Fast global kernel density mode seeking with application to localisation and tracking

Copyright © 2005 IEEE.We address the problem of seeking the global mode of a density function using the mean shift algorithm. Mean shift, like other gradient ascent optimisation methods, is susceptible to local maxima, and hence often fails to find the desired global maximum. In this work, we propose a multi-bandwidth mean shift procedure that alleviates this problem, which we term annealed mean shift, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the algorithm plays the same role as the temperature in annealing. We observe that the over-smoothed density function with a sufficiently large bandwidth is uni-modal. Using a continuation principle, the influence of the global peak in the density function is introduced gradually. In this way the global maximum is more reliably located. Generally, the price of this annealing-like procedure is that more iterations are required. Since it is imperative that the computation complexity is minimal in real-time applications such as visual tracking. We propose an accelerated version of the mean shift algorithm. Compared with the conventional mean shift algorithm, the accelerated mean shift can significantly decrease the number of iterations required for convergence. The proposed algorithm is applied to the problems of visual tracking and object localisation. We empirically show on various data sets that the proposed algorithm can reliably find the true object location when the starting position of mean shift is far away from the global maximum, in contrast with the conventional mean shift algorithm that will usually get trapped in a spurious local maximum.Chunhua Shen, Michael J. Brooks and Anton van den Henge

Text Detection in Natural Scenes and Technical Diagrams with Convolutional Feature Learning and Cascaded Classification

An enormous amount of digital images are being generated and stored every day. Understanding text in these images is an important challenge with large impacts for academic, industrial and domestic applications. Recent studies address the difficulty of separating text targets from noise and background, all of which vary greatly in natural scenes. To tackle this problem, we develop a text detection system to analyze and utilize visual information in a data driven, automatic and intelligent way. The proposed method incorporates features learned from data, including patch-based coarse-to-fine detection (Text-Conv), connected component extraction using region growing, and graph-based word segmentation (Word-Graph). Text-Conv is a sliding window-based detector, with convolution masks learned using the Convolutional k-means algorithm (Coates et. al, 2011). Unlike convolutional neural networks (CNNs), a single vector/layer of convolution mask responses are used to classify patches. An initial coarse detection considers both local and neighboring patch responses, followed by refinement using varying aspect ratios and rotations for a smaller local detection window. Different levels of visual detail from ground truth are utilized in each step, first using constraints on bounding box intersections, and then a combination of bounding box and pixel intersections. Combining masks from different Convolutional k-means initializations, e.g., seeded using random vectors and then support vectors improves performance. The Word-Graph algorithm uses contextual information to improve word segmentation and prune false character detections based on visual features and spatial context. Our system obtains pixel, character, and word detection f-measures of 93.14%, 90.26%, and 86.77% respectively for the ICDAR 2015 Robust Reading Focused Scene Text dataset, out-performing state-of-the-art systems, and producing highly accurate text detection masks at the pixel level. To investigate the utility of our feature learning approach for other image types, we perform tests on 8- bit greyscale USPTO patent drawing diagram images. An ensemble of Ada-Boost classifiers with different convolutional features (MetaBoost) is used to classify patches as text or background. The Tesseract OCR system is used to recognize characters in detected labels and enhance performance. With appropriate pre-processing and post-processing, f-measures of 82% for part label location, and 73% for valid part label locations and strings are obtained, which are the best obtained to-date for the USPTO patent diagram data set used in our experiments. To sum up, an intelligent refinement of convolutional k-means-based feature learning and novel automatic classification methods are proposed for text detection, which obtain state-of-the-art results without the need for strong prior knowledge. Different ground truth representations along with features including edges, color, shape and spatial relationships are used coherently to improve accuracy. Different variations of feature learning are explored, e.g. support vector-seeded clustering and MetaBoost, with results suggesting that increased diversity in learned features benefit convolution-based text detectors

Multiscale image analysis of calcium dynamics in cardiac myocytes

Cardiac myocytes constitute a unique physiological system. They are the muscle cells that build up heart tissue and provide the force to pump blood by synchronously contracting at every beat. This contraction is regulated by calcium concentration, among other ions, which exhibits a very complex behaviour, rich in dynamical states at the molecular, cellular and tissue levels. Details of such dynamical patterns are closely related to the mechanisms responsible for cardiac function and also cardiac disease, which is the first cause of death in the modern world. The emerging field of translational cardiology focuses on the study of how such mechanisms connect and influence each other across spatial and temporal scales finally yielding to a certain clinical condition. In order to study such patterns, we benefit from the recent and very important advances in the field of experimental cell physiology. In particular, fluorescence microscopy allows us to observe the distribution of calcium in the cell with a spatial resolution below the micron and a frame rate around the millisecond, thus providing a very accurate monitoring of calcium fluxes in the cell. This thesis is the result of over five years' work on biological signal and digital image processing of cardiac cells. During this period of time the aim has been to develop computational techniques for extracting quantitative data of physiological relevance from microscopy images at different scales. The two main subjects covered in the thesis are image segmentation and classification methods applied to fluorescence microscopy imaging of cardiac myocytes. These methods are applied to a variety of problems involving different space and time scales such as the localisation of molecular receptors, the detection and characterisation of spontaneous calcium-release events and the propagation of calcium waves across a culture of cardiac cells. The experimental images and data have been provided by four internationally renowned collaborators in the field. It is thanks to them and their teams that this thesis has been possible. They are Dr. Leif Hove-Madsen from the Institut de Ciències Cardiovasculars de Catalunya in Barcelona, Prof. S. R. Wayne Chen from the Department of Physiology and Pharmacology in the Libin Cardiovascular Institute of Alberta, University of Calgary, Dr. Peter P. Jones from the Department of Physiology in the University of Otago, and Prof. Glen Tibbits from the Department of Biomedical Physiology & Kinesiology at the Simon Fraser University in Vancouver. The work belongs to the biomedical engineering discipline, focusing on the engineering perspective by applying physics and mathematics to solve biomedical problems. Specifically, we frame our contributions in the field of computational translational cardiology, attempting to connect molecular mechanisms in cardiac cells up to cardiac disease by developing signal and image-processing methods and machine-learning methods that are scalable through the different scales. This computational approach allows for a quantitative, robust and reproducible analysis of the experimental data and allows us to obtain results that otherwise would not be possible by means of traditional manual methods. The results of the thesis provide specific insight into different cell mechanisms that have a non-negligible impact at the clinical level. In particular, we gain a deeper knowledge of cell mechanisms related to cardiac arrhythmia, fibrillation phenomena, the emergence of alternans and anomalies in calcium handling due to cell ageing.Els cardiomiòcits constitueixen un sistema fisiològic únic. Són les cèl·lules muscular que formen el cor i proporcionen la força per bombar la sang fent una contracció a cada batec. La regulació d'aquesta contracció es fa mitjançant concentració de calci (entre d'altres ions) i presenta una dinàmica molt complexa tant a l'escala molecular, cel·lular i de teixit. Detalls d'aquesta dinàmica estan fortament relacionats amb la funció cardíaca i per sobre de tot amb patologies cardíaques. La disciplina emergent de la cardiologia translacional es centra en l'estudi de com aquests mecanismes es connecten i s'influencien entre sí a través de diferents escales temporals i espacials finalment donant lloc a condicions clíniques. Per estudiar aquests patrons ens beneficiem dels recents avenços en fisiologia i biologia cel·lular. En particular, la microscòpia de fluorescència ens permet observar la distribució de calci dins una cèl·lula amb una resolució espacial per sota de la micra i temporal per sota del mil·lisegon, permetent un monitoratge acurat dels fluxos de calci en la cèl·lula cardíaca. Aquesta tesi és el resultat de més de cinc anys de feina en processament de senyal i imatge de cardiomiòcits humans. Durant aquest període de temps l'objectiu principal ha estat desenvolupar tècniques computacionals per extraure dades d'imatges de microscòpia amb rellevància fisiològica. Els dos temes principals coberts a la tesi són segmentació d'imatges i classificadors, aplicats a imatges de microscòpia de fluorescència de cardiomiòcits. Els mètodes s'apliquen a diferents problemes involucrant diverses escales espacials i temporals, des de determinar la posició de receptors a l’escala molecular passant detectar i caracteritzar alliberament espontani de calci intracel·lular fins a la propagació d'ones de calci en un cultiu de cèl·lules cardíaques. Les dades experimentals han estat proporcionades per quatre col·laboradors de renom internacional. És gràcies a ells i els seus equips que aquesta tesi ha estat possible. Són el Dr. Leif Hove-Madsen de l'Institut de Ciències Cardiovasculars de Catalunya a Barcelona, el Dr. S.R. Wayne Chen del Department of Physiology and Pharmacology al Libin Cardiovascular Institute of Alberta, University of Calgary, el Dr. Peter P. Jones del Department of Physiology a la University of Otago, i el Dr. Glen Tibbits del Department of Biomedical Physiology & Kinesiology de la Simon Fraser University a Vancouver. El treball pertany a la disciplina de la enginyeria biomèdica, fent èmfasi a la perspectiva de l'enginyeria, aplicant física i matemàtiques per solucionar problemes de la biomedicina. Específicament, s'emmarca en la cardiologia translacional computacional, mirant de connectar mecanismes a l’escala molecular amb patologies cardíaques mitjançant tècniques de processament de dades i aprenentatge automàtic que són escalables a les diferents escales d’aplicació. Aquest enfocament computacional permet una anàlisi quantitatiu, robust i reproduïble de les dades experimentals i ens permet d'obtenir resultats que serien impossibles d'assolir mitjançant els tradicionals mètodes manuals. Els resultats que proporciona la tesi han permès aprofundir en l'enteniment de diferents mecanismes fisiològics amb impacte en l'àmbit clínic. Particularment hem permès d’assolir coneixements relacionats amb l'arítmia cardíaca, la fibril·lació, processos d'alternança i anomalies relacionades amb l’envelliment

Computer-aided image quality assessment in automated 3D breast ultrasound images

Automated 3D breast ultrasound (ABUS) is a valuable, non-ionising adjunct to X-ray mammography for breast cancer screening and diagnosis for women with dense breasts. High image quality is an important prerequisite for diagnosis and has to be guaranteed at the time of acquisition. The high throughput of images in a screening scenario demands for automated solutions. In this work, an automated image quality assessment system rating ABUS scans at the time of acquisition was designed and implemented. Quality assessment of present diagnostic ultrasound images has rarely been performed demanding thorough analysis of potential image quality aspects in ABUS. Therefore, a reader study was initiated, making two clinicians rate the quality of clinical ABUS images. The frequency of specific quality aspects was evaluated revealing that incorrect positioning and insufficiently applied contact fluid caused the most relevant image quality issues. The relative position of the nipple in the image, the acoustic shadow caused by the nipple as well as the shape of the breast contour reflect patient positioning and ultrasound transducer handling. Morphological and histogram-based features utilized for machine learning to reproduce the manual classification as provided by the clinicians. At 97 % specificity, the automatic classification achieved sensitivities of 59 %, 45 %, and 46 % for the three aforementioned aspects, respectively. The nipple is an important landmark in breast imaging, which is generally---but not always correctly---pinpointed by the technicians. An existing nipple detection algorithm was extended by probabilistic atlases and exploited for automatic detection of incorrectly annotated nipple marks. The nipple detection rate was increased from 82 % to 85 % and the classification achieved 90 % sensitivity at 89 % specificity. A lack of contact fluid between transducer and skin can induce reverberation patterns and acoustic shadows, which can possibly obscure lesions. Parameter maps were computed in order to localize these artefact regions and yielded a detection rate of 83 % at 2.6 false positives per image. Parts of the presented work were integrated to clinical workflow making up a novel image quality assessment system that supported technicians in their daily routine by detecting images of insufficient quality and indicating potential improvements for a repeated scan while the patient was still in the examination room. First evaluations showed that the proposed method sensitises technicians for the radiologists' demands on diagnostically valuable images

A real-time data mining technique applied for critical ECG rhythm on handheld device

Sudden cardiac arrest is often caused by ventricular arrhythmias and these episodes can lead to death for patients with chronic heart disease. Hence, detection of such arrhythmia is crucial in mobile ECG monitoring. In this research, a systematic study is carried out to investigate the possible limitations that are preventing the realisation of a real-time ECG arrhythmia data-mining algorithm suitable for application on mobile devices. Based on the findings, a computationally lightweight algorithm is devised and tested. Ventricular tachycardia (VT) is the most common type of ventricular arrhythmias and is also the deadliest.. A ventricular tachycardia (VT) episode is due to a disorder ofthe regular contractions ofthe heart. It occurs when the human heart ventricles generate a rapid heartbeat which disrupts the regular physiology cycle. The normal sinus rhythm (NSR) of a regular human heart beat signal has its signature PQRST waveform and in regular pattern. Whereas, the characteristics of a ventricular tachycardia (VT) signal waveforms are short R-R intervals, widen QRS duration and the absence of P-waves. Each type of ECG arrhythmia previously mentioned has a unique waveform signature that can be exploited as features to be used for the realization of an automated ECG analysis application. In order to extract this known ECG waveform feature, a time-domain analysis is proposed for feature extraction. Cross-correlation allows the computation of a co-efficient that quantifies the similarity between two times-series. Hence, by cross-correlating known ECG waveform templates with an unknown ECG signal, the coefficient can indicate the similarities. In previous published work, a preliminary study was carried out. The cross-correlation coefficient wave (CCW) technique was introduced for feature extraction. The outcome ofthis work presents CCW as a promising feature to differentiate between NSR, VT and Vfib signals. Moreover, cross-correlation computation does not require high computational overhead. Next, an automated detection algorithm requires a classification mechanism to make sense of the feature extracted. A further study is conducted and published, a fuzzy set k-NN classifier was introduced for the classification of CCW feature extracted from ECG signal segments. A training set of size 180 is used. The outcome of the study indicates that the computationally light-weight fuzzy k-NN classifier can reliably classify between NSR and VT signals, the class detection rate is low for classifying Vfib signal using the fuzzy k-NN classifier. Hence, a modified algorithm known as fuzzy hybrid classifier is proposed. By implementing an expert knowledge based fuzzy inference system for classification of ECG signal; the Vfib signal detection rate was improved. The comparison outcome was that the hybrid fuzzy classifier is able to achieve 91.1% correct rate, 100% sensitivity and 100% specificity. The previously mentioned result outperforms the compared classifiers. The proposed detection and classification algorithm is able to achieve high accuracy in analysing ECG signal feature of NSR, VT and Vfib nature. Moreover, the proposed classifier is successfully implemented on a smart mobile device and it is able to perform data-mining of the ECG signal with satisfiable results

Information Extraction from Messy Data, Noisy Spectra, Incomplete Data, and Unlabeled Images

Data collected from real-world scenarios are never ideal but often messy because data errors are inevitable and may occur in creative and unexpected ways. And there are always some unexpected tricky troubles between ideal theory and real-world applications. Although with the development of data science, more and more elegant algorithms have been well developed and validated by rigorous proof, data scientists still have to spend 50\% to 80\% of their work time on cleaning and organizing data, leaving little time for actual data analysis. This dissertation research involves three scenarios of statistical modeling with common data issues: quantifying function effect on noisy functional data, multistage decision-making model over incomplete data, and unsupervised image segmentation over imperfect engineering images. And three methodologies are proposed accordingly to solve them efficiently. In Chapter 2, a general two-step procedure is proposed to quantify the effects of a certain treatment on the spectral signals subjecting to multiple uncertainties for an engineering application that involves materials treatment for aircraft maintenance. With this procedure, two types of uncertainties in the spectral signals, offset shift and multiplicative error, are carefully addressed. In the two-step procedure, a novel optimization problem is formulated to estimate the representative template spectrum first, and then another optimization problem is formulated to obtain the pattern of modification $\mathbf{g}$ that reveals how the treatment affects the shape of the spectral signal, as well as a vector $\boldsymbol{\delta}$ that describes the degree of change caused by different treatment magnitudes. The effectiveness of the proposed method is validated in a simulation study. \textcolor{black}{Furtherly, in} a real case study, the proposed method \textcolor{black}{is used} to investigate the effect of plasma exposure on the FTIR spectra. As a result, the proposed method effectively identifies the pattern of modification under uncertainties in the manufacturing environment, which matches the knowledge of the affected chemical components by the plasma treatment. And the recovered magnitude of modification provides guidance in selecting the control parameter of the plasma treatment. In Chapter 3, an active learning-based multistage sequential decision-making model is proposed to assist doctors and patients to make cost-effective treatment recommendations when some clinical data are more expensive or time-consuming to collect than other laboratory data. The main idea is to formulate the incomplete clinical data into a multistage decision-making model where the doctors can make diagnostics decisions sequentially in these stages, and actively collect only the necessary examination data from certain patients rather than all. There are two novelties in estimating parameters in the proposed model. First, unlike the existed ordinal logistic regression model which only models a single stage, a multistage model is built by maximizing the joint likelihood function for all samples in all stages. Second, considering that the data in different stages are nested in a cumulative way, it is assumed that the coefficients for common features in different stages are invariant. Compared with the baseline approach that models each stage individually and independently, the proposed multistage model with common coefficients assumption has significant advantages. It reduces the number of variables to estimate significantly, improves the computational efficiency, and makes the doctors feel intuitive by assuming that newly added features will not affect the weights of existed ones. In a simulation study, the relative efficiency of the proposed method with regards to the baseline approach is 162\% to 1,938\%, proving its efficiency and effectiveness soundly. Then, in a real case study, the proposed method estimates all parameters very efficiently and reasonably. %It estimates all parameters simultaneously to reach the global optimum and fully considers the cumulative characteristics between these stages by making common coefficients assumption. In Chapter 4, a simple yet very effective unsupervised image segmentation method, called RG-filter, is proposed to segment engineering images with no significant contrast between foreground and background for a material testing application. With the challenge of limited data size, imperfect data quality, unreachable binary true label, we developed the RG-filter which thresholding the pixels according to the relative magnitude of the R channel and G channel of the RGB image. %And the other one is called the superpixels clustering algorithm, where we add another layer of clustering over the segmented superpixels to binarize their labels. To test the performance of the existed image segmentation and proposed algorithm on our CFRP image data, we conducted a series of experiments over an example specimen. Comparing all the pixel labeling results, the proposed RG-filter outperforms the others to be the most recommended one. in addition, it is super intuitive and efficient in computation. The proposed RG-filter can help to analyze the failure mode distribution and proportion on the surface of composite material after destructive DCB testing. The result can help engineers better understand the weak link during the bonding of composite materials, which may provide guidance on how to improve the joining of structures during aircraft maintenance. Also, it can be crucial data when modeling together with some downstream data as a whole. And if we can predict it from other variables, the destructive DCB testing can be avoided, a lot of time and money can be saved. In Chapter 5, we concluded the dissertation and summarized the original contributions. In addition, future research topics associated with the dissertation have also been discussed. In summary, the dissertation contributes to the area of \textit{System Informatics and Control} (SIAC) to develop systematic methodologies based on messy real-world data in the field of composite materials and healthcare. The fundamental methodologies developed in this thesis have the potential to be applied to other advanced manufacturing systems.Ph.D

A comparative study of algorithms for automatic segmentation of dermoscopic images

Melanoma is the most common as well as the most dangerous type of skin cancer. Nevertheless, it can be effectively treated if detected early. Dermoscopy is one of the major non-invasive imaging techniques for the diagnosis of skin lesions. The computer-aided diagnosis based on the processing of dermoscopic images aims to reduce the subjectivity and time-consuming analysis related to traditional diagnosis. The first step of automatic diagnosis is image segmentation. In this project, the implementation and evaluation of several methods were proposed for the automatic segmentation of lesion regions in dermoscopic images, along with the corresponding implemented phases for image preprocessing and postprocessing. The developed algorithms include methods based on different state of the art techniques. The main groups of techniques which have been selected to be studied and implemented are thresholding-based methods, region-based methods, segmentation based on deformable models, as well as a new proposed approach based on the bag-of-words model. The implemented methods incorporate modifications for a better adaptation to features associated with dermoscopic images. Each implemented method was applied to a database constituted by 724 dermoscopic images. The output of the automatic segmentation procedure for each image was compared with the corresponding manual segmentation in order to evaluate the performance. The comparison between algorithms was carried out regarding the obtained evaluation metrics. The best results were achieved by the combination of region-based segmentation based on the multi-region adaptation of the k-means algorithm and the subIngeniería de Sistemas Audiovisuale

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition