SEPARATION OF OVERLAPPING OBJECT SEGMENTATION USING LEVEL SET WITH AUTOMATIC INITALIZATION ON DENTAL PANORAMIC RADIOGRAPH

To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%.  Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology

Semi Automatic Segmentation of a Rat Brain Atlas

A common approach to segment an MRI dataset is to use a standard atlas to identify different regions of interest. Existing 2D atlases, prepared by freehand tracings of templates, are seldom complete for 3D volume segmentation. Although many of these atlases are prepared in graphics packages like Adobe Illustrator® (AI), which present the geometrical entities based on their mathematical description, the drawings are not numerically robust. This work presents an automatic conversion of graphical atlases suitable for further usage such as creation of a segmented 3D numerical atlas. The system begins with DXF (Drawing Exchange Format) files of individual atlas drawings. The drawing entities are mostly in cubic spline format. Each segment of the spline is reduced to polylines, which reduces the complexity of data. The system merges overlapping nodes and polylines to make the database of the drawing numerically integrated, i.e. each location within the drawing is referred by only one point, each line is uniquely defined by only two nodes, etc. Numerous integrity diagnostics are performed to eliminate duplicate or overlapping lines, extraneous markers, open-ended loops, etc. Numerically intact closed loops are formed using atlas labels as seed points. These loops specify the boundary and tissue type for each area. The final results preserve the original atlas with its 1272 different neuroanatomical regions which are complete, non-overlapping, contiguous sub-areas whose boundaries are composed of unique polyline

Novel Techniques for Tissue Imaging and Characterization Using Biomedical Ultrasound

The use of ultrasound technology in the biomedical field has been widely increased in recent decades. Ultrasound modalities are considered more safe and cost effective than others that use ionizing radiation. Moreover, the use of high-frequency ultrasound provides means of high-resolution and precise tissue assessment. Consequently, ultrasound elastic waves have been widely used to develop non-invasive techniques for tissue assessment. In this work, ultrasound waves have been used to develop non-invasive techniques for tissue imaging and characterization in three different applications.;Currently, there is a lack of imaging modalities to accurately predict minute structures and defects in the jawbone. In particular, the inability of 2D radiographic images to detect bony periodontal defects resulted from infection of the periodontium. They also may carry known risks of cancer generation or may be limited in accurate diagnosis scope. Ultrasonic guided waves are sensitive to changes in microstructural properties, while high-frequency ultrasound has been used to reconstruct high-resolution images for tissue. The use of these ultrasound techniques may provide means for early diagnosis of marrow ischemic disorders via detecting focal osteoporotic marrow defect, chronic nonsuppurative osteomyelitis, and cavitations in the mandible (jawbone). The first part of this work investigates the feasibility of using guided waves and high frequency ultrasound for non-invasive human jawbone assessment. The experimental design and the signal/image processing procedures for each technique are developed, and multiple in vitro studies are carried out using dentate and non-dentate mandibles. Results from both the ultrasonic guided waves analysis and the high frequency 3D echodentographic imaging suggest that these techniques show great potential in providing non-invasive methods to characterize the jawbone and detect periodontal diseases at earlier stages.;The second part of this work describes indirect technique for characterization via reconstructing high-resolution microscopic images. The availability of well-defined genetic strains and the ability to create transgenic and knockout mice makes mouse models extremely significant tools in different kinds of research. For example, noninvasive measurement of cardiovascular function in mouse hearts has become a valuable need when studying the development or treatment of various diseases. This work describes the development and testing of a single-element ultrasound imaging system that can reconstruct high-resolution brightness mode (B-mode) images for mouse hearts and blood vessels that can be used for quantitative measurements in vitro. Signal processing algorithms are applied on the received ultrasound signals including filtering, focusing, and envelope detection prior to image reconstruction. Additionally, image enhancement techniques and speckle reduction are adopted to improve the image resolution and quality. The system performance is evaluated using both phantom and in vitro studies using isolated mouse hearts and blood vessels from APOE-KO and its wild type control. This imaging system shall provide a basis for early and accurate detection of different kinds of diseases such as atherosclerosis in mouse model.;The last part of this work is initialized by the increasing need for a non-invasive method to assess vascular wall mechanics. Endothelial dysfunction is considered a key factor in the development of atherosclerosis. Flow-mediated vasodilatation (FMD) measurement in brachial and other conduit arteries has become a common method to assess the endothelial function in vivo. In spite of the direct relationship that could be between the arterial wall multi-component strains and the FMD response, direct measurement of wall strain tensor due to FMD has not yet been reported in the literature. In this work, a noninvasive direct ultrasound-based strain tensor measuring (STM) technique is presented to assess changes in the mechanical parameters of the vascular wall during post-occlusion reactive hyperemia and/or FMD, including local velocities and displacements, diameter change, local strain tensor and strain rates. The STM technique utilizes sequences of B-mode ultrasound images as its input with no extra hardware requirement. The accuracy of the STM algorithm is assessed using phantom, and in vivo studies using human subjects during pre- and post-occlusion. Good correlations are found between the post-occlusion responses of diameter change and local wall strains. Results indicate the validity and versatility of the STM algorithm, and describe how parameters other than the diameter change are sensitive to reactive hyperemia following occlusion. This work suggests that parameters such as local strains and strain rates within the arterial wall are promising metrics for the assessment of endothelial function, which can then be used for accurate assessment of atherosclerosis

Effective 3D Geometric Matching for Data Restoration and Its Forensic Application

3D geometric matching is the technique to detect the similar patterns among multiple objects. It is an important and fundamental problem and can facilitate many tasks in computer graphics and vision, including shape comparison and retrieval, data fusion, scene understanding and object recognition, and data restoration. For example, 3D scans of an object from different angles are matched and stitched together to form the complete geometry. In medical image analysis, the motion of deforming organs is modeled and predicted by matching a series of CT images. This problem is challenging and remains unsolved, especially when the similar patterns are 1) small and lack geometric saliency; 2) incomplete due to the occlusion of the scanning and damage of the data. We study the reliable matching algorithm that can tackle the above difficulties and its application in data restoration. Data restoration is the problem to restore the fragmented or damaged model to its original complete state. It is a new area and has direct applications in many scientific fields such as Forensics and Archeology. In this dissertation, we study novel effective geometric matching algorithms, including curve matching, surface matching, pairwise matching, multi-piece matching and template matching. We demonstrate its applications in an integrated digital pipeline of skull reassembly, skull completion, and facial reconstruction, which is developed to facilitate the state-of-the-art forensic skull/facial reconstruction processing pipeline in law enforcement

Facial soft tissue segmentation

The importance of the face for socio-ecological interaction is the cause for a high demand on any surgical intervention on the facial musculo-skeletal system. Bones and soft-tissues are of major importance for any facial surgical treatment to guarantee an optimal, functional and aesthetical result. For this reason, surgeons want to pre-operatively plan, simulate and predict the outcome of the surgery allowing for shorter operation times and improved quality. Accurate simulation requires exact segmentation knowledge of the facial tissues. Thus semi-automatic segmentation techniques are required. This thesis proposes semi-automatic methods for segmentation of the facial soft-tissues, such as muscles, skin and fat, from CT and MRI datasets, using a Markov Random Fields (MRF) framework. Due to image noise, artifacts, weak edges and multiple objects of similar appearance in close proximity, it is difficult to segment the object of interest by using image information alone. Segmentations would leak at weak edges into neighboring structures that have a similar intensity profile. To overcome this problem, additional shape knowledge is incorporated in the energy function which can then be minimized using Graph-Cuts (GC). Incremental approaches by incorporating additional prior shape knowledge are presented. The proposed approaches are not object specific and can be applied to segment any class of objects be that anatomical or non-anatomical from medical or non-medical image datasets, whenever a statistical model is present. In the first approach a 3D mean shape template is used as shape prior, which is integrated into the MRF based energy function. Here, the shape knowledge is encoded into the data and the smoothness terms of the energy function that constrains the segmented parts to a reasonable shape. In the second approach, to improve handling of shape variations naturally found in the population, the fixed shape template is replaced by a more robust 3D statistical shape model based on Probabilistic Principal Component Analysis (PPCA). The advantages of using the Probabilistic PCA are that it allows reconstructing the optimal shape and computing the remaining variance of the statistical model from partial information. By using an iterative method, the statistical shape model is then refined using image based cues to get a better fitting of the statistical model to the patient's muscle anatomy. These image cues are based on the segmented muscle, edge information and intensity likelihood of the muscle. Here, a linear shape update mechanism is used to fit the statistical model to the image based cues. In the third approach, the shape refinement step is further improved by using a non-linear shape update mechanism where vertices of the 3D mesh of the statistical model incur the non-linear penalty depending on the remaining variability of the vertex. The non-linear shape update mechanism provides a more accurate shape update and helps in a finer shape fitting of the statistical model to the image based cues in areas where the shape variability is high. Finally, a unified approach is presented to segment the relevant facial muscles and the remaining facial soft-tissues (skin and fat). One soft-tissue layer is removed at a time such as the head and non-head regions followed by the skin. In the next step, bones are removed from the dataset, followed by the separation of the brain and non-brain regions as well as the removal of air cavities. Afterwards, facial fat is segmented using the standard Graph-Cuts approach. After separating the important anatomical structures, finally, a 3D fixed shape template mesh of the facial muscles is used to segment the relevant facial muscles. The proposed methods are tested on the challenging example of segmenting the masseter muscle. The datasets were noisy with almost all possessing mild to severe imaging artifacts such as high-density artifacts caused by e.g. dental fillings and dental implants. Qualitative and quantitative experimental results show that by incorporating prior shape knowledge leaking can be effectively constrained to obtain better segmentation results

Inisialisasi Otomatis Metode Level Set untuk Segmentasi Objek Overlapping pada Citra Panorama Gigi

Penelitian tentang segmentasi gigi individu telah banyak dilakukan dan memperoleh hasil yang baik. Namun, ketika dihadapkan kepada gigi overlap maka hal ini menjadi sebuah tantangan. Untuk memisahkan dua gigi overlap, maka perlu mengekstrak objek overlap terlebih dahulu. Metode level set banyak digunakan untuk melakukan segmentasi objek overlap, namun memiliki kelemahan yaitu perlu didefinisikan inisial awal metode level set secara manual oleh pengguna. Dalam penelitian ini diusulkan strategi inisialisasi otomatis pada metode level set untuk melakukan segmentasi gigi overlap menggunakan Hierarchical Cluster Analysis (HCA) pada citra panorama gigi. Tahapan strategi yang diusulkan terdiri dari preprocessing dimana di dalamnya ada proses perbaikan, rotasi dan cropping citra, dilanjutkan proses inisialisasi otomatis menggunakan algoritma HCA , dan yang terakhir segmentasi menggunakan metode level set. Hasil evaluasi menunjukkan bahwa strategi yang diusulkan berhasil melakukan inisialisasi secara otomatis dengan akurasi 73%. Hasil evaluasi segmentasi objek overlap cukup memuaskan dengan rasio misclassification error  0,93% dan relative foreground area error 24%. Dari hasil evaluasi menunjukkan bahwa strategi yang diusulkan dapat melakukan inisialisasi otomatis dengan baik. Inisialisasi yang tepat menghasilkan segmentasi yang baik pada metode level set.AbstractIndividual teeth segmentation has done a lot of the recent research and obtained good results. When faced with overlapping teeth, this is quite challenging. To separate overlapping teeth, it is necessary to extract the overlapping object first. The level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. This research proposes an automatic initialization strategy for the level set method to segment overlapping teeth using Hierarchical Cluster Analysis on dental panoramic radiograph images. The proposed strategy stage consists of preprocessing where there are several processes of enhancement, rotation, and cropping of the image, Then the automatic initialization process uses the HCA algorithm and the last is segmentation using the level set method. The evaluation results show that the proposed strategy is successful in carrying out automatic initialization with an accuracy of 73%. The results of the overlap object segmentation evaluation are satisfactory with a misclassification error ratio of 0.93% and a relative foreground area error of 24%. The evaluation results show that the proposed strategy can carry out automated initialization well. Proper initialization results can perform good segmentation of the level set method

Algorithms for enhanced artifact reduction and material recognition in computed tomography

Computed tomography (CT) imaging provides a non-destructive means to examine the interior of an object which is a valuable tool in medical and security applications. The variety of materials seen in the security applications is higher than in the medical applications. Factors such as clutter, presence of dense objects, and closely placed items in a bag or a parcel add to the difficulty of the material recognition in security applications. Metal and dense objects create image artifacts which degrade the image quality and deteriorate the recognition accuracy. Conventional CT machines scan the object using single source or dual source spectra and reconstruct the effective linear attenuation coefficient of voxels in the image which may not provide the sufficient information to identify the occupying materials. In this dissertation, we provide algorithmic solutions to enhance CT material recognition. We provide a set of algorithms to accommodate different classes of CT machines. First, we provide a metal artifact reduction algorithm for conventional CT machines which perform the measurements using single X-ray source spectrum. Compared to previous methods, our algorithm is robust to severe metal artifacts and accurately reconstructs the regions that are in proximity to metal. Second, we propose a novel joint segmentation and classification algorithm for dual-energy CT machines which extends prior work to capture spatial correlation in material X-ray attenuation properties. We show that the classification performance of our method surpasses the prior work's result. Third, we propose a new framework for reconstruction and classification using a new class of CT machines known as spectral CT which has been recently developed. Spectral CT uses multiple energy windows to scan the object, thus it captures data across higher energy dimensions per detector. Our reconstruction algorithm extracts essential features from the measured data by using spectral decomposition. We explore the effect of using different transforms in performing the measurement decomposition and we develop a new basis transform which encapsulates the sufficient information of the data and provides high classification accuracy. Furthermore, we extend our framework to perform the task of explosive detection. We show that our framework achieves high detection accuracy and it is robust to noise and variations. Lastly, we propose a combined algorithm for spectral CT, which jointly reconstructs images and labels each region in the image. We offer a tractable optimization method to solve the proposed discrete tomography problem. We show that our method outperforms the prior work in terms of both reconstruction quality and classification accuracy

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Intelligent laser scanning for computer aided manufacture.

Reverse engineering requires the acquisition of large amounts of data describing the surface of an object, sufficient to replicate that object accurately using appropriate fabrication techniques. This is important within a wide range of commercial and scientific fields where CAD models may be unavailable for parts that must be duplicated or modified, or where a physical model is used as a prototype. The three-dimensional digitisation of objects is an essential first step in reverse engineering. Optical triangulation laser sensors are one of the most popular and common non-contact methods used in the data acquisition process today. They provide the means for high resolution scanning of complex objects. Multiple scans of the object are usually required to capture the full 3D profile of the object. A number of factors, including scan resolution, system optics and the precision of the mechanical parts comprising the system may affect the accuracy of the process. A single perspective optical triangulation sensor provides an inexpensive method for the acquisition of 3D range image data