A FRAME WORK FOR MEDICAL IMAGE CLASSIFICATION USING SOFT COMPUTING...ijsc
Medical images provide diagnostic evidence/information about anatomical pathology. The growth in
database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI),
Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work.
CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more
reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio
therapy. Medical information systems goals are to deliver information to right persons at the right time and
place to improve care process quality and efficiency. This paper proposes an Artificial Immune System
(AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO)
with Local Search (LS) for medical image classification.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
Similar to Reeb Graph for Automatic 3D Cephalometry (20)
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
How Barcodes Can Be Leveraged Within Odoo 17Celine George
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
1. Mestiri Makram & Hamrouni Kamel
International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 17
Reeb Graph for Automatic 3D Cephalometry
Mestiri Makram mmestiri@gmail.com
Enit/Electrical/Image Processing
Enit
Elnasr 2, 2037, Tunisia
Hamrouni Kamel Hamrouni.kamel@lsts.com
Enit/Electrical/Image Processing
Enit
Elnasr 2, 2037, Tunisia
Abstract
The purpose of this study is to present a method of three-dimensional computed tomographic
(3D-CT) cephalometrics and its use to study cranio/maxilla-facial malformations. We propose a
system for automatic localization of cephalometric landmarks using reeb graphs. Volumetric
images of a patient were reconstructed into 3D mesh. The proposed method is carried out in
three steps: we begin by applying 3d mesh skull simplification, this mesh was reconstructed from
a head volumetric medical image, and then we extract a reeb graph. Reeb graph mesh extraction
represents a skeleton composed in a number of nodes and arcs. We are interested in the node
position; we noted that some reeb nodes could be considered as cephalometric landmarks under
specific conditions. The third step is to identify these nodes automatically by using elastic mesh
registration using “thin plate” transformation and clustering. Preliminary results show a landmarks
recognition rate of more than 90%, very close to the manually provided landmarks positions made
by a medical stuff.
Keywords: Reeb Graph, Cephalometry Landmarks, Thin Plate, LCP.
1. INTRODUCTION
Radiographic cephalometry has been one of the most important diagnostic tools in maxillofacial
diseases, since its introduction in the early 1930. It deals with the morphological scientific study of
the dimensions of all the structures present in a human head, usually through the use of
standardized lateral head radiographs. Generations of doctors have relied on the interpretation of
these images for their diagnosis and treatment planning as well as for the long-term follow-up of
growth and treatment results. Also in the planning for surgical orthodontic corrections of jaw
discrepancies, lateral/antero-posterior cephalograms have been valuable tools.
Parameters measurement is based on a set of agreed upon feature point’s landmarks. The
detection of the landmarks plays an essential role in diagnosis and treatment planning by doctors.
Head-growing analyzing with anatomical landmarks was first proposed by Arc Thomson, in 1917
[1]. His approach was based on a deforming grid. Changes in the landmarks position resulted in
deformations of the grid. His method was applied not only to quantify the effects of growth, but
also to relate different individuals and even different species. A malocclusion is a misalignment of
teeth or incorrect relation between the teeth of the two dental arches, Broadbent [2] and later
Brodie [3] applied a method based in landmarks to quantify malocclusions and study their effects.
2. Mestiri Makram & Hamrouni Kamel
International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 18
They used radio-graph image to define the Landmarks, head structure like bony or soft tissue,
had to be identified. This approach was used for measurement specification at an individual of
known age, sex and race to quantify head anatomy differences for diagnosis, such as the one
shown in Figure 1.
FIGURE 1: Manual Extraction of Cephalometry Landmarks in X-ray Image.
Downs [4] proposed in 1948 the first cephalometric analysis method. His approach was based on
measurements of 10 angles from a lateral radiographs from a group of selected individuals; he
calculates the average values and gives them a clinical significance. His approach was the basis
for most methods used at present.
Ricketts [5] and Steiner [6] proposed traced lines between significant landmarks, so their length
and angles can be measured and compared with standard values shown in Figure 2.
FIGURE 2: Line Traced Between Significant Landmarks [6].
Locating landmarks depends on medical expertise to locate the landmarks manually. This can
take an experienced orthodontist up to 30 min. The process is time consuming, subject to human
error and tedious.
An automated system would help to eliminate the above-mentioned problems and hence produce
repeatable results.
2. RELATED WORKS
All the automatic cephalometric landmarks recognition methods use 2D image processing, the
various methods used by researchers in this field found in the literature can be divided into three
different approaches: knowledge-based feature detection, pattern matching and neural networks
with fuzzy inference system.
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International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 19
Levy [7] proposed in 1986 the first automatic extraction of cephalometric landmarks. His method
was based on knowledge based line extraction technique. He begins with applying median filter
and histogram equalization to enhance the X-rays images, then he applies a Mero-Vassy
operator [8] to extract relevant edges using knowledge-based line tracker. The landmarks are
then located according to their geometric definition online crossing. This method requires good X-
rays image (cannot be guaranteed in practice). Parthasaraty [9] proposed an enhancement of
levy approach by introducing a multi resolution pyramidal analysis of X-rays images to reduce the
processing time.
Yen [10], Contereras [11], Jackson [12], Cohen [13] and Davis and Forsyth [14] presented similar
knowledge-based edge tracking methods. These methods depend highly on X-ray image quality
and can be used only for landmarks located on an edge.
The second approache used mathematical or statistical models to narrow down the search area
for each landmark, and then shape-matching techniques are used to locate the exact location of
each landmark. Cardillo and Sid-Ahmed [15] proposed a method based on a combination of
mathematical modeling methods, like affine transformation to remove the shifts, rotations and
size differences to reduce the size of the search area. Then, they applied a shape recognition
algorithm based on gray-scale mathematical morphology to locate the landmarks. The method
locates 76% of the landmarks within 2 mm. Grauetal[16], adds a line detection module to select
the most significant lines in Sid-Ahmed[15] approach. Then, he applies mathematical morphology
transformations for shape recognition. The disadvantage of theses methods is that it’s very
sensitive to noise present in X-rays images. Desvignes [17] proposed a statistical method based
on estimation of landmarks locations using adaptive coordinate space where locations are
registered. The method was tested on a set of 58 X-rays, of which 28 were used for training. They
obtain 99.5% of the landmarks but with 4:5 mm mean error between the real position and the
estimated position, far from the authorized range (±2 mm). Hutton [18] used active shape models
for cephalometric landmarking. They established a template of possible deformations from a
training set of hand-annotated image. The result was 35% of 16 landmarks within an acceptable
range of ±2 mm. Implementation could be used as a first estimation location of the landmarks.
The third category of researchers used neural networks and fuzzy inference systems to locate the
landmarks. Uchino [19] proposed a fuzzy learning machine that could learn the relation between
the gray levels of the image and the location of the landmarks. They begin by dividing the image
into nine blocks, each one is an input to the fuzzy learning machine. The weights were adjusted
by learning the coordinates of the landmark. The block containing the landmark was divided in
nine separate blocks until landmark location is obtained. This method produced an average error
of 2:3 mm. this method depends highly on scale, rotation and shift of X-rays images. Innes [20]
highlights regions containing craniofacial features using Pulse Coupled Neural Networks (PCNN)
from X-rays images. They get 36.7% rate for the region containing sella landmark, 88.1% for the
region containing the chin landamark, and 93.6% for the region containing the nose landmark.
The most disadvantage of PCNN’s method is that they require a considerable manual contribution
to set the required parameters.
In this paper, we are interested in the extension of 2D X-rays to 3D image cephalometry analysis.
Some work has been carried out to study the potential advantages of 3D imaging methods, such
as computed tomography for cephalometric analysis. Kragskov [21] made a comparative result,
using human dry skulls. Ferrario [22] studied 3D facial morphometry using infrared cameras, but
only as a supplement for classic cephalometry.
We propose a novel method based on the use of reeb graph for Automatic localization of
craniofacial landmarks. The task is a difficult one due to the complexity and variability of
cephalometry landmarks.
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International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 20
3. PROPOSED METHOD
As seen in previous works, many researchers have attempted automatic cephalometric
landmarks detections. However, a major drawback of the existing techniques is that they use a
2D representation of a 3D structure. In this paper we are interested in a 3D cephalometric
landmarks. The development of spiral CT and cone beam CT has revolutionized the medical
image techniques, the former providing outstanding resolution and the latter, with its low cost,
allowing unique accessibility, as a result 3-D imaging has become an essential tool in planning
and managing the treatment of facial deformity.
3.1 3D Mesh Database
The image data-base in composed of Dicom images stored in a series of 2D grey level images
from a Spiral CT scan (with 512 x 512 matrixes, 110 kV, and 80 mAs) performed at 1 mm slice
thickness. The extraction of hard and soft head tissue (bones and skin) is done by using a
threshold and region growing technique. Each bones and skin parts are separately constructed
using marching cube algorithm as shown in Figure 3.
FIGURE 3: 3D Mesh Extraction of Hard and Soft Tissues.
The results of this extraction method are 3 meshes: interior/exterior skull bones, and mandibular
bone Figure-4.
FIGURE 4: 3D Mesh Extraction from Volumetric CT-Scan .
3.2 3D Cephalometric Landmarks
After getting the mesh database, 3D cephalometric landmarks must be localized in hard tissue. In
the literature we found 20 hard landmarks, some of them are presented in table-1.
CT-Scan Image
Threshold and
region growing
Interior
Cranium bone
Exterior
Cranium bone
Mandibule Head Skin
3D mesh reconstruction
(Marching Cube)
Volumic grey level images
3D mesh images
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International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 21
No Abbreviation Hard Tissue
1 N Nasion is the midpoint of the frontonasal suture
2 S Sella is the centre of the hypophyseal fossa (sella turcica).
3 Po Porion is the most superior point of each external acoustic meatus.
4 Or Orbitale is the most inferior point of each infra- orbital rim.
5 ANS Anterior Nasal Spine is the most anterior midpoint of the anterior nasal spine of the maxilla.
6 PNS Posterior Nasal Spine is the most posterior midpoint of the posterior nasal spine of the palatine bone.
7 PMP Posterior Maxillary Point is the point of maximum concavity of the posterior border of the palatine bone in the horizontal plane at both sides.
8 UI Upper Incisor is the most mesial point of the tip of the crown of each upper central incisor.
9 LI Lower Incisor is the most mesial point of the tip of the crown of each lower central incisor.
10 UMcusp Upper Molar cusp is the most inferior point of the mesial cusp of the crown of each first upper molar in the profile plane.
TABLE 1: 10 of the 20 Cephalometric Landmarks Proposed by Swennen [23].
Figure 5 shows some sample of CT-scan images with located landmarks [23].
FIGURE 5: A: Right hard cephalometric landmarks, B: Facial hard cephalometric landmarks, C: Soft
cephalometric landmarks.
3.3 Automatic Localization of Craniofacial Landmarks
Figure 6 presented the bloc diagram of the proposed method for automatic localization of 3D
cephalometric landmarks.
After transforming the volumetric medical images to a 3D surface mesh, we need to make a
reference shape model containing all cephalometric landmarks information’s. This is made with
what is called “cephalometric head atlas”.
FIGURE 6: Automatic localization of 3D cephalometric landmarks using reeb graphs.
Cephalometric
Head Atlas
Patient Head Mesh
Mesh Simplification
(Edge Collapse)
Reeb Graph
Extraction
Elastic Registration
(Thin Plate Spline)
Automatic Landmarks
localisation
(NNB algorithm)
Rigid registration
(ICP)
A B C
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International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 22
To automatically identify 3D landmarks, we need to have prior knowledge information’s. We
create a cephalometric head atlas based on medical stuff help. This atlas is composed of skull
and skin meshes with 3D cephalometric landmarks as shown in Figure 7.
FIGURE 7: Cephalometric atlas of skull. A: initial clean skull mesh, B: Cephalometric landmarks and C:
Manual localization of cephalometric landmarks.
In our method we choose to automatically localize cephalometric landmarks using reeb graph.
Below we will expose the reeb graph extraction.
The Reeb graph is a chart of connectivity on a surface between its critical points. The main
advantage of the reeb graph is that it makes it possible to represent 3D anatomy in a simple
topology way. It is composed of nodes and arcs; each whole of level associates a node. The
graph of Reeb is obtained starting from the computation of a µ function introduced by the theory
of Morse [24] defined on the closed surface of an object in its critical points [25].
We define µ: S ! R defined on surface S of a 3D object. The Reeb graph is a space quotient of
the graph of µ in S, defined by the relation of following equivalence between X є S and Y є S:
· µ(X)=µ(Y) (1)
X~Y " · X et Y are in the same Related component of
µ-1(µ(X)), and the image µ(Y) is S.
The aspect of the graph is entirely related to the choice of the µ function, which determine the
properties of stability and invariance of the resulting graphs. Several functions of application were
proposed in the state of the art for the construction of Reeb graphs [26]. One example is the
function (µ(ν(x,y,z)) = z / ν є S), adapted well for the models whose points are mainly distributed
in the Z-axis direction.
FIGURE 8: Reeb Graph with a z Projection µ Function.
A B C
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International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 23
The function suggested in [27] as «outdistances geodesic extrema of curve » is based on growth
of areas starting from local Gaussian curves on tops (germs). The results depend on the position
of the germs and require no disturbed data; it’s a precise calculation of the local curves. Hilaga
[27] defines a function by computing the distance from a point of the surface to the center of mass
G of the object:
(µ(ν) = d(G, ν) / ν є S et d : Euclidian distance) (2)
The function defined by Thierny[28] at the characteristic points and the geodesic distances is
defined by the following equation:
µμ v = 1 − δ v, v! (3)
With δ v, v! = min!"!!!δ(v, v!!
)
Where δ is the function of the geodesic distances and v! are characteristic points. The function
suggested in [29] is the integral of the geodetic distances g(v, p) of v to the other point’s p of the
surface (4).
µμ v = g v, p dS!!!
. (4)
In our approach we choose this µμ function and tested it with different mesh simplification. Figure-9
shows an example of reeb graph extraction.
FIGURE 9: A: Mandibule Patient Mesh, B: Reeb Graph Extraction.
Surface mesh simplification is the process of reducing the faces number used in the mesh
surface, while keeping preserved as much as possible the overall shape, volume and boundaries.
Cignoni [29] proposed a comparison of mesh simplification algorithms, and divided these
methods into 7 approaches: coplanar facets merging, controlled vertex/edge/face decimation, re-
tiling, energy function optimization, vertex clustering, wavelet-based approaches, and
simplification via intermediate hierarchical representation.
We test many simplification methods with reeb graph transformation, and we choose to use the
edge collapse method. These techniques reduce a model’s complexity by repeated use of the
simple edge collapse operation. Researchers have proposed various methods of determining the
“minimal cost edge” to collapse at each step (figure-10).
FIGURE 10: Edge Collapse Algorithm [30].
A C
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International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 24
We try to define the effect of the edge collapse simplification process on the reeb
graph extraction. The result in shown in figure-11
FIGURE 11: Patient Skull Simplification. A: 332.932 vertices and 662.564 faces, B: 20.047 vertices and
38.958 faces and C: 3.887 vertices and 7320 faces.
We notice that, after mesh simplification, the number of nodes in the reeb graph decreased and
they became closed to cephalometric landmarks. And, of course, the computation time is reduced
Figure-12.
FIGURE 12: Decrease of reeb graph nodes and reconciliation with cephalometric lanmarks.
Mesh registration is a technique used to find a transformation for mapping a source mesh known
as a reference mesh and a target mesh. This technique associated to a head cephalometric atlas
should help us to get closer to cephalometric landmarks. Every elastic registration process should
begin with a rigid one. In this study, we targeted the rigid registration between two different
meshes using the ICP algorithm (Iterative Closest Point).
The principle of ICP is to iterate between a step of mapping data and another step of optimization
of rigid transformation until convergence. The transformation used for registration is composed of
a 3D rotation and translation. At each iteration, the Algorithm provides a list of matched points
and an estimate of the transformation. The algorithm converges when the error in distance
between matched points is less than a given threshold. The ICP algorithm is presented in the
following [30] where Np is the number of points CP1 and CP2 and k is the index of current
iteration. Figure-13 shows a example of rigid registration between mesh patient and
cephalometric mesh atlas.
FIGURE 13: Rigid registration between patient and the cephalometric atlas
A B C
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International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 25
Mesh elastic registration is a mesh deformation process; one of the transformations that are able
to represent elastic deformations is the thin-plate spline (TPS). Thin plate splines were introduced
by Bookstein[31] for geometric design. In 2D images, the TPS model describes the transformed
coordinates (xT,yT) both independently as a function(7) of the original coordinates (x, y):
(xT,yT)=(fx(x,y),fy(x,y)) (7)
The algorithm begins with a given displacements of a number of landmark points, the TPS model
interpolates those points, while maintaining maximal smoothness. For each landmark point (x,y),
the displacement is represented by an additional z-coordinate, and, for each point, the thin plate
is fixed at position (x, y, z). The strain energy is calculated by integrating the second derivative
over the entire surface that can be minimized by solving a set of linear equations.
dxdy
y
z
yx
z
x
z
R
∫∫ ⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
∂
∂
+⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
∂∂
∂
+⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
∂
∂
2
2
2
2222
2
2
.2 (8)
The TPS model for one of the transformed coordinates is given by parameter vectors a and D (9):
F(xT,yT)= a1+a2x+a3y+…+
( )( )∑=
−
n
i ii yxLFD1
,
(9)
Where F(r)= r2log(r) is the basis function, a = [a1 a2 a3 a4]T defines the affine part of the
transformation, D gives an additional non-linear deformation, and the Li are the landmarks that
the TPS interpolates figure-4.
FIGURE 14: A template configuration (left) and a target configuration (right) of five landmarks each.
The deformation right grid illustrates the thin-plate spline function between these configurations
as applied to the left regular grid.
The method interpolates some of the points using smoother transformation controlled by a
parameter µ, which weights the optimization of landmark distance and smoothness. For µ = 0,
there is full interpolation, while for very large µ, there is only an affine transformation. In our
methods the landmarks used in the TPS algorithm are the reeb graph nodes as shown in figure-
15.
FIGURE 15: Elastic Registration using Thin Plate.
10. Mestiri Makram & Hamrouni Kamel
International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 26
As result of the elastic registration, the patient reeb graph nodes become closer to the atlas
cephalometric head landmarks. Since, some reeb graph nodes are not cephalometric landmarks,
we have to select the real ones.
Automatic localization of cephalometry landmarks
This step starts with the creation of possible landmark localization in cephalometric atlas. Then, it
identifies the reeb graph nodes that exist in the mesh patient. For each node, it searches the
closest landmarks in the cephalometric atlas through the calculation of Euclidean distance
between an acceptable range of ±2 mm. Finally, as a result all patient reeb graph nodes are
associated to a nearest cephalometric landmarks[32].
FIGURE 16: Localization of Cephalometric Landmarks in Patient Skull.
4. TEST AND RESULTS
To validate our approach we asked a doctor to conduct manually a cephalometric analysis on the
mesh patient. We then calculate the error between its points and cephalometric landmarks
automatically localized by our method. The result is showed in the following table:
No Abbreviation Error in mm Recognized
1 N 0.5 Yes
2 S 2.6 No
3 Po 1.3 Yes
4 Or 0.4 Yes
5 ANS 0.7 Yes
6 PNS 2.8 No
7 PMP 2 Yes
8 UI 1.3 Yes
9 LI 1.7 Yes
10 UMcusp 1.8 Yes
11 LMcusp 0.9 Yes
12 Men 0.8 Yes
13 Go 1.5 Yes
14 Fz 1.7 Yes
15 Zy 0.3 Yes
16 A 0.4 Yes
17 B 0.5 Yes
18 Pog 0.8 Yes
19 Ba 0.6 Yes
20 Co 1.4 Yes
TABLE 2: Result of cephalometric landmarks detection
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International Journal of Image Processing (IJIP), Volume (8) : Issue (2) : 2014 27
Our method localized 18 landmarks on 20. This mean a percentage of 90% landmarks in patient
mesh was recognized.
FIGURE 17: Localization of A- Sella and B- PNS Landmarks.
The Sella landmark (S) is very difficult to be localized. It is not localized in the surface mesh, and
the doctors need to insert it taking into account the interior skull surface. The Posterior Nasal
Spine Landmark (PNS) is defined on the exo-cranial skull base view of the 3-D hard tissue
surface representation. This landmark depends highly on the accuracy of the mesh
reconstruction.
5. CONCLUSION
In this paper, we have proposed a novel method for automatic 3D localization of cephalometric
landmarks. Our approach is based on reeb graph nodes. In particular, we have presented results
for localized 90% of cephalometric landmarks in CT-Scan medical images. We initiated a new
approach for automatic three-dimensional cephalometric analysis. The proposed method needs
to be validated on a larger database.
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