Underwater Imaging Using Underwater Vehicle for Subsea Surveillance

This Final Year Project (FYP) focuses on the improvement of images captured through the built-in underwater camera in the HydroView MAX, which is a Remotely-operated Vehicle (ROV) used to perform inspections in subsea environment. Images captured underwater are always degraded due to issues such as light scattering and colour changes. Image-processing algorithms are applied to improve the degraded images so that the images obtained will be enhanced and closer to their true colours for further analysis. These qualities are required so that the degree of corrosion of the underwater pipelines can be estimated with considerable reliability. The estimation of the corrosion degree is made possible by judging on the percentage of corroded surface over the pipeline surface based on the binary image generated

Underwater Imaging Using Underwater Vehicle for Subsea Surveillance

This Final Year Project (FYP) focuses on the improvement of images captured through the built-in underwater camera in the HydroView MAX, which is a Remotely-operated Vehicle (ROV) used to perform inspections in subsea environment. Images captured underwater are always degraded due to issues such as light scattering and colour changes. Image-processing algorithms are applied to improve the degraded images so that the images obtained will be enhanced and closer to their true colours for further analysis. These qualities are required so that the degree of corrosion of the underwater pipelines can be estimated with considerable reliability. The estimation of the corrosion degree is made possible by judging on the percentage of corroded surface over the pipeline surface based on the binary image generated

Instance segmentation and material classification in X-ray computed tomography

Over the past thirty years, X-Ray Computed Tomography (CT) has been widely used in security checking due to its high resolution and fully 3-d construction. Designing object segmentation and classification algorithms based on reconstructed CT intensity data will help accurately locate and classify the potential hazardous articles in luggage. Proposal-based deep networks have been successful recently in segmentation and recognition tasks. However, they require large amount of labeled training images, which are hard to obtain in CT research. This thesis develops a non-proposal 3-d instance segmentation and classification structure based on smoothed fully convolutional networks (FCNs), graph-based spatial clustering and ensembling kernel SVMs using volumetric texture features, which can be trained on limited and highly unbalanced CT intensity data. Our structure will not only significantly accelerate the training convergence in FCN, but also efficiently detect and remove the outlier voxels in training data and guarantee the high and stable material classification performance. We demonstrate the performance of our approach on experimental volumetric images of containers obtained using a medical CT scanner

Segmentation and Classification of Edges Using Minimum Description Length Approximation and Complementary Junction Cues

This article presents a method for segmenting and classifying edges using minimum description length (MDL) approximation with automatically generated break points. A scheme is proposed where junction candidates are first detected in a multi-scale preprocessing step, which generates junction candidates with associated regions of interest. These junction features are matched to edges based on spatial coincidence. For each matched pair, a tentative break point is introduced at the edge point closest to the junction. Finally, these feature combinations serve as input for an MDL approximation method which tests the validity of the break point hypotheses and classifies the resulting edge segments as either "straight " or "curved". Experiments on real world image data demonstrate the viability of the approach

Implementation of an Automated Image Processing System for Observing the Activities of Honey Bees

This research designed and implemented an automated system to collect data on honey bees using computer science techniques. This system utilizes image processing techniques to extract data from the videos taken in front or at the top of the hive’s entrance. Several web-based applications are used to obtain temperature and humidity data from National weather Service to supplement the data that are collected at the hive locally. All the weather data and those extracted from the images are stored in a MySQL database for analysis and accessed by an iPhone App that is designed as part of this research

Slantlet transform-based segmentation and α -shape theory-based 3D visualization and volume calculation methods for MRI brain tumour

Magnetic Resonance Imaging (MRI) being the foremost significant component of medical diagnosis which requires careful, efficient, precise and reliable image analyses for brain tumour detection, segmentation, visualisation and volume calculation. The inherently varying nature of tumour shapes, locations and image intensities make brain tumour detection greatly intricate. Certainly, having a perfect result of brain tumour detection and segmentation is advantageous. Despite several available methods, tumour detection and segmentation are far from being resolved. Meanwhile, the progress of 3D visualisation and volume calculation of brain tumour is very limited due to absence of ground truth. Thus, this study proposes four new methods, namely abnormal MRI slice detection, brain tumour segmentation based on Slantlet Transform (SLT), 3D visualization and volume calculation of brain tumour based on Alpha (α) shape theory. In addition, two new datasets along with ground truth are created to validate the shape and volume of the brain tumour. The methodology involves three main phases. In the first phase, it begins with the cerebral tissue extraction, followed by abnormal block detection and its fine-tuning mechanism, and ends with abnormal slice detection based on the detected abnormal blocks. The second phase involves brain tumour segmentation that covers three processes. The abnormal slice is first decomposed using the SLT, then its significant coefficients are selected using Donoho universal threshold. The resultant image is composed using inverse SLT to obtain the tumour region. Finally, in the third phase, four original ideas are proposed to visualise and calculate the volume of the tumour. The first idea involves the determination of an optimal α value using a new formula. The second idea is to merge all tumour points for all abnormal slices using the α value to form a set of tetrahedrons. The third idea is to select the most relevant tetrahedrons using the α value as the threshold. The fourth idea is to calculate the volume of the tumour based on the selected tetrahedrons. In order to evaluate the performance of the proposed methods, a series of experiments are conducted using three standard datasets which comprise of 4567 MRI slices of 35 patients. The methods are evaluated using standard practices and benchmarked against the best and up-to-date techniques. Based on the experiments, the proposed methods have produced very encouraging results with an accuracy rate of 96% for the abnormality slice detection along with sensitivity and specificity of 99% for brain tumour segmentation. A perfect result for the 3D visualisation and volume calculation of brain tumour is also attained. The admirable features of the results suggest that the proposed methods may constitute a basis for reliable MRI brain tumour diagnosis and treatments

Використання методів глибинного навчання для визначення зони уваги на Т2-зважених зображеннях перфузійної МРТ

Магістерська дисертація за темою «Використання методів глибинного навчання для визначення зони уваги на Т2-зважених зображеннях перфузійної МРТ» виконана студентом кафедри біомедичної кібернетики ФБМІ Дюміним Олексієм Дмитровичем зі спеціальності 122 «Комп’ютерні науки» за освітньо-професійною програмою «Комп’ютерні технології в біології та медицині», та складається зі: вступу; 4 розділів («Літературний огляд», «Теоретична частина», «Аналітична частина», «Практична частина»), розділу з розрахунком стартап-проєкту, висновків до кожного з цих розділів; загальних висновків; списку використаних джерел, який налічує 72 найменування. Загальний обсяг роботи 121 сторінка. Актуальність теми. На сьогоднішній день важливу роль у діагностиці та лікуванні цереброваскулярних та онкологічних захворювань відіграє такий метод оцінювання зображень перфузійної МРТ, як динамічно-сприйнятлива контрастна магнітно-резонансна томографія (DSC). Ця методика записує зміни інтенсивності пікселів на динамічних серіях Т2-зважених зображень МРТ, отриманих до, під час та після введення контрастної речовини у судинну систему. Отримана в результаті DSC візуалізація перфузійних характеристик на картах перфузії використовується для виявлення областей з потенційним ураженням та постановки діагнозу. Проте через низький контраст між ураженням і навколишніми тканинами погіршується візуалізація ураження мозку на картах перфузії, що може призвести до помилково високих або хибно низьких результатів оцінки параметрів перфузії.[72] Щоб проблема була вирішена, програмне забезпечення для аналізу перфузійних DSC даних повинно попередньо обробляти дані часової послідовності шляхом сегментації тканин мозку та створювати бінарну маску для так званої зони уваги (ROI).[72] Для вирішення проблеми сегментації зображень була запропонована ідея використання модифікованої згорткової нейронної мережі на основі ResNet. Використання запропонованої нейронної мережі забезпечує найбільш точні результати сегментації та тим самим вирішує основну проблему автоматизованої сегментації. Мета і завдання дослідження. Метою роботи є підвищення точності сегментації Т2-зважених МРТ-зображень за рахунок використання модифікованої нейронної мережі на базі ResNet. Для досягнення поставленої мети необхідно виконати наступні завдання: 1. Реалізувати нейронну мережу на базі ResNet. 2. Знайти та реалізувати модифікації нейронної мережі на основі ResNet. 3. Провести попередній аналіз даних. 4. Дослідити отримані результати сегментації Т2-зважених МРТ-зображень. Об’єкт дослідження. Т2-зважених МРТ-зображень мозку. Предмет дослідження. Сегментація Т2-зважених МРТ-зображень мозку. Методи дослідження. Машинне навчання, згорткова нейронна мережа.Master's thesis on the topic "Deep learning methods for region of interest detection on perfusion T2-weighted MR images" is executed by the student of the department of biomedical cybernetics (Faculty of Biomedical Engineering) Diumin Oleksii Dmytrovych in the specialty 122 "Computer science" on the educational and professional program "Computer technologies in biology and medicine", and consists of: introduction; 4 sections ("Literary review", "Theoretical part", "Analytical part", "Practical part"), section with a startup calculation, conclusions to each of these sections; general conclusions; references, which includes 72 titles. The total volume of work is 121 pages. Relevance of the topic. Today, such a method of evaluating perfusion MRI images as dynamic susceptibility contrast magnetic resonance imaging (DSC) plays an important role in the diagnosis and treatment of cerebrovascular and oncological diseases. This technique records changes in pixel intensity on a dynamic series of T2-weighted MRI images obtained before, during, and after administration of a contrast agent into the vasculature. The resulting DSC visualization of perfusion characteristics on perfusion maps is used to identify areas of potential damage and make a diagnosis. However, due to the low contrast between the lesion and the surrounding tissues, the visualization of the brain lesion on the perfusion maps is impaired, which can lead to falsely high or falsely low results of the estimation of the perfusion parameters.[72] To solve the problem, perfusion DSC data analysis software must preprocess the time-series data by segmenting the brain tissue and create a binary mask for the so-called region of interest (ROI).[72] To solve the problem of image segmentation, the idea of using a modified convolutional neural network based on ResNet was proposed. Using the proposed neural network provides the most accurate segmentation results and thereby solves the main problem of automated segmentation. Objective and task sof the study. The aim of the work is to increase the accuracy of segmentation of T2-weighted MRI images by using a modified neural network based on ResNet. To achieve the goal, the following tasks must be completed: 1. Implement a neural network based on ResNet. 2. Find and implement neural network modifications based on ResNet. 3. Conduct preliminary data analysis. 4. To study the obtained results of segmentation of T2-weighted MRI images. Object of study. T2-weighted MRI images of the brain. Subject of study. Segmentation of T2-weighted MRI brain images. Research methods. Machine learning, convolutional neural network