Development of single cell shape measures and quantification of shape changes with cancer progression

2018 Summer.Includes bibliographical references.In spite of significant recent progress in cancer diagnostics and treatment, it is still the second leading cause of death in the United States. Some of the complexity of cancer arises from its heterogeneity. Cancer tumors in each patient are different than other patients. Even different tumors from one patient could differ from each other. Such a high diversity of tumors makes it challenging to correctly characterize cancer and come up with the best treatment plan for each patient. In order to do that, a complex combination of clinical and histopathological data need to be collected. This dissertation provides the evidence that the shape of the cells can be used in conjunction with other methods for a more reliable cancer characterization. In this study, experimental studies, numerical representation of the cell shape, big data analysis methods, and machine learning techniques are combined to provide a tool to better characterize cancer cells using their shape information. It provides evidence that cell shape encodes information about the cell phenotype, and demonstrates that the former can be used to predict the latter. This dissertation proposes detailed quantitative methods for quantifying the shape and structure of a cell and its nucleus. These features are classified into three main categories of textural, spreading and irregularity measures, which are then sub-categorized into nine different shape categories. Textural measures are used to quantify changes in actin organization for the cells perturbed with cytoskeletal drugs. Using the spreading and irregularity measures, it is shown that the changes in actin structure lead to significant changes in irregularity of the boundary of a cell and spreading of the cell and nuclei. Using these methods, the shape of retina, breast, and osteosarcoma cancer cells are quantified and it is shown that the majority of cells have similar changes in their shape once they become cancerous. Then, a neural network is trained on the shape of the cells which leads to an excellent prediction of class of cancer cells. This study shows that even though cancer cells have different characteristics, they can be categorized into clinically relevant subgroups using their shape information alone

The Role of Transient Vibration of the Skull on Concussion

Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to the cortex, with no layer of cerebrospinal fluid to reflect the wave or cushion its force. To date, there is few researches investigating the effect of transient vibration of the skull. Therefore, the overall goal of the proposed research is to gain better understanding of the role of transient vibration of the skull on concussion. This goal will be achieved by addressing three research objectives. First, a MRI skull and brain segmentation automatic technique is developed. Due to bones’ weak magnetic resonance signal, MRI scans struggle with differentiating bone tissue from other structures. One of the most important components for a successful segmentation is high-quality ground truth labels. Therefore, we introduce a deep learning framework for skull segmentation purpose where the ground truth labels are created from CT imaging using the standard tessellation language (STL). Furthermore, the brain region will be important for a future work, thus, we explore a new initialization concept of the convolutional neural network (CNN) by orthogonal moments to improve brain segmentation in MRI. Second, the creation of a novel 2D and 3D Automatic Method to Align the Facial Skeleton is introduced. An important aspect for further impact analysis is the ability to precisely simulate the same point of impact on multiple bone models. To perform this task, the skull must be precisely aligned in all anatomical planes. Therefore, we introduce a 2D/3D technique to align the facial skeleton that was initially developed for automatically calculating the craniofacial symmetry midline. In the 2D version, the entire concept of using cephalometric landmarks and manual image grid alignment to construct the training dataset was introduced. Then, this concept was extended to a 3D version where coronal and transverse planes are aligned using CNN approach. As the alignment in the sagittal plane is still undefined, a new alignment based on these techniques will be created to align the sagittal plane using Frankfort plane as a framework. Finally, the resonant frequencies of multiple skulls are assessed to determine how the skull resonant frequency vibrations propagate into the brain tissue. After applying material properties and mesh to the skull, modal analysis is performed to assess the skull natural frequencies. Finally, theories will be raised regarding the relation between the skull geometry, such as shape and thickness, and vibration with brain tissue injury, which may result in concussive injury

A neuro-genetic hybrid approach to automatic identification of plant leaves

Plants are essential for the existence of most living things on this planet. Plants are used for providing food, shelter, and medicine. The ability to identify plants is very important for several applications, including conservation of endangered plant species, rehabilitation of lands after mining activities and differentiating crop plants from weeds. In recent times, many researchers have made attempts to develop automated plant species recognition systems. However, the current computer-based plants recognition systems have limitations as some plants are naturally complex, thus it is difficult to extract and represent their features. Further, natural differences of features within the same plant and similarities between plants of different species cause problems in classification. This thesis developed a novel hybrid intelligent system based on a neuro-genetic model for automatic recognition of plants using leaf image analysis based on novel approach of combining several image descriptors with Cellular Neural Networks (CNN), Genetic Algorithm (GA), and Probabilistic Neural Networks (PNN) to address classification challenges in plant computer-based plant species identification using the images of plant leaves. A GA-based feature selection module was developed to select the best of these leaf features. Particle Swam Optimization (PSO) and Principal Component Analysis (PCA) were also used sideways for comparison and to provide rigorous feature selection and analysis. Statistical analysis using ANOVA and correlation techniques confirmed the effectiveness of the GA-based and PSO-based techniques as there were no redundant features, since the subset of features selected by both techniques correlated well. The number of principal components (PC) from the past were selected by conventional method associated with PCA. However, in this study, GA was used to select a minimum number of PC from the original PC space. This reduced computational cost with respect to time and increased the accuracy of the classifier used. The algebraic nature of the GA’s fitness function ensures good performance of the GA. Furthermore, GA was also used to optimize the parameters of a CNN (CNN for image segmentation) and then uniquely combined with PNN to improve and stabilize the performance of the classification system. The CNN (being an ordinary differential equation (ODE)) was solved using Runge-Kutta 4th order algorithm in order to minimize descritisation errors associated with edge detection. This study involved the extraction of 112 features from the images of plant species found in the Flavia dataset (publically available) using MATLAB programming environment. These features include Zernike Moments (20 ZMs), Fourier Descriptors (21 FDs), Legendre Moments (20 LMs), Hu 7 Moments (7 Hu7Ms), Texture Properties (22 TP) , Geometrical Properties (10 GP), and Colour features (12 CF). With the use of GA, only 14 features were finally selected for optimal accuracy. The PNN was genetically optimized to ensure optimal accuracy since it is not the best practise to fix the tunning parameters for the PNN arbitrarily. Two separate GA algorithms were implemented to optimize the PNN, that is, the GA provided by MATLAB Optimization Toolbox (GA1) and a separately implemented GA (GA2). The best chromosome (PNN spread) for GA1 was 0.035 with associated classification accuracy of 91.3740% while a spread value of 0.06 was obtained from GA2 giving rise to improved classification accuracy of 92.62%. The PNN-based classifier used in this study was benchmarked against other classifiers such as Multi-layer perceptron (MLP), K Nearest Neigbhour (kNN), Naive Bayes Classifier (NBC), Radial Basis Function (RBF), Ensemble classifiers (Adaboost). The best candidate among these classifiers was the genetically optimized PNN. Some computational theoretic properties on PNN are also presented

Of Cell Shapes and Motion: The Physical Basis of Animal Cell Migration.

Motile cells have developed a variety of migration modes relying on diverse traction-force-generation mechanisms. Before the behavior of intracellular components could be easily imaged, cell movements were mostly classified by different types of cellular shape dynamics. Indeed, even though some types of cells move without any significant change in shape, most cell propulsion mechanisms rely on global or local deformations of the cell surface. In this review, focusing mostly on metazoan cells, we discuss how different types of local and global shape changes underlie distinct migration modes. We then discuss mechanical differences between force-generation mechanisms and finish by speculating on how they may have evolved

Biological image analysis

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software. A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time

Spherical Harmonics Models and their Application to non-Spherical Shape Particles

The dissertation investigates spherical harmonics method for describing a particle shape. The main object of research is the non-spherical shape particles. The purpose of this dissertation is to create spherical harmonics model for a non-pherical particle. The dissertation also focuses on determining the suitability of the lowresolution spherical harmonics for describing various non-spherical particles. The work approaches a few tasks such as testing the suitability of a spherical harmonics model for simple symmetric particles and applying it to complex shape particles. The first task is formulated aiming to test the modelling concept and strategy using simple shapes. The second task is related to the practical applications, when complex shape particles are considered. The dissertation consists of introduction, 4 chapters, general conclusions, references, a list of publications by the author on the topic of the dissertation, a summary in Lithuanian and 5 annexes. The introduction reveals the investigated problem, importance of the thesis and the object of research, describes the purpose and tasks of the thesis, research methodology, scientific novelty, the practical significance of results and defended statements. The introduction ends in presenting the author’s publications on the topic of the dissertation, offering the material of made presentations in conferences and defining the structure of the dissertation. Chapter 1 revises the literature: the particulate systems and their processes, shapes of the particles and methods for describing the shape, shape indicators. At the end of the chapter, conclusions are drawn and the tasks for the dissertation are reconsidered. Chapter 2 presents the modelling approach and strategies for the points of the particle surface, spherical harmonics, the calculation of the expansion coefficients, integral parameters and curvature and also the conclusions. Chapters 3 and 4 analize the modelling results of the simple and complex particles. At the end of the both chapters conclusions are drawn. 5 articles focusing on the topic of the dissertation have been published: two articles – in the Thomson ISI register, one article – in conference material and scientific papers in Thomson ISI Proceedings data base, one article – in the journal quoted by other international data base, one article – in material reviewed during international conference. 8 presentations on the subject of the dissertation have been given in conferences at national and international levels

Robust visual speech recognition using optical flow analysis and rotation invariant features

The focus of this thesis is to develop computer vision algorithms for visual speech recognition system to identify the visemes. The majority of existing speech recognition systems is based on audio-visual signals and has been developed for speech enhancement and is prone to acoustic noise. Considering this problem, aim of this research is to investigate and develop a visual only speech recognition system which should be suitable for noisy environments. Potential applications of such a system include the lip-reading mobile phones, human computer interface (HCI) for mobility-impaired users, robotics, surveillance, improvement of speech based computer control in a noisy environment and for the rehabilitation of the persons who have undergone a laryngectomy surgery. In the literature, there are several models and algorithms available for visual feature extraction. These features are extracted from static mouth images and characterized as appearance and shape based features. However, these methods rarely incorporate the time dependent information of mouth dynamics. This dissertation presents two optical flow based approaches of visual feature extraction, which capture the mouth motions in an image sequence. The motivation for using motion features is, because the human perception of lip-reading is concerned with the temporal dynamics of mouth motion. The first approach is based on extraction of features from the optical flow vertical component. The optical flow vertical component is decomposed into multiple non-overlapping fixed scale blocks and statistical features of each block are computed for successive video frames of an utterance. To overcome the issue of large variation in speed of speech, each utterance is normalized using simple linear interpolation method. In the second approach, four directional motion templates based on optical flow are developed, each representing the consolidated motion information in an utterance in four directions (i.e.,up, down, left and right). This approach is an evolution of a view based approach known as motion history image (MHI). One of the main issues with the MHI method is its motion overwriting problem because of self-occlusion. DMHIs seem to solve this issue of overwriting. Two types of image descriptors, Zernike moments and Hu moments are used to represent each image of DMHIs. A support vector machine (SVM) classifier was used to classify the features obtained from the optical flow vertical component, Zernike and Hu moments separately. For identification of visemes, a multiclass SVM approach was employed. A video speech corpus of seven subjects was used for evaluating the efficiency of the proposed methods for lip-reading. The experimental results demonstrate the promising performance of the optical flow based mouth movement representations. Performance comparison between DMHI and MHI based on Zernike moments, shows that the DMHI technique outperforms the MHI technique. A video based adhoc temporal segmentation method is proposed in the thesis for isolated utterances. It has been used to detect the start and the end frame of an utterance from an image sequence. The technique is based on a pair-wise pixel comparison method. The efficiency of the proposed technique was tested on the available data set with short pauses between each utterance