Predictive Modelling of Bone Ageing

Bone age assessment (BAA) is a task performed daily by paediatricians in hospitalsworldwide. The main reasons for BAA to be performed are: fi�rstly, diagnosis of growth disorders through monitoring skeletal development; secondly, prediction of final adult height; and fi�nally, verifi�cation of age claims. Manually predicting bone age from radiographs is a di�fficult and time consuming task. This thesis investigates bone age assessment and why automating the process will help. A review of previous automated bone age assessment systems is undertaken and we investigate why none of these systems have gained widespread acceptance. We propose a new automated method for bone age assessment, ASMA (Automated Skeletal Maturity Assessment). The basic premise of the approach is to automatically extract descriptive shape features that capture the human expertise in forming bone age estimates. The algorithm consists of the following six modularised stages: hand segmentation; hand segmentation classifi�cation; bone segmentation; feature extraction; bone segmentation classifi�cation; bone age prediction. We demonstrate that ASMA performs at least as well as other automated systems and that models constructed on just three bones are as accurate at predicting age as expert human assessors using the standard technique. We also investigate the importance of ethnicity and gender in skeletal development. Our conclusion is that the feature based system of separating the image processing from the age modelling is the best approach, since it off�ers flexibility and transparency, and produces accurate estimates

Face recognition by means of advanced contributions in machine learning

Face recognition (FR) has been extensively studied, due to both scientific fundamental challenges and current and potential applications where human identification is needed. FR systems have the benefits of their non intrusiveness, low cost of equipments and no useragreement requirements when doing acquisition, among the most important ones. Nevertheless, despite the progress made in last years and the different solutions proposed, FR performance is not yet satisfactory when more demanding conditions are required (different viewpoints, blocked effects, illumination changes, strong lighting states, etc). Particularly, the effect of such non-controlled lighting conditions on face images leads to one of the strongest distortions in facial appearance. This dissertation addresses the problem of FR when dealing with less constrained illumination situations. In order to approach the problem, a new multi-session and multi-spectral face database has been acquired in visible, Near-infrared (NIR) and Thermal infrared (TIR) spectra, under different lighting conditions. A theoretical analysis using information theory to demonstrate the complementarities between different spectral bands have been firstly carried out. The optimal exploitation of the information provided by the set of multispectral images has been subsequently addressed by using multimodal matching score fusion techniques that efficiently synthesize complementary meaningful information among different spectra. Due to peculiarities in thermal images, a specific face segmentation algorithm has been required and developed. In the final proposed system, the Discrete Cosine Transform as dimensionality reduction tool and a fractional distance for matching were used, so that the cost in processing time and memory was significantly reduced. Prior to this classification task, a selection of the relevant frequency bands is proposed in order to optimize the overall system, based on identifying and maximizing independence relations by means of discriminability criteria. The system has been extensively evaluated on the multispectral face database specifically performed for our purpose. On this regard, a new visualization procedure has been suggested in order to combine different bands for establishing valid comparisons and giving statistical information about the significance of the results. This experimental framework has more easily enabled the improvement of robustness against training and testing illumination mismatch. Additionally, focusing problem in thermal spectrum has been also addressed, firstly, for the more general case of the thermal images (or thermograms), and then for the case of facialthermograms from both theoretical and practical point of view. In order to analyze the quality of such facial thermograms degraded by blurring, an appropriate algorithm has been successfully developed. Experimental results strongly support the proposed multispectral facial image fusion, achieving very high performance in several conditions. These results represent a new advance in providing a robust matching across changes in illumination, further inspiring highly accurate FR approaches in practical scenarios.El reconeixement facial (FR) ha estat àmpliament estudiat, degut tant als reptes fonamentals científics que suposa com a les aplicacions actuals i futures on requereix la identificació de les persones. Els sistemes de reconeixement facial tenen els avantatges de ser no intrusius,presentar un baix cost dels equips d’adquisició i no la no necessitat d’autorització per part de l’individu a l’hora de realitzar l'adquisició, entre les més importants. De totes maneres i malgrat els avenços aconseguits en els darrers anys i les diferents solucions proposades, el rendiment del FR encara no resulta satisfactori quan es requereixen condicions més exigents (diferents punts de vista, efectes de bloqueig, canvis en la il·luminació, condicions de llum extremes, etc.). Concretament, l'efecte d'aquestes variacions no controlades en les condicions d'il·luminació sobre les imatges facials condueix a una de les distorsions més accentuades sobre l'aparença facial. Aquesta tesi aborda el problema del FR en condicions d'il·luminació menys restringides. Per tal d'abordar el problema, hem adquirit una nova base de dades de cara multisessió i multiespectral en l'espectre infraroig visible, infraroig proper (NIR) i tèrmic (TIR), sota diferents condicions d'il·luminació. En primer lloc s'ha dut a terme una anàlisi teòrica utilitzant la teoria de la informació per demostrar la complementarietat entre les diferents bandes espectrals objecte d’estudi. L'òptim aprofitament de la informació proporcionada pel conjunt d'imatges multiespectrals s'ha abordat posteriorment mitjançant l'ús de tècniques de fusió de puntuació multimodals, capaces de sintetitzar de manera eficient el conjunt d’informació significativa complementària entre els diferents espectres. A causa de les característiques particulars de les imatges tèrmiques, s’ha requerit del desenvolupament d’un algorisme específic per la segmentació de les mateixes. En el sistema proposat final, s’ha utilitzat com a eina de reducció de la dimensionalitat de les imatges, la Transformada del Cosinus Discreta i una distància fraccional per realitzar les tasques de classificació de manera que el cost en temps de processament i de memòria es va reduir de forma significa. Prèviament a aquesta tasca de classificació, es proposa una selecció de les bandes de freqüències més rellevants, basat en la identificació i la maximització de les relacions d'independència per mitjà de criteris discriminabilitat, per tal d'optimitzar el conjunt del sistema. El sistema ha estat àmpliament avaluat sobre la base de dades de cara multiespectral, desenvolupada pel nostre propòsit. En aquest sentit s'ha suggerit l’ús d’un nou procediment de visualització per combinar diferents bandes per poder establir comparacions vàlides i donar informació estadística sobre el significat dels resultats. Aquest marc experimental ha permès més fàcilment la millora de la robustesa quan les condicions d’il·luminació eren diferents entre els processos d’entrament i test. De forma complementària, s’ha tractat la problemàtica de l’enfocament de les imatges en l'espectre tèrmic, en primer lloc, pel cas general de les imatges tèrmiques (o termogrames) i posteriorment pel cas concret dels termogrames facials, des dels punt de vista tant teòric com pràctic. En aquest sentit i per tal d'analitzar la qualitat d’aquests termogrames facials degradats per efectes de desenfocament, s'ha desenvolupat un últim algorisme. Els resultats experimentals recolzen fermament que la fusió d'imatges facials multiespectrals proposada assoleix un rendiment molt alt en diverses condicions d’il·luminació. Aquests resultats representen un nou avenç en l’aportació de solucions robustes quan es contemplen canvis en la il·luminació, i esperen poder inspirar a futures implementacions de sistemes de reconeixement facial precisos en escenaris no controlats.Postprint (published version

A web-based framework devised using a Model-View-Controller architecture

Dissertação de Mestrado em Engenharia Biomédica-Desporto e ReabilitaçãoEste trabalho tem como objetivo desenvolver uma web-based framework devised using a Model-View-Controller architecture. Esta aplicação foi desenvolvida para a elaboração de relatórios de análise de marcha ou movimento do complexo do ombro, utilizando os benefícios da modelagem biomecânica do OpenSim. Foi construído combinando as tecnologias web e a linguagem de programação Python, de forma a melhorar a usabilidade, interação, extensibilidade e acrescentar um grau de automação necessário em aplicações clínicas. A interface foi projetada sob uma arquitetura Model-View-Controller (MVC) num servidor web Apache. Esta permite aos usuários efectuarem upload de informações clínicas do paciente (por exemplo, informações sobre gênero, idade, dor, incapacidade e outros), determinar como o modelo de antropometria musculoesquelético selecionado deve ser modificado, de modo a que corresponda melhor às características dos pacientes e em que grau o segmento de cada modelo (marcadores), deve coincidir com os dados de movimento recolhidos durante o processo de cinemática inversa. Por fim permite ao usuário definir as variáveis de relatório; se o relatório deve conter resultados de um dado ensaio, uma análise inter-ensaios ou comparar o movimento reconstruído com conjunto de dados normativos correspondentes; se a classificação apresenta uma disfunção do movimento e qual é a sua precisão; bem como pode colocar anotações com informação para cada gráfico. A interface foi testada com o Sytem Usability Scale (SUS) em dois grupos, que são representativos dos potenciais usuários: a) estudantes de engenharia biomédica; b) clínicos e estudantes de fisioterapia. Neste teste, avaliámos a usability (com scores de 74,2 e 84,4 para o grupo a) e b), respetivamente) e a learnability (com scores de 67,9 e 78,6 para o grupo a) e b), respetivamente) demonstrando que a interface é útil, clara, fácil de usar, intuitiva e recomendável

Multi-fractal dimension features by enhancing and segmenting mammogram images of breast cancer

The common malignancy which causes deaths in women is breast cancer. Early detection of breast cancer using mammographic image can help in reducing the mortality rate and the probability of recurrence. Through mammographic examination, breast lesions can be detected and classified. Breast lesions can be detected using many popular tools such as Magnetic Resonance Imaging (MRI), ultrasonography, and mammography. Although mammography is very useful in the diagnosis of breast cancer, the pattern similarities between normal and pathologic cases makes the process of diagnosis difficult. Therefore, in this thesis Computer Aided Diagnosing (CAD) systems have been developed to help doctors and technicians in detecting lesions. The thesis aims to increase the accuracy of diagnosing breast cancer for optimal classification of cancer. It is achieved using Machine Learning (ML) and image processing techniques on mammogram images. This thesis also proposes an improvement of an automated extraction of powerful texture sign for classification by enhancing and segmenting the breast cancer mammogram images. The proposed CAD system consists of five stages namely pre-processing, segmentation, feature extraction, feature selection, and classification. First stage is pre-processing that is used for noise reduction due to noises in mammogram image. Therefore, based on the frequency domain this thesis employed wavelet transform to enhance mammogram images in pre-processing stage for two purposes which is to highlight the border of mammogram images for segmentation stage, and to enhance the region of interest (ROI) using adaptive threshold in the mammogram images for feature extraction purpose. Second stage is segmentation process to identify ROI in mammogram images. It is a difficult task because of several landmarks such as breast boundary and artifacts as well as pectoral muscle in Medio-Lateral Oblique (MLO). Thus, this thesis presents an automatic segmentation algorithm based on new thresholding combined with image processing techniques. Experimental results demonstrate that the proposed model increases segmentation accuracy of the ROI from breast background, landmarks, and pectoral muscle. Third stage is feature extraction where enhancement model based on fractal dimension is proposed to derive significant mammogram image texture features. Based on the proposed, model a powerful texture sign for classification are extracted. Fourth stage is feature selection where Genetic Algorithm (GA) technique has been used as a feature selection technique to select the important features. In last classification stage, Artificial Neural Network (ANN) technique has been used to differentiate between Benign and Malignant classes of cancer using the most relevant texture feature. As a conclusion, classification accuracy, sensitivity, and specificity obtained by the proposed CAD system are improved in comparison to previous studies. This thesis has practical contribution in identification of breast cancer using mammogram images and better classification accuracy of benign and malign lesions using ML and image processing techniques

A non-invasive image based system for early diagnosis of prostate cancer.

Prostate cancer is the second most fatal cancer experienced by American males. The average American male has a 16.15% chance of developing prostate cancer, which is 8.38% higher than lung cancer, the second most likely cancer. The current in-vitro techniques that are based on analyzing a patients blood and urine have several limitations concerning their accuracy. In addition, the prostate Specific Antigen (PSA) blood-based test, has a high chance of false positive diagnosis, ranging from 28%-58%. Yet, biopsy remains the gold standard for the assessment of prostate cancer, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The major limitation of the relatively small needle biopsy samples is the higher possibility of producing false positive diagnosis. Moreover, the visual inspection system (e.g., Gleason grading system) is not quantitative technique and different observers may classify a sample differently, leading to discrepancies in the diagnosis. As reported in the literature that the early detection of prostate cancer is a crucial step for decreasing prostate cancer related deaths. Thus, there is an urgent need for developing objective, non-invasive image based technology for early detection of prostate cancer. The objective of this dissertation is to develop a computer vision methodology, later translated into a clinically usable software tool, which can improve sensitivity and specificity of early prostate cancer diagnosis based on the well-known hypothesis that malignant tumors are will connected with the blood vessels than the benign tumors. Therefore, using either Diffusion Weighted Magnetic Resonance imaging (DW-MRI) or Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), we will be able to interrelate the amount of blood in the detected prostate tumors by estimating either the Apparent Diffusion Coefficient (ADC) in the prostate with the malignancy of the prostate tumor or perfusion parameters. We intend to validate this hypothesis by demonstrating that automatic segmentation of the prostate from either DW-MRI or DCE-MRI after handling its local motion, provides discriminatory features for early prostate cancer diagnosis. The proposed CAD system consists of three majors components, the first two of which constitute new research contributions to a challenging computer vision problem. The three main components are: (1) A novel Shape-based segmentation approach to segment the prostate from either low contrast DW-MRI or DCE-MRI data; (2) A novel iso-contours-based non-rigid registration approach to ensure that we have voxel-on-voxel matches of all data which may be more difficult due to gross patient motion, transmitted respiratory effects, and intrinsic and transmitted pulsatile effects; and (3) Probabilistic models for the estimated diffusion and perfusion features for both malignant and benign tumors. Our results showed a 98% classification accuracy using Leave-One-Subject-Out (LOSO) approach based on the estimated ADC for 30 patients (12 patients diagnosed as malignant; 18 diagnosed as benign). These results show the promise of the proposed image-based diagnostic technique as a supplement to current technologies for diagnosing prostate cancer

SURVIVAL AND SUBSET CLASSIFICATION ANALYSIS OF 82 PATIENTS WITH INFLAMMATORY MYOPATHY

Background: Idiopathic inflammatory myopathies (IIM) are characterised by chronic muscle inflammation, various organ involvements and the presence of certain specific autoantibodies. Objectives: We assessed survival and characterized subsets based on muscle biopsy and myositis specific autoantibodies (MSAs). Methods: Eighty-two patients with muscle biopsy proven IIM were included in the study. All cases had MSA and myositis associated antibody (MAA) tests (Jo-1, PL-7, PL-12, Mi-2, SRP, Pm-Scl, Ku, ribosomal, AMA-M2) using Western-blot kits. Survival analysis was performed by Kaplan Meier test. Results: Fift y-nine women and 23 men with a mean age of 49.3 ± 14.6 years and with 7.5 ± 4.5 years of mean follow-up time were included. Interstitial lung disease (ILD) (51.2%), arthritis (51.2%), Raynaud’s phenomenon (42.7%), skin symptoms (45.1%), dysphagia (24.4%) and significant cardiac involvement (15.9%) were the most prevalent disease manifestations. 15 cases were associated with malignancies. Myositis subsets were as follow: 26.8% (n=22) polymyositis /PM/, 30.5% (n=25) dermatomyositis/DM/, 1.2% (n=1) juvenile PM/DM, 8.5% (n=7) inclusion body myositis /IBM/, 22% (n=18) overlap myositis /OM/, and 11% (n=9) immune mediated necrotizing myopathy /IMNM/. Malignancy was most frequently associated with IMNM (7 out of 9 patients). Altogether 18 patients died from which 15 deaths can be connected to myositis related events. Eight patients died of malignancies, 5 patients due to cardiac events (heart failure, arrythmia), 2 due to lung fibrosis and 3 by unknown causes. The worst prognosis with a 10-year survival of 31 % was in the IMNM subgroup (p<0.01), followed by patients with PM (68%), IBM (84%) OM (85.1%) and DM (85.3%). Mi-2 positive patients had a favourable prognosis with a 10-year survival of 100%. Patients with IMNM had the worst prognosis (10-year survival of 31.1%), followed by PM (76%), DM and IBM (85.7% each). Patients with antisynthetase antibody-positivity had worse prognosis compared to patients with other antibodies or no identifiable antibodies (10-year survival of 55%, n=16) (p<0.05). Conclusions: Th e worst survivals were seen in the IMNM and PM groups, due to the high frequency of the underlying malignancies and cardiac manifestations. Although ILD was the most frequent involvement, it was not the main cause of death

MRI IN EVALUATION OF INFLAMMATORY MYOPATHY

Background: MRI of skeletal muscles has been widely used to assess several types of myopathies, including inherited and acquired muscle diseases. Objectives: To describe current possibilities in use of MRI in diagnostics and assessment of idiopathic inflammatory myopathies (IIMs). Methods: T2 weighted images with fat suppression (T2W/FS) or short tau inversion recovery (STIR) sequence with long time to echo (TE) and T1 weighted images were used to evaluate inflammatory changes (STIR) and muscle atrophy or fat substitution (T1). Simple scoring system was used for correlative studies with histopathological changes. New and more elaborate system for scoring of MR scans was developed and used to evaluate longitudinal images during the therapeutic study. Results: Muscle biopsy guided by positive MRI finding contains significantly more inflammatory cells than the biopsy taken from MRI identified non-affected sites. However, even in parts of muscles, which look unaffected on MR scan, important numbers of the inflammatory cells can be found. It is mainly the signal intensity in MR scan, which is associated with disease activity in the acute presentation of IIMs. Longitudinal follow-up of patients with IIMs showed significant reduction of signal intensity in number of muscles when using new detailed scoring method. Conclusions: Muscle MRI is a useful method to guide the biopsy site in IIMs. Scoring system that uses semiquantitative assessment of individual muscles is sensitive for evaluation of improvement during the treatment. No universal scoring method has been validated and accepted so far for evaluation of inflammation and atrophic changes during IIMs. Developmnet of standard recommendations for muscle MRI assessment in IIMs is very much needed. References: 1. Tomasová Studýnková J, et al. Rheumatology (Oxford) 2007,46:1174–79. 2. Kubínová K, et al. Curr Opin Rheumatol 2017;29:623–31. 3. Kubínová K, et al. Clin Exp Rheumatol 2018;36 Suppl 114(5):74–81

ULTRASOUND EVALUATION OF THE ANKLE JOINTS AND TENDONS IN SYSTEMIC LUPUS ERYTHEMATOSUS

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with musculoskeletal involvement as one of the most common and earliest clinical manifestations which occur in 95% of patients. High-resolution ultrasound (US) already proved to be a useful diagnostic tool for the evaluation of pathological changes of the joints and tendons in the majority of inflammatory rheumatic diseases. There are no studies that evaluate the frequency of involvement of ankle joints in adult patients with SLE. Objectives: The aim of this study is to asses the frequency of ankle joints and tendons involvement in SLE patients using US and correlate the findings with physical examination, laboratory tests, and disease activity scores. Here we will show preliminary results of the survey in the first 10 out of 60 included patients. Methods: Ten consecutive SLE patients were enrolled in the study and underwent clinical evaluation, laboratory tests and bilateral high-resolution US on the same day. Gray-scale and power Doppler (PD) US were performed for imaging the talocrural (TC), subtalar joints (ST) and ankle tendons, then second and third MCP joints, second and third PIP joints, wrists and second and third MTP joints. Ankle inflammatory US score and global inflammatory US score were calculated. Results: Preliminary results in 10 patients show the US detected inflammatory joint abnormalities in 7/10 (70%) patients and tendon involvement in 1/10 (10%). A total of 180 joints and 200 tendons were examined. Both of MTP and TC joints were affected in 60% patients, MCP joints in 50%, ST in 40%, wrists in 30% and PIP joints in 10% of patients. Th e most prevalent pathological US finding was joint effusion, less frequently synovial hypertrophy while positive PD signal was rarely presented. Only one patient had bony erosion detected. Effusion in TC joints was present in 60% patients, synovial hypertrophy in 40% and positive PD in 10%. As many as 62,5% of patients without inflammatory joint symptoms had pathological US findings in ankle joints. Th e global US inflammatory score had a mean value of 5,6, and ankle US inflammatory mean value score 2,9. Conclusions: Results of the preliminary study show a high prevalence of US verified inflammatory joint changes in SLE patients. Surprisingly, the foot and ankle joints were most commonly affected and a great number of asymptomatic patients had pathological US findings in ankle joints. References: 1. Iagnocco A, Ceccarelli F, Rizzo C, Truglia S, Massaro L, Spinelli FR, et al. Ultrasound evaluation of hand, wrist and foot joint synovitis in systemic lupus erythematosus. Rheumatology. 2014;53(3):465–72. 2. Delle Sedie A, Riente L, Scire CA, Iagnocco A, Filippucci E, Meenagh G, et al. Ultrasound imaging for the rheumatologist XXIV. Sonographic evaluation of wrist and hand joint and tendon involvement in systemic lupus erythematosus. Clin Exp Rheumatol. 2009;27(6):897–901. 3. Iagnocco A, Epis O, Delle Sedie A, Meenagh G, Filippucci E, Riente L, et al. Ultrasound imaging for the rheumatologist. XVII. Role of colour Doppler and power Doppler. Clin Exp Rheumatol. 2008 Oct;26(5):759–62. 4. Porta F, Radunovic G, Vlad V, Micu MC, Nestorova R, Petranova T, et al. Th e role of Doppler ultrasound in rheumatic diseases. Rheumatology. 2012;51(6):976–82