Ανάλυση πολυκαναλικών εικόνων χρωμοσωμάτων

The study of chromosomes is one of the major areas of study for modern genetics because the chromosomes are the carriers of all the genetic material (DNA) of an organism that are transferred from generation to generation by means of reproduction. The assignment of each chromosome to each class from a chromosome image takes time and demands great experience to avoid mistakes that can lead to misdiagnosis. For this reason there have been developed algorithms for image processing and automated analysis of chromosomes. There are several methods and techniques for the cultivation of chromosomes each of which leads to a different type of image. For example, if the chromosomes are cultured according to the protocol G-Banding the resulting image is a gray level image. In this thesis we deal with M-FISH protocol which leads to a multichannel image (6 channels). In this technique the biological experiment has been constructed so that each of the 24 chromosome types (1-22, X, Y) would be reflected in a different color. The purpose of this thesis is the identification and classification of human chromosomes from multichannel M-FISH images. Initially, we developed a method based on the Watershed transform for the region segmentation (grouping pixels with similar characteristics) of chromosomes. The Watershed transform requires a measure of separability between similar areas and for this reason we chose to calculate the multichannel gradient. In this way we achieve a clear separation between areas with different color corresponding to a different chromosome class. The segmentation results are quite satisfactory compared to other methods reported in the literature on the same M-FISH basis images. After segmentation we perform region classification using a statistical classifier that employs the Bayes rule. This classifier is simple to develop and implement and provides satisfactory classification performance. Compared with existing approaches that use Pixel by Pixel classification the proposed region-based method showed better results. We also study the effectiveness image smoothing using Vector Median Filtering and its variants and provide comparative experimental results. One of the problems in the chromosome classification methodologies using multichannel M-FISH images is the fact that they demand a labeled training set to build the classifier. For example a Bayes classifier requires estimating the parameters such as mean and covariance for each of the 24 chromosomes classes. The existence of a methodology that does not require a labeled training set is therefore essential. Such an unsupervised methodology is presented in this thesis. First, we segment the M-FISH image using the Watershed transform to remove the background. Then we estimate which of the remaining pixels have been hybridized or not using the EM algorithm in each of the 5 channels of the image. Then we use a Gaussian Mixture Model to classify each pixel into one of the 24 classes of chromosomes. To build this model for the first time we exploit prior information about which chromosome class emits to each of the five channels. The adaptation of the parameters of Gaussian Mixture Model by using the Maximum a Posterior Expectation Maximization method (MAP EM) results in an increase in the rate of correct classification. It is noteworthy that the proposed unsupervised methodology achieves higher classification rates when compared to supervised classification methodologies. One of the problems for automatic chromosome segmentation methods is the problem of the occlusion of chromosomes. In particular, two important factors influencing the segmentation are the following Chromosomes that overlap with one another, Chromosomes which adjoin one another. We have developed a method that addresses both these problems successfully. Initially we apply a recursive Watershed transform to get an initial assessment of areas of chromosomes. Then for each area of the Watershed transform we determine high curvature points around the perimeter of the chromosomal region. From these points will begin a gradient path which crosses the region and separates the chromosome region where two chromosomes are tangent to each other. If two or more chromosomes overlap each other, then the path splits the chromosomes into pieces. Then we form the Region Adjacency Graph and categorize each area using a region Bayes classifier. If a pair of neighboring regions shares the same class then they are joined together. The method was tested on chromosome images and the success rate of the method was satisfactory. In addition we compared the method with other segmentation methodologies such as the pale paths and the results were much better, especially for the case of overlapping chromosomes.Η µελέτη των χρωµοσωµάτων αποτελεί έναν από τους σηµαντικότερους τοµείς µελέτης για τη σύγχρονη Γενετική διότι τα χρωµοσώµατα αποτελούν τους φορείς όλου του γενετικού υλικού (DNA) ενός οργανισµού που µεταβιβάζονται από γενιά σε γενιά µε την βοήθεια της αναπαραγωγής. Τα χρωµοσώµατα ανήκουν σε κατηγορίες και η ανάθεση κάθε χρωµοσώµατος στην κατηγορία του από µια εικόνα χρωµοσωµάτων απαιτεί χρόνο αλλά και µεγάλη εµπειρία για την αποφυγή λαθών που µπορούν να οδηγήσουν σε εσφαλµένη διάγνωση. Για το λόγο αυτό αναπτύχθηκαν τεχνικές για την επεξεργασία και ανάλυση εικόνων χρωµοσωµάτων και τον αυτόµατο χαρακτηρισµό τους. Υπάρχουν αρκετοί τρόποι και τεχνικές για την καλλιέργεια των χρωµοσωµάτων η κάθε µια από τις οποίες οδηγεί και σε διαφορετικό τύπο εικόνας. Για παράδειγµα αν τα χρωµοσώµατα καλλιεργηθούν σύµφωνα µε το πρωτόκολλο G-Banding η εικόνα που προκύπτει είναι µια γκρι (grey-scale) εικόνα. Στην παρούσα διατριβή ασχολούµαστε µε το πρωτόκολλο M-FISH το οποίο οδηγεί σε µια πολυκαναλική εικόνα (6 καναλιών). Στην τεχνική αυτή το βιολογικό πείραµα έχει κατασκευαστεί έτσι ώστε η κάθε µια από τις 24 κατηγορίες χρωµοσωµάτων (1-22,Χ,Υ) να αποτυπώνεται µε διαφορετικό χρώµα. Αντικείµενο της παρούσας διατριβής είναι η αναγνώριση και κατηγοριοποίηση των ανθρωπίνων χρωµοσωµάτων από πολυκαναλικές εικόνες M-FISH. Αρχικά αναπτύξαµε µια µέθοδο βασισµένη στον µετασχηµατισµό Watershed για την κατάτµηση (οµαδοποίηση εικονοστοιχείων µε παρόµοια χαρακτηριστικά) των χρωµοσωµάτων σε περιοχές. Ο µετασχηµατισµός Watershed απαιτεί ένα µέτρο διαχωρισηµότητας µεταξύ όµοιων περιοχών και γι’ αυτό το λόγο επιλέξαµε τον υπολογισµό της πολυκαναλικής παραγώγου. Με αυτό τον τρόπο εξασφαλίζεται ο σαφής διαχωρισµός µεταξύ περιοχών µε διαφορετικό χρώµα και επιπλέον αφαιρούµε το υπόβαθρο (background). Τα αποτελέσµατα της κατάτµησης είναι αρκετά ικανοποιητικά συγκρινόµενα µε αυτά της βιβλιογραφίας για την ίδια βάση εικόνων M-FISH. Για την ταξινόµηση κάθε περιοχής χρησιµοποιήσαµε έναν στατιστικό ταξινοµητή βασισµένο στον κανόνα του Bayes. Ο ταξινοµητής αυτός είναι απλός στην υλοποίηση του και έχει χρησιµοποιηθεί και σε άλλες µελέτες. Σε σύγκριση µε ήδη υπάρχουσες µεθοδολογίες οι οποίες χρησιµοποιούν Pixel by Pixel κατηγοριοποίηση η µέθοδος µας (που βασίζεται σε ταξινόµηση περιοχών) εµφάνισε καλύτερα αποτελέσµατα. Τέλος, µελετούµε την αποτελεσµατικότητα των φίλτρων διανυσµατικού διαµέσου (Vector Median Filtering) και παραλλαγών του εάν εφαρµοστούν στην εικόνα πριν την ταξινόµηση. Η αποτελεσµατικότητα των φίλτρων ∆ιαµέσου εξετάζεται συγκρίνοντας το ποσοστό σωστής ταξινόµησης πριν και µετά την χρήση των φίλτρων αυτών. Ένα από τα προβλήµατα που εµφανίζουν όλες οι µεθοδολογίες κατηγοριοποίησης χρωµοσωµάτων από πολυκαναλικές εικόνες M-FISH είναι η προϋπόθεση ύπαρξης ενός συνόλου εκπαίδευσης για την εκπαίδευση του ταξινοµητή. Στην περίπτωση για παράδειγµα ενός ταξινοµητή Bayes απαιτείται η εκτίµηση των παραµέτρων όπως της µέσης τιµής και του πίνακα συµµεταβλητότητας για κάθε µια από τις 24 κατηγορίες χρωµοσωµάτων. Η ύπαρξη µιας µεθοδολογίας που θα είναι ανεξάρτητη από το σύνολο εκπαίδευσης που επιλέγουµε έχει σηµαντική αξία. Μια τέτοια µεθοδολογία παρουσιάζεται στην διατριβή αυτή. Αρχικά λαµβάνουµε µια κατάτµηση της εικόνας M-FISH µε την χρήση της µεθοδολογίας Watershed (αποµακρύνοντας το υπόβαθρο) και κατόπιν εκτιµούµε ποια από τα εικονοστοιχεία έχουν υβριδοποιηθεί ή όχι µε την χρήση του αλγορίθµου EM σε κάθε ένα από τα 5 κανάλια της εικόνας µας. Στην συνέχεια χρησιµοποιούµε ένα πολυκαναλικό Gaussian Mixture Model για την κατηγοριοποίηση κάθε εικονοστοιχείου σε µια από τις 24 κατηγορίες χρωµοσωµάτων. Στο µοντέλο αυτό χρησιµοποιείται για πρώτη φορά εκ των προτέρων πληροφορία σχετικά µε το σε ποιο κανάλι εκπέµπει κάθε κατηγορία χρωµοσώµατος. Η περαιτέρω εκπαίδευση των παραµέτρων του Gaussian Mixture Model από τον αλγόριθµο Maximum A Posterior Expectation Maximization (MAP EM) επιτρέπει την αύξηση του ποσοστού σωστής κατηγοριοποίησης. Η µεθοδολογία αυτή επιτυγχάνει ακόµη καλύτερα ποσοστά συγκρινόµενη ακόµη µε µεθοδολογίες ταξινόµησης µε επίβλεψη. ∆ύο σηµαντικοί παράγοντες επηρεάζουν την κατάτµηση των εικόνων χρωµοσωµάτων και είναι οι εξής : Χρωµοσώµατα που επικαλύπτουν το ένα το άλλο, Χρωµοσώµατα που εφάπτονται το ένα στο άλλο. Προτείνουµε µια µέθοδο που αντιµετωπίζει και τα δύο αυτά προβλήµατα µε επιτυχία. Αρχικά εφαρµόζουµε έναν επαναληπτικό µετασχηµατισµό Watershed ώστε να πάρουµε µια αρχική εκτίµηση των περιοχών των χρωµοσωµάτων. Στην συνέχεια εντοπίζουµε σηµεία υψηλής κύρτωσης πάνω στην περίµετρο της χρωµοσωµατικής περιοχής. Ξεκινώντας από τα σηµεία αυτά, θα δηµιουργούµε ένα µονοπάτι παραγώγου (Gradient Path) το οποίο διασχίζει την περιοχή χρωµοσώµατος και διαχωρίζει την περιοχή όταν δύο χρωµοσώµατα εφάπτονται το ένα στο άλλο. Αν δυο ή και περισσότερα χρωµοσώµατα επικαλύπτονται τότε το µονοπάτι διαχωρίζει σε δύο τµήµατα την περιοχή των χρωµοσωµάτων. Στην συνέχεια σχηµατίζουµε τον γράφο γειτνίασης περιοχών (Region Adjacency Graph) και κατηγοριοποιούµε κάθε περιοχή κάνοντας χρήση ενός ταξινοµητή περιοχών Bayes. Για κάθε ζεύγος γειτονικών περιοχών που έχουν την ίδια κατηγορία ενώνουµε τις δύο αυτές περιοχές. Συγκριτικά αποτελέσµατα µε άλλες µεθόδους δείχνουν την ανωτερότητα της µεθόδου ιδιαίτερα στην περίπτωση των επικαλυπτόµενων χρωµοσωµάτων

On Image based Enhancement for 3D Dense Reconstruction of Low Light Aerial Visual Inspected Environments

Micro Aerial Vehicles (MAV)s have been distinguished, in the last decade, for their potential to inspect infrastructures in an active manner and provide critical information to the asset owners. Inspired by this trend, the mining industry is lately focusing to incorporate MAVs in their production cycles. Towards this direction, this article proposes a novel method to enhance 3D reconstruction of low-light environments, like underground tunnels, by using image processing. More specifically, the main idea is to enhance the low light resolution of the collected images, captured onboard an aerial platform, before inserting them to the reconstruction pipeline. The proposed method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm that limits the noise, while amplifies the contrast of the image. The overall efficiency and improvement achieved of the novel architecture has been extensively and successfully evaluated by utilizing data sets captured from real scale underground tunnels using a quadrotor.ISBN för värdpublikation: 978-3-030-17797-3, 978-3-030-17798-0</p

PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction

Maritime journeys significantly depend on weather conditions, and so meteorology has always had a key role in maritime businesses. Nowadays, the new era of innovative machine learning approaches along with the availability of a wide range of sensors and microcontrollers creates increasing perspectives for providing on-board reliable short-range forecasting of main meteorological variables. The main goal of this study is to propose a lightweight on-board solution for real-time weather prediction. The system is composed of a commercial weather station integrated with an industrial IOT-edge data processing module that computes the wind direction and speed forecasts without the need of an Internet connection. A regression machine learning algorithm was chosen so as to require the smallest amount of resources (memory, CPU) and be able to run in a microcontroller. The algorithm has been designed and coded following specific conditions and specifications. The system has been tested on real weather data gathered from static weather stations and onboard during a test trip. The efficiency of the system has been proven through various error metrics

Remaining Useful Battery Life Prediction for UAVs based on Machine Learning

Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time window, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be cast as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple experimental data from different flight conditions.Konferensartikel i tidskriftCollaborative Aerial Robotic Workers, AEROWORKSIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

A new clinical test for sensorimotor function of the hand – development and preliminary validation

Abstract Background Sensorimotor disturbances of the hand such as altered neuromuscular control and reduced proprioception have been reported for various musculoskeletal disorders. This can have major impact on daily activities such as dressing, cooking and manual work, especially when involving high demands on precision and therefore needs to be considered in the assessment and rehabilitation of hand disorders. There is however a lack of feasible and accurate objective methods for the assessment of movement behavior, including proprioception tests, of the hand in the clinic today. The objective of this observational cross- sectional study was to develop and conduct preliminary validation testing of a new method for clinical assessment of movement sense of the wrist using a laser pointer and an automatic scoring system of test results. Methods Fifty physiotherapists performed a tracking task with a hand-held laser pointer by following a zig-zag pattern as accurately as possible. The task was performed with left and right hand in both left and right directions, with three trials for each hand movement. Each trial was video recorded and analysed with a specifically tailored image processing pipeline for automatic quantification of the test. The main outcome variable was Acuity, calculated as the percent of the time the laser dot was on the target line during the trial. Results The results showed a significantly better Acuity for the dominant compared to non-dominant hand. Participants with right hand pain within the last 12 months had a significantly reduced acuity (p < 0.05), and although not significant there was also a similar trend for reduced Acuity also for participants with left hand pain. Furthermore, there was a clear negative correlation between Acuity and Speed indicating a speed-accuracy trade off commonly found in manual tasks. The repeatability of the test showed acceptable intra class correlation (ICC2.1) values (0.68-0.81) and standard error of measurement values ranging between 5.0–6.3 for Acuity. Conclusions The initial results suggest that the test may be a valid and feasible test for assessment of the movement sense of the hand. Future research should include assessments on different patient groups and reliability evaluations over time and between testers

Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks

This article proposes a novel visual framework for detecting tunnel crossings/junctions in underground mine areas towards the autonomous navigation of Micro Aeril Vehicles (MAVs). Usually mine environments have complex geometries, including multiple crossings with different tunnels that challenge the autonomous planning of aerial robots. Towards the envisioned scenario of autonomous or semi-autonomous deployment of MAVs with limited Line-of-Sight in subterranean environments, the proposed module acknowledges the existence of junctions by providing crucial information to the autonomy and planning layers of the aerial vehicle. The capability for a junction detection is necessary in the majority of mission scenarios, including unknown area exploration, known area inspection and robot homing missions. The proposed novel method has the ability to feed the image stream from the vehicles’ on-board forward facing camera in a Convolutional Neural Network (CNN) classification architecture, expressed in four categories: 1) left junction, 2) right junction, 3) left &amp; right junction, and 4) no junction in the local vicinity of the vehicle. The core contribution stems for the incorporation of AlexNet in a transfer learning scheme for detecting multiple branches in a subterranean environment. The validity of the proposed method has been validated through multiple data-sets collected from real underground environments, demonstrating the performance and merits of the proposed module.ISBN för värdpublikation: 978-1-7281-4878-6, 978-1-7281-4879-3</p

MAV Navigation in Unknown Dark Underground Mines Using Deep Learning

This article proposes a Deep Learning (DL) method to enable fully autonomous flights for low-cost Micro Aerial Vehicles (MAVs) in unknown dark underground mine tunnels. This kind of environments pose multiple challenges including lack of illumination, narrow passages, wind gusts and dust. The proposed method does not require accurate pose estimation and considers the flying platform as a floating object. The Convolutional Neural Network (CNN) supervised image classifier method corrects the heading of the MAV towards the center of the mine tunnel by processing the image frames from a single on-board camera, while the platform navigates at constant altitude and desired velocity references. Moreover, the output of the CNN module can be used from the operator as means of collision prediction information. The efficiency of the proposed method has been successfully experimentally evaluated in multiple field trials in an underground mine in Sweden, demonstrating the capability of the proposed method in different areas and illumination levels.ISBN för värdpublikation: 978-3-90714-402-2, 978-1-7281-8813-3</p

Vision-based MAV Navigation in Underground Mine Using Convolutional Neural Network

This article presents a Convolutional Neural Network (CNN) method to enable autonomous navigation of low-cost Micro Aerial Vehicle (MAV) platforms along dark underground mine environments. The proposed CNN component provides on-line heading rate commands for the MAV by utilising the image stream from the on-board camera, thus allowing the platform to follow a collision-free path along the tunnel axis. A novel part of the developed method consists of the generation of the data-set used for training the CNN. More specifically, inspired from single image haze removal algorithms, various image data-sets collected from real tunnel environments have been processed offline to provide an estimation of the depth information of the scene, where ground truth is not available. The calculated depth map is used to extract the open space in the tunnel, expressed through the area centroid and is finally provided in the training of the CNN. The method considers the MAV as a floating object, thus accurate pose estimation is not required. Finally, the capability of the proposed method has been successfully experimentally evaluated in field trials in an underground mine in Sweden.ISBN för värdpublikation: 978-1-7281-4878-6, 978-1-7281-4879-3</p

Exploring the detectability of short-circuit faults in inverter-fed induction motors

This paper explores the possibility of creating an automatic method for assessing the condition of induction motor circuits fed by inverters. The stator current and magnetic flux are processed in the frequency domain and a feature selection stage is employed to pinpoint the most informative components to further be fed to a classifier that performs the assessment of the motor circuit. The results are promising, indicating that short circuit detection as well as quantification is feasible using noninvasive techniques

An Intelligent Injury Rehabilitation Guidance System for Recreational Runners Using Data Mining Algorithms

In recent years the number of people who exercise every day has increased dramatically. More precisely, due to COVID period many people have become recreational runners. Recreational running is a regular way to keep active and healthy at any age. Additionally, running is a popular physical exercise that offers numerous health advantages. However, recreational runners report a high incidence of musculoskeletal injuries due to running. The healthcare industry has been compelled to use information technology due to the quick rate of growth and developments in electronic systems, the internet, and telecommunications. Our proposed intelligent system uses data mining algorithms for the rehabilitation guidance of recreational runners with musculoskeletal discomfort. The system classifies recreational runners based on a questionnaire that has been built according to the severity, irritability, nature, stage, and stability model and advise them on the appropriate treatment plan/exercises to follow. Through rigorous testing across various case studies, our method has yielded highly promising results, underscoring its potential to significantly contribute to the well-being and rehabilitation of recreational runners facing musculoskeletal challenges