The Computational Techniques Developed to Analyze DNA Gel Images

The analysis of gel electrophoresis images is very crucial for molecular biologists to comprehend and interpret their experimental results. Thus, enhancing current mathematical methods and developing new accurate ones is very important and challenging task for bioinformaticians. For example, enhancing the commonly used mathematical method in gel analysis known as "Fitting method estimation" and proposing a new efficient method entitled "Ruler estimation" for preprocessing a given image and detecting lanes and bands automatically. Both mathematical methods implemented in our newly developed software. Three mathematical models namely, linear, quadratic and cubic fitting are tested for the accuracy of detecting the bands and lanes in the gel image to determine the best fitting model. A friendly user interface is developed for this new program using MATLB GUI to extract useful bimolecular information accurately and automatically. The new software has the ability to manually add or delete any band(s) and estimate the size of any unknown band(s) on the gel. Moreover, the similarity and (dis)similarity between lanes "samples" are estimated based on comparing the numbers and sizes of bands to generate a phylogram tree

Image Analysis of Pellet Size for a Control System in Industrial Feed Production

When producing aquaculture fish feed pellets, the size of the output product is of immense importance. As the production method cannot produce pellets of constant and uniform size using constant machine settings, there is a demand for size control. Fish fed with feed pellets of improper size are prone to not grow as expected, which is undesirable to the aquaculture industry. In this paper an image analysis method is proposed for automatic size-monitoring of pellets. This is called granulometry and the method used here is based on the mathematical morphological opening operation. In the proposed method, no image object segmentation is needed. The results show that it is possible to extract a general size distribution from an image of piled disordered pellets representing both length and diameter of the pellets in combination as an area

Characterization of forest understory using multi-temporal full-waveform airborne laser scanning

Der Unterwuchs als Teil der Waldstruktur hat eine wichtige Funktion im Hinblick auf die Dynamik der Waldentwicklung. Allerdings ist die Charakterisierung des Unterwuchses mittels Fernerkundungsmethoden problematisch, da die Vegetationsdichte eine Erfassung der vertikalen Struktur stark limitiert. Unter Verwendung von flugzeuggestütztem, multi-temporalen Laserscanning ist es möglich, den Unterwuchs in einem dichten Laubwald zu detektieren und zu charakterisieren. Basierend auf den geometrischen Informationen der Laser-Punktwolke und den zugehörigen full-waveform Charakteristiken wurden folgende Unterwuchsklassen abgeleitet: vegetationsfreie Flächen, Streu, Unterwuchs 3 m. Für die Validierung wurde sowohl terrestrisches Laserscanning als auch eine umfangreiche Feldmessung entsprechend dem VALERI Ansatzes verwendet. Die Detektion des Unterwuchses erfolgte mit einer Genauigkeit von 78%; die Klassifikation erreichte eine Genauigkeit von 64%

3D Axon Structure Extraction and Analysis in Confocal Fluorescence Microscopy Images

The morphological properties of axons, such as their branching patterns and oriented structures, are of great interest for biologists in the study of the synaptic connectivity of neurons. In these studies, researchers use triple immunofluorescent confocal microscopy to record morphological changes of neuronal processes. Three-dimensional (3D) microscopy image analysis is then required to extract morphological features of the neuronal structures. In this article, we propose a highly automated 3D centerline extraction tool to assist in this task. For this project, the most difficult part is that some axons are overlapping such that the boundaries distinguishing them are barely visible. Our approach combines a 3D dynamic programming (DP) technique and marker-controlled watershed algorithm to solve this problem. The approach consists of tracking and updating along the navigation directions of multiple axons simultaneously. The experimental results show that the proposed method can rapidly and accurately extract multiple axon centerlines and can handle complicated axon structures such as cross-over sections and overlapping objects.Molecular and Cellular Biolog

Development of thermal imaging preclassifier for diabetic foot

La diabetes mellitus tipo II es una enfermedad crónica con base genética, pero factores como la dieta poco saludable, la inactividad física y la obesidad también influyen.  Entre las complicaciones de esta, se destaca el pie diabético debido a la neuropatía causada por la hiperglucemia, esto puede llevar a úlceras y lesiones difíciles de tratar, hasta incluso requerir cirugía, el diagnóstico actual implica pruebas costosas y tiempo de espera para resultados. Este proyecto busca desarrollar un preclasificador de imágenes térmicas en MATLAB® mediante deep learning para distinguir entre pies sanos y diabéticos, además, se recopilan datos clínicos del paciente a través de una interfaz en APP DESIGNER® y se almacenan en una base de datos, estos datos se utilizan para categorizar a los pacientes como: sin riesgo, propensos o con síntomas de pie diabético, proporcionando recomendaciones de cuidado según la categorización. Este enfoque tiene como objetivo facilitar la detección temprana y eficaz del pie diabético, reduciendo el tiempo de diagnóstico y el riesgo de complicaciones graves para los pacientes.Diabetes mellitus type II is a chronic disease with a genetic basis, but factors such as unhealthy diet, physical inactivity and obesity also play a role.  Among the complications of this, diabetic foot stands out due to neuropathy caused by hyperglycemia, this can lead to ulcers and lesions difficult to treat, even requiring surgery, the current diagnosis involves expensive tests and waiting time for results. This project seeks to develop a thermal imaging pre-sorter in MATLAB® using deep learning to distinguish between healthy and diabetic feet, furthermore, clinical patient data is collected through an interface in APP DESIGNER® and stored in a database, this data is used to categorize patients as not at risk, prone or with diabetic foot symptoms, providing care recommendations according to the categorization. This approach aims to facilitate early and effective detection of diabetic foot, reducing the time to diagnosis and the risk of serious complications for patients

Automation Of Particle Detection And Tracking Of Motor Proteins

University of Minnesota M.S.E.E. thesis. September 2015. Major: Electrical Engineering. Advisor: Murti Salapaka. 1 computer file (PDF); vii, 46 pages.Cells are the basic structural, functional and biological functional unit in living organisms. Cells are usually called “fundamental building blocks of life” due to their ability to replicate independently. The study of cells and cell processes is imperative in the study of living organisms. Particle detection and tracking plays a vital role in understanding the processes that occur in living cells. Biological processes involve complex and dynamic machinery which makes it very difficult to analyze and draw conclusions from the observations. Technological advancements have significantly improved the quality and quantity of data that can be collected: particles with nanometer resolution can now be imaged with intricate details over a significant interval of time, thus providing us access to information about the biological processes at a cellular and molecular level. The extensive use of fluorescent probes sheds light on the different particles and their roles in the various processes. However, there are a lot of factors which affect the processes that are not under our control thereby inhibiting us from successfully detecting and tacking particles. The presence of a plethora of particles which vary in size, nature, density of occurrence, fluorescence, nature of motion etc. makes it impossible to have a unified detection and tracking algorithm that can provide us with the most accurate results. The presence of a wide number of independent parameters some of which are mentioned above makes it hard to simulate a process and hinders the understanding of processes and drawing conclusions from them. This study mainly focuses on summarizing some of the most popular detection and tracking algorithms. Towards the end, the developed detection and tracking algorithm is applied to study bidirectional axonal cargo transport. A video containing the result of the tracking algorithm has been submitted as Supplementary Video 1

Automatización de la adquisición y procesamiento de datos en microscopía electrónica tridimensional

257 P.La presente tesis ha sido realizada en la unidad de biología estructural del Centro de Investigación Cooperativa en biociencias (CIC bioGUNE) en colaboración con el Grupo de Control Automático (GCA) del departamento de Ingeniería de Sistemas y Automática de la Universidad de País Vasco (UPV/EHU). Esta tesis se centra en el desarrollo de un nuevo esquema de control inteligente utilizando diferentes algoritmos de inteligencia artificial para automatizar la adquisición de datos en un TEM dedicado a experimentos de cryo-EM. Este trabajo ha sido realizado utilizando el equipamiento de la plataforma de microscopía electrónica del CIC bioGUNE, especializada en el análisis de muestras a temperaturas criogénicas con el microscopio electrónico de transmisión modelo JEM-2200FS/CR de la compañía Jeol. El esquema de control inteligente permite el control remoto del microscopio y la monitorización en tiempo real del proceso de adquisición y análisis de las imágenes en una sesión cryo-TEM en modo automático. Uno de los objetivos de este trabajo es que el sistema de control inteligente lleve a cabo la misma tarea y de forma similar a cómo lo realizaría un microscopista experto en sesiones cryo-TEM, evaluando la calidad de las imágenes en tiempo real