Identification of dynamic textures using Dynamic Mode Decomposition

Abstract Dynamic Textures (DTs) are image sequences of moving scenes that present stationary properties in time. In this paper, we apply Dynamic Mode Decomposition (DMD) and Dynamic Mode Decomposition with Control (DMDc) to identify a parametric model of dynamic textures. The identification results are compared with a benchmark method from the dynamic texture literature, both from a mathematical and from a computational complexity point of view. Extensive simulations are carried out to assess the performance of the proposed algorithms with regards to synthesis and denoising purposes, with different types of dynamic textures. Results show that DMD and DMDc present lower error, lower residual noise and lower variance compared to the benchmark approach

Computational Modeling for Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, and Grading

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology (DP) images. Due to uncontrolled growth and infiltration into the surrounding tissues, the tumor regions appear with a significant texture variation in the static MRI volume and also in the longitudinal imaging study. Consequently, this study developed computational models using novel texture features to segment abnormal brain tissues (tumor, and stroke lesions), tracking the change of tumor volume in longitudinal images, and tumor grading in MR images. Manual delineation and analysis of these abnormal tissues in large scale is tedious, error-prone, and often suffers from inter-observer variability. Therefore, efficient computational models for robust segmentation of different abnormal tissues is required to support the diagnosis and analysis processes. In this study, brain tissues are characterized with novel computational modeling of multi-fractal texture features for multi-class brain tumor tissue segmentation (BTS) and extend the method for ischemic stroke lesions in MRI. The robustness of the proposed segmentation methods is evaluated using a huge amount of private and public domain clinical data that offers competitive performance when compared with that of the state-of-the-art methods. Further, I analyze the dynamic texture behavior of tumor volume in longitudinal imaging and develop post-processing frame-work using three-dimensional (3D) texture features. These post-processing methods are shown to reduce the false positives in the BTS results and improve the overall segmentation result in longitudinal imaging. Furthermore, using this improved segmentation results the change of tumor volume has been quantified in three types such as stable, progress, and shrinkage as observed by the volumetric changes of different tumor tissues in longitudinal images. This study also investigates a novel non-invasive glioma grading, for the first time in literature, that uses structural MRI only. Such non-invasive glioma grading may be useful before an invasive biopsy is recommended. This study further developed an automatic glioma grading scheme using the invasive cell nuclei morphology in DP images for cross-validation with the same patients. In summary, the texture-based computational models proposed in this study are expected to facilitate the clinical management of patients with the brain tumors and strokes by automating large scale imaging data analysis, reducing human error, inter-observer variability, and producing repeatable brain tumor quantitation and grading

Dynamic texture synthesis in image and video processing.

Xu, Leilei.Thesis submitted in: October 2007.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 78-84).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Texture and Dynamic Textures --- p.1Chapter 1.2 --- Related work --- p.4Chapter 1.3 --- Thesis Outline --- p.7Chapter 2 --- Image/Video Processing --- p.8Chapter 2.1 --- Bayesian Analysis --- p.8Chapter 2.2 --- Markov Property --- p.10Chapter 2.3 --- Graph Cut --- p.12Chapter 2.4 --- Belief Propagation --- p.13Chapter 2.5 --- Expectation-Maximization --- p.15Chapter 2.6 --- Principle Component Analysis --- p.15Chapter 3 --- Linear Dynamic System --- p.17Chapter 3.1 --- System Model --- p.18Chapter 3.2 --- Degeneracy and Canonical Model Realization --- p.19Chapter 3.3 --- Learning of Dynamic Textures --- p.19Chapter 3.4 --- Synthesizing Dynamic Textures --- p.21Chapter 3.5 --- Summary --- p.21Chapter 4 --- Dynamic Color Texture Synthesis --- p.25Chapter 4.1 --- Related Work --- p.25Chapter 4.2 --- System Model --- p.26Chapter 4.2.1 --- Laplacian Pyramid-based DCTS Model --- p.28Chapter 4.2.2 --- RBF-based DCTS Model --- p.28Chapter 4.3 --- Experimental Results --- p.32Chapter 4.4 --- Summary --- p.42Chapter 5 --- Dynamic Textures using Multi-resolution Analysis --- p.43Chapter 5.1 --- System Model --- p.44Chapter 5.2 --- Multi-resolution Descriptors --- p.46Chapter 5.2.1 --- Laplacian Pyramids --- p.47Chapter 5.2.2 --- Haar Wavelets --- p.48Chapter 5.2.3 --- Steerable Pyramid --- p.49Chapter 5.3 --- Experimental Results --- p.51Chapter 5.4 --- Summary --- p.55Chapter 6 --- Motion Transfer --- p.59Chapter 6.1 --- Problem formulation --- p.60Chapter 6.1.1 --- Similarity on Appearance --- p.61Chapter 6.1.2 --- Similarity on Dynamic Behavior --- p.62Chapter 6.1.3 --- The Objective Function --- p.65Chapter 6.2 --- Further Work --- p.66Chapter 7 --- Conclusions --- p.67Chapter A --- List of Publications --- p.68Chapter B --- Degeneracy in LDS Model --- p.70Chapter B.l --- Equivalence Class --- p.70Chapter B.2 --- The Choice of the Matrix Q --- p.70Chapter B.3 --- Swapping the Column of C and A --- p.71Chapter C --- Probability Density Functions --- p.74Chapter C.1 --- Probability Distribution --- p.74Chapter C.2 --- Joint Probability Distributions --- p.75Bibliography --- p.7