Developing Bioimage Informatics – from Microscopy to Software Solutions – with alpha2beta1 Integrin as a Case Study

Biokuvainformatiikan kehittäminen – mikroskopiasta ohjelmistoratkaisuihin – sovellusesimerkkinä α2β1-integriini Kun ihmisen genomi saatiin sekvensoitua vuonna 2003, biotieteiden päätehtäväksi tuli selvittää eri geenien tehtävät, ja erilaisista biokuvantamistekniikoista tuli keskeisiä tutkimusmenetelmiä. Teknologiset kehitysaskeleet johtivat erityisesti fluoresenssipohjaisten valomikroskopiatekniikoiden suosion räjähdysmäiseen kasvuun, mutta mikroskopian tuli muuntua kvalitatiivisesta tieteestä kvantitatiiviseksi. Tämä muutos synnytti uuden tieteenalan, biokuvainformatiikan, jonka on sanottu mahdollisesti mullistavan biotieteet. Tämä väitöskirja esittelee laajan, poikkitieteellisen työkokonaisuuden biokuvainformatiikan alalta. Väitöskirjan ensimmäinen tavoite oli kehittää protokollia elävien solujen neliulotteiseen konfokaalimikroskopiaan, joka oli yksi nopeimmin kasvavista biokuvantamismenetelmistä. Ihmisen kollageenireseptori α2β1-integriini, joka on tärkeä molekyyli monissa fysiologisissa ja patologisissa prosesseissa, oli sovellusesimerkkinä. Työssä saavutettiin selkeitä visualisointeja integriinien liikkeistä, yhteenkeräytymisestä ja solun sisään siirtymisestä, mutta työkaluja kuvainformaation kvantitatiiviseen analysointiin ei ollut. Väitöskirjan toiseksi tavoitteeksi tulikin tällaiseen analysointiin soveltuvan tietokoneohjelmiston kehittäminen. Samaan aikaan syntyi biokuvainformatiikka, ja kipeimmin uudella alalla kaivattiin erikoistuneita tietokoneohjelmistoja. Tämän väitöskirjatyön tärkeimmäksi tulokseksi muodostui näin ollen BioImageXD, uudenlainen avoimen lähdekoodin ohjelmisto moniulotteisten biokuvien visualisointiin, prosessointiin ja analysointiin. BioImageXD kasvoi yhdeksi alansa suurimmista ja monipuolisimmista. Se julkaistiin Nature Methods -lehden biokuvainformatiikkaa käsittelevässä erikoisnumerossa, ja siitä tuli tunnettu ja laajalti käytetty. Väitöskirjan kolmas tavoite oli soveltaa kehitettyjä menetelmiä johonkin käytännönläheisempään. Tehtiin keinotekoisia piidioksidinanopartikkeleita, joissa oli "osoitelappuina" α2β1-integriinin tunnistavia vasta-aineita. BioImageXD:n avulla osoitettiin, että nanopartikkeleilla on potentiaalia lääkkeiden täsmäohjaussovelluksissa. Tämän väitöskirjatyön yksi perimmäinen tavoite oli edistää uutta ja tuntematonta biokuvainformatiikan tieteenalaa, ja tämä tavoite saavutettiin erityisesti BioImageXD:n ja sen lukuisten julkaistujen sovellusten kautta. Väitöskirjatyöllä on merkittävää potentiaalia tulevaisuudessa, mutta biokuvainformatiikalla on vakavia haasteita. Ala on liian monimutkainen keskimääräisen biolääketieteen tutkijan hallittavaksi, ja alan keskeisin elementti, avoimen lähdekoodin ohjelmistokehitystyö, on aliarvostettu. Näihin seikkoihin tarvitaan useita parannuksia,

Rotation Invariant Real-time Face Detection and Recognition System

In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated - the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under different viewing angles in realtime. The development of such a system will enhance the capability and robustness of current face recognition technology. The whole-face approach recognizes faces by classifying a single feature vector consisting of the gray values of the whole face image. The component-based approach first locates the facial components and extracts them. These components are normalized and combined into a single feature vector for classification. The Support Vector Machine (SVM) is used as the classifier for both approaches. Extensive tests with respect to the robustness against pose changes are performed on a database that includes faces rotated up to about 40 degrees in depth. The component-based approach clearly outperforms the whole-face approach on all tests. Although this approach isproven to be more reliable, it is still too slow for real-time applications. That is the reason why a real-time face recognition system using the whole-face approach is implemented to recognize people in color video sequences.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

False normal Lung Clearance Index in infants with cystic fibrosis due to software algorithms.

BACKGROUND Lung clearance index (LCI), a marker of ventilation inhomogeneity, is elevated early in children with cystic fibrosis (CF). However, in infants with CF, LCI values are found to be normal, although structural lung abnormalities are often detectable. We hypothesized that this discrepancy is due to inadequate algorithms of the available software package. AIM Our aim was to challenge the validity of these software algorithms. METHODS We compared multiple breath washout (MBW) results of current software algorithms (automatic modus) to refined algorithms (manual modus) in 17 asymptomatic infants with CF, and 24 matched healthy term-born infants. The main difference between these two analysis methods lies in the calculation of the molar mass differences that the system uses to define the completion of the measurement. RESULTS In infants with CF the refined manual modus revealed clearly elevated LCI above 9 in 8 out of 35 measurements (23%), all showing LCI values below 8.3 using the automatic modus (paired t-test comparing the means, P < 0.001). Healthy infants showed normal LCI values using both analysis methods (n = 47, paired t-test, P = 0.79). The most relevant reason for false normal LCI values in infants with CF using the automatic modus was the incorrect recognition of the end-of-test too early during the washout. CONCLUSION We recommend the use of the manual modus for the analysis of MBW outcomes in infants in order to obtain more accurate results. This will allow appropriate use of infant lung function results for clinical and scientific purposes.

Face encoding and recognition in the human brain.

A dissociation between human neural systems that participate in the encoding and later recognition of new memories for faces was demonstrated by measuring memory task-related changes in regional cerebral blood flow with positron emission tomography. There was almost no overlap between the brain structures associated with these memory functions. A region in the right hippocampus and adjacent cortex was activated during memory encoding but not during recognition. The most striking finding in neocortex was the lateralization of prefrontal participation. Encoding activated left prefrontal cortex, whereas recognition activated right prefrontal cortex. These results indicate that the hippocampus and adjacent cortex participate in memory function primarily at the time of new memory encoding. Moreover, face recognition is not mediated simply by recapitulation of operations performed at the time of encoding but, rather, involves anatomically dissociable operations.

An integrative approach to face and expression recognition from 3D scans

Biometrics is afield of study which pursues the association of a person's identity with his/her physiological or behavioral characteristics.^ As one aspect of biometrics, face recognition has attracted special attention because it is a natural and noninvasive means to identify individuals. Most of the previous studies in face recognition are based on two-dimensional (2D) intensity images. Face recognition based on 2D intensity images, however, is sensitive to environment illumination and subject orientation changes, affecting the recognition results. With the development of three-dimensional (3D) scanners, 3D face recognition is being explored as an alternative to the traditional 2D methods for face recognition.^ This dissertation proposes a method in which the expression and the identity of a face are determined in an integrated fashion from 3D scans. In this framework, there is a front end expression recognition module which sorts the incoming 3D face according to the expression detected in the 3D scans. Then, scans with neutral expressions are processed by a corresponding 3D neutral face recognition module. Alternatively, if a scan displays a non-neutral expression, e.g., a smiling expression, it will be routed to an appropriate specialized recognition module for smiling face recognition.^ The expression recognition method proposed in this dissertation is innovative in that it uses information from 3D scans to perform the classification task. A smiling face recognition module was developed, based on the statistical modeling of the variance between faces with neutral expression and faces with a smiling expression.^ The proposed expression and face recognition framework was tested with a database containing 120 3D scans from 30 subjects (Half are neutral faces and half are smiling faces). It is shown that the proposed framework achieves a recognition rate 10% higher than attempting the identification with only the neutral face recognition module.^

An incremental multilinear system for human face learning and recognition

This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.

Vertical Bone Measurements From Cone Beam Computed Tomography Images Using Different Software Packages.

This article aimed at comparing the accuracy of linear measurement tools of different commercial software packages. Eight fully edentulous dry mandibles were selected for this study. Incisor, canine, premolar, first molar and second molar regions were selected. Cone beam computed tomography (CBCT) images were obtained with i-CAT Next Generation. Linear bone measurements were performed by one observer on the cross-sectional images using three different software packages: XoranCat®, OnDemand3D® and KDIS3D®, all able to assess DICOM images. In addition, 25% of the sample was reevaluated for the purpose of reproducibility. The mandibles were sectioned to obtain the gold standard for each region. Intraclass coefficients (ICC) were calculated to examine the agreement between the two periods of evaluation; the one-way analysis of variance performed with the post-hoc Dunnett test was used to compare each of the software-derived measurements with the gold standard. The ICC values were excellent for all software packages. The least difference between the software-derived measurements and the gold standard was obtained with the OnDemand3D and KDIS3D (-0.11 and -0.14 mm, respectively), and the greatest, with the XoranCAT (+0.25 mm). However, there was no statistical significant difference between the measurements obtained with the different software packages and the gold standard (p> 0.05). In conclusion, linear bone measurements were not influenced by the software package used to reconstruct the image from CBCT DICOM data.

Effects of temporal association on recognition memory

The influence of temporal association on the representation and recognition of objects was investigated. Observers were shown sequences of novel faces in which the identity of the face changed as the head rotated. As a result, observers showed a tendency to treat the views as if they were of the same person. Additional experiments revealed that this was only true if the training sequences depicted head rotations rather than jumbled views: in other words, the sequence had to be spatially as well as temporally smooth. Results suggest that we are continuously associating views of objects to support later recognition, and that we do so not only on the basis of the physical similarity, but also the correlated appearance in time of the objects.

Analysing the usefulness of motion correction software in myocardial perfusion imaging

Introduction Myocardial Perfusion Imaging (MPI) is a very important tool in the assessment of Coronary Artery Disease ( CAD ) patient s and worldwide data demonstrate an increasingly wider use and clinical acceptance. Nevertheless, it is a complex process and it is quite vulnerable concerning the amount and type of possible artefacts, some of them affecting seriously the overall quality and the clinical utility of the obtained data. One of the most in convenient artefacts , but relatively frequent ( 20% of the cases ) , is relate d with patient motion during image acquisition . Mostly, in those situations, specific data is evaluated and a decisi on is made between A) accept the results as they are , consider ing that t he “noise” so introduced does not affect too seriously the final clinical information, or B) to repeat the acquisition process . Another possib ility could be to use the “ Motion Correcti on Software” provided within the software package included in any actual gamma camera. The aim of this study is to compare the quality of the final images , obtained after the application of motion correction software and after the repetition of image acqui sition. Material and Methods Thirty cases of MPI affected by Motion Artefacts and repeated , were used. A group of three, independent (blinded for the differences of origin) expert Nuclear Medicine Clinicians had been invited to evaluate the 30 sets of thre e images - one set for each patient - being ( A) original image , motion uncorrected , (B) original image, motion corrected, and (C) second acquisition image, without motion . The results so obtained were statistically analysed . Results and Conclusion Results obtained demonstrate that the use of the Motion Correction Software is useful essentiall y if the amplitude of movement is not too important (with this specific quantification found hard to define precisely , due to discrepancies between clinicians and other factors , namely between one to another brand); when that is not the case and the amplitude of movement is too important , the n the percentage of agreement between clinicians is much higher and the repetition of the examination is unanimously considered ind ispensable.

Dados de reconhecimento facial em Business Intelligence

Vivemos cada vez mais numa era de crescentes avanços tecnológicos em diversas áreas. O que há uns anos atrás era considerado como praticamente impossível, em muitos dos casos, já se tornou realidade. Todos usamos tecnologias como, por exemplo, a Internet, Smartphones e GPSs de uma forma natural. Esta proliferação da tecnologia permitiu tanto ao cidadão comum como a organizações a sua utilização de uma forma cada vez mais criativa e simples de utilizar. Além disso, a cada dia que passa surgem novos negócios e startups, o que demonstra o dinamismo que este crescimento veio trazer para a indústria. A presente dissertação incide sobre duas áreas em forte crescimento: Reconhecimento Facial e Business Intelligence (BI), assim como a respetiva combinação das duas com o objetivo de ser criado um novo módulo para um produto já existente. Tratando-se de duas áreas distintas, é primeiramente feito um estudo sobre cada uma delas. A área de Business Intelligence é vocacionada para organizações e trata da recolha de informação sobre o negócio de determinada empresa, seguindo-se de uma posterior análise. A grande finalidade da área de Business Intelligence é servir como forma de apoio ao processo de tomada de decisão por parte dos analistas e gestores destas organizações. O Reconhecimento Facial, por sua vez, encontra-se mais presente na sociedade. Tendo surgido no passado através da ficção científica, cada vez mais empresas implementam esta tecnologia que tem evoluído ao longo dos anos, chegando mesmo a ser usada pelo consumidor final, como por exemplo em Smartphones. As suas aplicações são, portanto, bastante diversas, desde soluções de segurança até simples entretenimento. Para estas duas áreas será assim feito um estudo com base numa pesquisa de publicações de autores da respetiva área. Desde os cenários de utilização, até aspetos mais específicos de cada uma destas áreas, será assim transmitido este conhecimento para o leitor, o que permitirá uma maior compreensão por parte deste nos aspetos relativos ao desenvolvimento da solução. Com o estudo destas duas áreas efetuado, é então feita uma contextualização do problema em relação à área de atuação da empresa e quais as abordagens possíveis. É também descrito todo o processo de análise e conceção, assim como o próprio desenvolvimento numa vertente mais técnica da solução implementada. Por fim, são apresentados alguns exemplos de resultados obtidos já após a implementação da solução.

IWP strategy: TechFrame

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Development of a semi-automatic self learning pattern recognition system for the control of electronic boards

This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway - Mayo Institute of Technology and an industrial company, Tyco/Mallinckrodt Galway. The project aimed to develop a semi - automatic, self - learning pattern recognition system capable of detecting defects on the printed circuits boards such as component vacancy, component misalignment, component orientation, component error, and component weld. The research was conducted in three directions: image acquisition, image filtering/recognition and software development. Image acquisition studied the process of forming and digitizing images and some fundamental aspects regarding the human visual perception. The importance of choosing the right camera and illumination system for a certain type of problem has been highlighted. Probably the most important step towards image recognition is image filtering, The filters are used to correct and enhance images in order to prepare them for recognition. Convolution, histogram equalisation, filters based on Boolean mathematics, noise reduction, edge detection, geometrical filters, cross-correlation filters and image compression are some examples of the filters that have been studied and successfully implemented in the software application. The software application developed during the research is customized in order to meet the requirements of the industrial partner. The application is able to analyze pictures, perform the filtering, build libraries, process images and generate log files. It incorporates most of the filters studied and together with the illumination system and the camera it provides a fully integrated framework able to analyze defects on printed circuit boards.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Aging effects on selective attention-related electroencephalographic patterns during face encoding.

Previous electrophysiological studies revealed that human faces elicit an early visual event-related potential (ERP) within the occipito-temporal cortex, the N170 component. Although face perception has been proposed to rely on automatic processing, the impact of selective attention on N170 remains controversial both in young and elderly individuals. Using early visual ERP and alpha power analysis, we assessed the influence of aging on selective attention to faces during delayed-recognition tasks for face and letter stimuli, examining 36 elderly and 20 young adults with preserved cognition. Face recognition performance worsened with age. Aging induced a latency delay of the N1 component for faces and letters, as well as of the face N170 component. Contrasting with letters, ignored faces elicited larger N1 and N170 components than attended faces in both age groups. This counterintuitive attention effect on face processing persisted when scenes replaced letters. In contrast with young, elderly subjects failed to suppress irrelevant letters when attending faces. Whereas attended stimuli induced a parietal alpha band desynchronization within 300-1000 ms post-stimulus with bilateral-to-right distribution for faces and left lateralization for letters, ignored and passively viewed stimuli elicited a central alpha synchronization larger on the right hemisphere. Aging delayed the latency of this alpha synchronization for both face and letter stimuli, and reduced its amplitude for ignored letters. These results suggest that due to their social relevance, human faces may cause paradoxical attention effects on early visual ERP components, but they still undergo classical top-down control as a function of endogenous selective attention. Aging does not affect the face bottom-up alerting mechanism but reduces the top-down suppression of distracting letters, possibly impinging upon face recognition, and more generally delays the top-down suppression of task-irrelevant information.