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Brain MRI Landmark Identification and Detection Brain MRI Landmark Identification and Detection By Ali Asaei In Partial Fulfilment of the Requirements for the Degree of MASTER OF SCIENCE In The Department of Electrical and Computer Engineering Thesis advisors: Dr. Babak Ardekani, The Nathan S. Kline Institute for Psychiatric Research Dr. Faramarz Vaziri, The Department of Electrical and Computer Engineering State University of New York New Paltz, New York 12561 December 2015 Brain MRI Landmark Identification and Detection By Ali Asaei State University of New York at New Paltz We, the thesis committee for the above candidate for the Master of Science Degree, hereby recommend acceptance of this thesis Babak Ardekani, PhD Faramarz Vaziri, PhD Department of Electrical and Computer Engineering State University of New York at New Paltz Approved by: Babak Ardekani, PhD (Advisor) Faramarz Vaziri, PhD (Advisor) Approved on: Babak Izadi, PhD (Chair) Abstract Knowledge of the location of anatomical landmarks on the brain is important in neu- roimaging. Applications include landmark-based image registration, segmentation of brain structures, electrode placement in deep brain stimulation, and prospective subject positioning in longitudinal imaging. Landmarks are specific structures with distinguish- able morphological characteristics. In this study, we only consider point landmarks on magnetic resonance imaging (MRI) brain scans. The most basic method for locating anatomical landmarks on MRI is manual placement by a trained operator. However, manual landmark detection is a strenuous and tedious task, especially if large databases are involved and/or multiple landmarks need to be located. Therefore, automatic land- mark detection on MRI has become an active area of research. Model-based methods are popular for detecting brain landmarks. Generally, model-based landmark detection includes a training set of MRI scans on which the location of certain landmarks are known, usually by manual placement. The location of landmarks on the training set is then used to derive and store models for individual landmarks. Then, when the same landmarks are to be located on a test MRI volume, the models are recalled and their information is used to automatically detect the landmarks. In this thesis, we propose a new unsupervised landmark identification method for the training phase of this process to replace manual landmark identification on the training set of MRI volumes. This method employs an iterative algorithm for detecting a set of landmarks on the training set that are leave-one-out consistent. In addition, we suggest a detection method to locate the corresponding points on a given test volume. In this study, the method was implemented and applied to a dataset of sixty 3D MRI volumes. The training was performed on 30 volumes. The remaining 30 volumes were used as a test set on which the detection algorithm located the corresponding landmarks. iii In the landmark identification approach, a set of candidate seeds are necessary as the initial guesses of landmark positions. The position and number of the seeds are optional. In this study, we used 154 candidate seeds spread uniformly across the entire brain volume. All the identified and detected landmarks were inspected manually us- ing a graphical user interface. To further evaluate the performance of the introduced method, we registered a set of 152 brain images to a reference space employing this method. Brain overlap of the registered volumes improved as a result of landmark based registration. As a further application, we used landmark detection for rigid-body registration of longitudinal MRI volumes. These are MRI volumes scanned from the same individual over time. We show that landmark detection is a fast method that can be used to obtain a good initial rigid-body registration which can then be followed by fine-tuning of the registration parameters. Keywords: landmark identification, landmark detection. iv Acknowledgements I would like to express my gratitude to my supervisors Dr. Babak Ardekani of the Nathan Kline Institute for Psychiatric Research (NKI) and Dr. Faramarz Vaziri of the State University of New York at New Paltz, for introducing me to the topic, engagement in the learning process, and their supports in the research work and academic studies of my thesis. I would like to thank Dr. Baback Izadi, the chair of the Electrical and Computer Engineering department for his support and for providing the financial assis- tance. I also thank all those who have helped me in any respect during the completion of this work, especially my parents who have encouraged and supported me through life. v Contents List of Figures viii List of Tablesx 1 Introduction1 1.1 Motivations.................................1 1.2 Challenges..................................2 1.3 Thesis overview...............................2 2 Background3 2.1 3D digital images..............................3 2.1.1 Spatial resolution..........................5 2.2 Image I/O..................................7 2.3 Image interpolation.............................8 2.3.1 Nearest neighbor interpolation...................9 2.3.2 Trilinear interpolation.......................9 2.4 Coordinate systems............................. 10 2.5 Image orientation.............................. 12 2.6 Spatial transformation........................... 14 2.6.1 Linear transformation matrices.................. 14 2.7 MRI Scans.................................. 17 vi Contents 2.8 Brain landmarks.............................. 18 2.9 Current brain landmark detection methods................ 20 2.9.1 Model-based landmark detection................. 22 2.9.1.1 Landmark template................... 22 2.9.1.2 Searching space...................... 23 2.9.1.3 Similarity measure.................... 24 3 Landmark identification and detection methods 26 3.1 Method overview.............................. 26 3.2 Image normalization............................ 28 3.3 Supervised landmark detection....................... 29 3.4 Unsupervised landmark identification................... 32 3.4.1 Initial seeds............................. 34 4 Registration method 35 5 Results 39 5.1 Image data................................. 39 5.2 Test setup.................................. 39 5.3 Evaluation.................................. 41 5.3.1 Overlap Index............................ 43 5.3.2 Application............................. 45 6 Conclusion 47 Bibliography 47 A Least-squares affine transformation estimationI B Registration Figures III vii List of Figures 2.1 A rectangular parallelepiped representing the 3D imaging FOV.....3 2.2 (a) Discrete sampling scheme. (b) A digital image with the size of nx = 5, ny = 7, nz = 3 voxels...........................4 2.3 Images of the same object with different resolutions [1]..........6 2.4 (a) Schematic of an MR image slice [2]. (b) Parallel imaging slices to cover the brain entire volume [3]......................6 2.5 Mapping between voxel indices [i, j, k] and memory locations v.....8 2.6 Schematic of a 3D sampling grid [4]....................9 2.7 Three medical imaging coordinate systems and corresponding axes [5].. 11 2.8 (a) Anatomical reference planes [6]. (b) Anatomical axes and cross sec- tions of the brain [7]............................ 12 2.9 A slice from a PIL image.......................... 13 2.10 Brain mid-sagittal scheme and some anatomical landmarks [8]...... 19 2.11 Spherical voxel templates.......................... 23 3.1 Framework of the proposed method..................... 26 3.2 Flow chart of the identification algorithm................. 27 3.3 Flow chart of the identification algorithm................. 28 viii List of Figures 3.4 The MSP of an MR image in PIL space. (a) AC (green) and PC (red) landmarks are detected using the algorithm in [9]. (b) Reoriented image according to the detected AC and PC................... 29 3.5 Flowchart for computing affine transformation TLM and TA....... 30 3.6 Fuzzy brain template............................. 34 4.1 Flowchart of the proposed registration method.............. 35 4.2 The 8 selected landmarks on the MSP................... 36 4.3 Flowchart for computing affine transformation TLM and TA....... 37 4.4 The sample plot of similarity function f versus dx, dy, dz, θx, θy, and θz. 38 5.1 An example of an identified landmark in 8 sample images. The red crosses (first row) and the blue crosses (second row) display the seeds and converged points in the coronal plane, respectively.......... 40 5.2 First row displays the average volume in PIL space. Second row displays the average volume in the original space.................. 43 5.3 (a) AC (green) and PC (red) on MSP. (b) 8 landmarks on MSP..... 45 5.4 MSP of 6 sample images and the 8 detected landmarks.......... 46 B.1 Same cross sections of original longitudinal images from one subject... III B.2 Same cross sections of registered longitudinal images from one subject.IV ix List of Tables 5.1 Number of the identified (total or symmetric) and detected landmarks on 30 training images and 30 test images for 3 template radii...... 41 x 1 Introduction 1.1 Motivations MRI scanners provide high resolution 3D digital image data for clinical assessment or research studies. Manual identification of anatomical landmarks on brain MR images is a common practice in neuroimaging. Although, manual labeling is still the gold standard in landmark detection methods, manual procedures are labor-intensive and subjective. Therefore, a computer-aided method to automatically detect landmarks would resolve the difficulties
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