Eternity Kills

Panel: The Calling: Writing with Responsibilit

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Recovering the state sequence of hidden Markov models using mean-field approximations

Inferring the sequence of states from observations is one of the most fundamental problems in Hidden Markov Models. In statistical physics language, this problem is equivalent to computing the marginals of a one-dimensional model with a random external field. While this task can be accomplished through transfer matrix methods, it becomes quickly intractable when the underlying state space is large. This paper develops several low-complexity approximate algorithms to address this inference problem when the state space becomes large. The new algorithms are based on various mean-field approximations of the transfer matrix. Their performances are studied in detail on a simple realistic model for DNA pyrosequencing.Comment: 43 pages, 41 figure

Development of polymer and lipid materials for enhanced delivery of nucleic acids and proteins

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references.The development of synthetic vectors enabling efficient intracellular delivery of macromolecular therapeutics such as nucleic acids and proteins could potentially catalyze the clinical translation of many gene and protein-based therapies. However, progress has been hindered by a lack of safe and effective materials and by insufficient insight into the relationship between key delivery properties and efficacy. Accordingly, working with a promising class of cationic, degradable gene delivery vectors, poly(-amino ester)s (PBAEs), we develop novel, hydrophobic PBAE terpolymers that display dramatically increased gene delivery potency and nanoparticle stability. We then develop a technique based on size-exclusion chromatography that enables the isolation of well-defined, monodisperse PBAE polymer fractions with greater transfection activities than the starting polymer. This technique also allows us to elucidate the dependence of gene delivery properties on polymer molecular weight (MW). Subsequently, we examine the cellular uptake and trafficking mechanisms of PBAE/DNA polyplexes, and demonstrate that polyplex internalization and transfection depend on a key endo/lysosomal cholesterol transport protein, Niemann-Pick C1 (Npcl). Finally, working with cationic lipids termed lipidoids, which have shown exceptional potency for the delivery of RNAi therapeutics, we develop these materials for intracellular delivery of proteins using a simple and novel approach in which nucleic acids serve as a handle for protein encapsulation and delivery. Preliminary in vivo experiments suggest the potential application of this approach toward lipidoid-mediated delivery of protein-based vaccines. Taken together, the work presented here advances the development of polymer and lipid materials for the safe and effective intracellular delivery of DNA and protein therapeutics.by Ahmed Atef Eltoukhy.Ph.D

Observer-based Controller for VTOL-UAVs Tracking using Direct Vision-Aided Inertial Navigation Measurements

This paper proposes a novel observer-based controller for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive measurements from a Vision-Aided Inertial Navigation System (VA-INS) and produce the required thrust and rotational torque inputs. The VA-INS is composed of a vision unit (monocular or stereo camera) and a typical low-cost 6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a gyroscope. A major benefit of this approach is its applicability for environments where the Global Positioning System (GPS) is inaccessible. The proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately estimate attitude (orientation), gyroscope bias, position, and linear velocity. Ability to use VA-INS measurements directly makes the proposed observer design more computationally efficient as it obviates the need for attitude and position reconstruction. Once the motion components are estimated, the observer-based controller is used to control the VTOL-UAV attitude, angular velocity, position, and linear velocity guiding the vehicle along the desired trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and the control errors of the observer-based controller are proven to be exponentially stable starting from almost any initial condition. To achieve global and unique VTOL-UAV representation in 6 DoF, the proposed approach is posed on the Lie Group and the design in unit-quaternion is presented. Although the proposed approach is described in a continuous form, the discrete version is provided and tested. Keywords: Vision-aided inertial navigation system, unmanned aerial vehicle, vertical take-off and landing, stochastic, noise, Robotics, control systems, air mobility, observer-based controller algorithm, landmark measurement, exponential stability