Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas

Directional Dense-Trajectory-based Patterns for Dynamic Texture Recognition

International audienceRepresentation of dynamic textures (DTs), well-known as a sequence of moving textures, is a challenging problem in video analysis due to disorientation of motion features. Analyzing DTs to make them "under-standable" plays an important role in different applications of computer vision. In this paper, an efficient approach for DT description is proposed by addressing the following novel concepts. First, beneficial properties of dense trajectories are exploited for the first time to efficiently describe DTs instead of the whole video. Second, two substantial extensions of Local Vector Pattern operator are introduced to form a completed model which is based on complemented components to enhance its performance in encoding directional features of motion points in a trajectory. Finally, we present a new framework, called Directional Dense Trajectory Patterns , which takes advantage of directional beams of dense trajectories along with spatio-temporal features of their motion points in order to construct dense-trajectory-based descriptors with more robustness. Evaluations of DT recognition on different benchmark datasets (i.e., UCLA, DynTex, and DynTex++) have verified the interest of our proposal

Volumes of Blurred-Invariant Gaussians for Dynamic Texture Classification

International audienceAn effective model, which jointly captures shape and motion cues, for dynamic texture (DT) description is introduced by taking into account advantages of volumes of blurred-invariant features in three main following stages. First, a 3-dimensional Gaussian kernel is used to form smoothed sequences that allow to deal with well-known limitations of local encoding such as near uniform regions and sensitivity to noise. Second , a receptive volume of the Difference of Gaussians (DoG) is figured out to mitigate the negative impacts of environmental and illumination changes which are major challenges in DT understanding. Finally, a local encoding operator is addressed to construct a discriminative descriptor of enhancing patterns extracted from the filtered volumes. Evaluations on benchmark datasets (i.e., UCLA, DynTex, and DynTex++) for issue of DT classification have positively validated our crucial contributions

Geographic uncertainties in external exposome studies: A multi-scale approach to reduce exposure misclassification

Background: Many studies on environment-health associations have emphasized that the selected buffer size (i.e., the scale of the geographic context when exposures are assigned at people's address location) may affect estimated effect sizes. However, there is limited methodological progress in addressing these buffer size-related uncertainties. Aim: We aimed to 1) develop a statistical multi-scale approach to address buffer-related scale effects in cohort studies, and 2) investigate how environment-health associations differ between our multi-scale approach and ad hoc selected buffer sizes. Methods: We used lacunarity analyses to determine the largest meaningful buffer size for multiple high-resolution exposure surfaces (i.e., fine particulate matter [PM2.5], noise, and the normalized difference vegetation index [NDVI]). Exposures were linked to 7.7 million Dutch adults at their home addresses. We assigned exposure estimates based on buffers with fine-grained distance increments until the lacunarity-based upper limit was reached. Bayesian Cox model averaging addressed geographic uncertainties in the estimated exposure effect sizes within the exposure-specific upper buffer limits on mortality. Z-tests assessed statistical differences between averaged effect sizes and those obtained through pre-selected 100, 300, 1200, and 1500 m buffers. Results: The estimated lacunarity curves suggested exposure-specific upper buffer size limits; the largest was for NDVI (960 m), followed by noise (910 m) and PM2.5 (450 m). We recorded 845,229 deaths over eight years of follow-up. Our multi-scale approach indicated that higher values of NDVI were health-protectively associated with mortality risk (hazard ratio [HR]: 0.917, 95 % confidence interval [CI]: 0.886–0.948). Increased noise exposure was associated with an increased risk of mortality (HR: 1.003, 95 % CI: 1.002–1.003), while PM2.5 showed null associations (HR:0.998, 95 % CI: 0.997–1.000). Effect sizes of NDVI and noise differed significantly across the averaged and prespecified buffers (p < 0.05). Conclusions: Geographic uncertainties in residential-based exposure assessments may obscure environment-health associations or risk spurious ones. Our multi-scale approach produced more consistent effect estimates and mitigated contextual uncertainties

Cityscape, poverty and crime: a quantitative assessment using VHR imagery

[EN] The first part of this work reviews the potential applications of satellite remote sensing to regional science research in urban settings. The availability of satellite remote sensing data has increased significantly in the last two decades. The increasing spatial resolution of commercial satellite imagery has influenced the emergence of new research and applications of regional science in urban settlements because it is now possible to identify individual objects of the urban fabric. The most common applications found in the literature are the detection of urban deprivation hot spots, quality of life index assessment, urban growth analysis, house value estimation, urban population estimation, urban social vulnerability assessment, and the variability of intra-urban crime rates. The satellite remote sensing imagery used in these applications has medium, high or very high spatial resolution (Landsat MSS, Landsat TM and ETM+, SPOT, ASTER, IRS, Ikonos and QuickBird). Consistent relationships between socio-economic variables derived from censuses and field surveys and proxy variables of vegetation coverage measured from satellite remote sensing data have been found in several cities in the US. Different approaches and techniques have been applied successfully around the world, but local research is always needed to account for the unique elements of each place. Spectral mixture analysis, object-oriented classifications and image texture measures are some of the techniques of image processing that have been implemented with good results. This work contributes empirical evidence about the usefulness of remote sensing imagery to quantify the degree of poverty at the intra-urban scale. This concept is based on two premises: first, that the physical appearance of an urban settlement is a reflection of the society; and second, that the people who reside in urban areas with similar physical housing conditions have similar social and demo- graphic characteristics. We evaluate the potential of the image-derived urban fabric descriptors to explain a measure of poverty known as the Slum Index. We found that these variables explain up to 59% of the variability in the Slum Index. Similar approaches could be used to lower the cost of socioeconomic surveys by developing an econometric model from a sample and applying that model to the rest of the city and to perform intercensal or intersurvey estimates of intra-urban Slum Index maps. The last part of this work analyzes the relation between the urban layout and crime. The link between place and crime is at the base of social ecology theories of crime that focus in the relationship of the characteristics of geographical areas and crime rates. The broken windows theory states that visible cues of physical and social disorder in a neighborhood can lead to an increase in more serious crime. Based on the premise that a settlement's appearance is a reflection of the society, we ask whether a neighbor- hood's design has a quantifiable imprint when seen from space using urban fabric descriptors computed from VHR imagery. The percentage of impervious surfaces other than clay roofs, the fraction of clay roofs to impervious surfaces, two structure descriptors related to the homogeneity of the urban layout, and the uniformity texture descriptor were all statistically significant. Areas with higher homicide rates tended to have higher local variation and less general homogeneity; that is, the urban layouts were more crowded and cluttered, with small dwellings with different roofing materials located in close proximity to one another, and these regions often lacked other homogeneous surfaces such as open green spaces, wide roads, or large facilities. These results seem to be in agreement with the broken windows theory and CPTED in the sense that more heterogeneous and disordered urban layouts are associated with higher homicide rates.[ES] La primera parte aporta una revisión de las aplicaciones de la teledetección satelital en la investigación de ciencia regional en entornos urbanos. La disponibilidad de imágenes satelitales se ha incrementado significativamente en las dos últimas décadas, al tiempo que la resolución espacial ha venido aumentando, lo que ha influenciado el surgimiento de investigaciones y aplicaciones de ciencia regional en zonas urbanas. Las aplicaciones más comunes son la detección de hot spots de pobreza urbana, la evaluación de índices de calidad de vida, el análisis del crecimiento urbano, la estimación de valores de vivienda, la estimación de población urbana, la evaluación de la vulnerabilidad social y las variaciones intra-urbanas en tasas de crimen. Las imágenes satelitales usadas tienen resolución espacial media, alta o muy alta (Landsat MSS, Landsat TM y ETM+, SPOT, ASTER, IRS, Ikonos y Quickbird). Se han encontrado relaciones consistentes entre variables socio-económicas obtenidas de censos y encuestas y variables de la cobertura de vegetación en varias ciudades de Estados Unidos. Algunas de las técnicas que se han implementado y obtenido buenos resultados son el análisis de mezcla espectral, las clasificaciones orientadas a objetos y las medidas de textura de la imagen. Se aporta evidencia empírica acerca de la utilidad de las imágenes satelitales para cuantificar el grado de pobreza a escala intra-urbana. Se basa en dos premisas: primero, que la apariencia física de un asentamiento urbano es un reflejo de la sociedad que lo habita; y segundo, que la población de áreas urbanas con condiciones físicas de vivienda parecidas tiene características sociales y demográficas similares. Evaluamos el potencial de los descriptores del tejido urbano extraídos de la imagen para explicar una medida de pobreza conocida como el índice Slum. Encontramos que esas variables explican hasta un 59% de la variabilidad en el índice Slum. Aproximaciones similares a esta podrían usarse para disminuir el costo de encuestas socioeconómicas por medio del desarrollo de un modelo econométrico usando una muestra y luego aplicando el modelo al resto de la ciudad, y para elaborar estimaciones inter-censales o inter-encuestas de mapas intra-urbanos del índice Slum. La última parte analiza la relación entre el trazado urbano y crimen. El enlace entre el lugar y el crimen está en la base de las teorías socio-ecológicas de crimen que se enfocan en la relación de las características de las áreas geográficas y las tasas de crimen. La teoría de las ventanas rotas afirma que las evidencias visibles de desorden físico y social en un barrio pueden llevar al incremento de crímenes más serios. Con base en la premisa de que la apariencia de un asentamiento es un reflejo de la sociedad, nos preguntamos si el diseño del barrio tiene un impacto cuantificable cuando se observa desde el espacio usando descriptores del tejido urbano obtenidos de imágenes de muy alta resolución. El porcentaje de superficies impermeables diferentes a los techos de arcilla, la fracción de techos de arcilla sobre las superficies impermeables, dos variables de estructura relacionadas con la homogeneidad del trazado urbano y la variable de textura uniformidad resultaron estadísticamente significativas. Las áreas con tasas de homicidio más altas tienden a tener mayor variación local y menor homogeneidad general; esto es, los trazados urbanos son más desordenados y hacinados, con pequeñas viviendas que tienen materiales diferentes en sus techos localizadas muy cerca unas de otras, y estas áreas carecen a menudo de otras superficies homogéneas tales como espacios verdes abiertos, vías amplias y grandes construcciones industriales o institucionales. Estos resultados parecen estar en acuerdo con la teoría de las ventanas rotas y CPTED en el sentido de que los trazados urbanos más desordenados y heterogéneos están asociados con tasas de homicid[CA] La primera part aporta una revisió de les potencials aplicacions de la teledetecció espacial a la investigació en ciència regional en entorns urbans. La disponibilitat de dades de percepció remota des de satèl·lits s'ha incrementat significativament a les dues últimes dècades. La resolució espacial de les imatges de satèl·lit comercials també han anat augmentant i això, ha influït en l'aparició de investigacions i aplicacions a la ciència regional en assentaments urbans. Les aplicacions més comunes trobades a la literatura són la detecció de punts calents de pobresa urbana, l'avaluació dels índex de qualitat de vida, les anàlisis de creixement urbà, l'avaluació de la vulnerabilitat social i les variacions intraurbanes de les taxes de crims. Les imatges de satèl·lit emprades tenen resolució espacial mitjana, alta o molt alta (Landsat MSS, Landsat TM i ETM+, SPOT, ASTER, IRS, Ikonos y Quickbird). S'han torbat relacions consistents entre variables socioeconòmiques obtingudes de censos i enquestes i variables de la cobertura de vegetació en varies ciutats del Estats Units. Algunes de les tècniques que s'han implementat i han donat bons resultats són l'anàlisi de mescla espectral, les classificacions orientades a objecte i les mesures de textura de les imatges. Es aporta evidència empírica sobre la utilitat de les imatges de satèl·lit per quantificar el grau de pobresa a escala intraurbana. Es bassa en dues premisses: primer, que l'aparença física d'un assentament urbà n'és un reflex de la societat que l'habita; i segon, que les persones que resideixen en àrees urbanes amb condicions físiques de vivenda paregudes tenen també característiques socials i demogràfiques similars. Avaluem el potencial dels descriptors del teixit urbà extrets de la imatge per explicar una mesura de pobresa coneguda com index Slum. Trobem que aquestes variables expliquen fins un 59% de la variabilitat de l'índex Slum. Aproximacions semblants a aquesta es podrien emprar per a disminuir el cost de les enquestes socioeconòmiques mitjançant el desenvolupament d'un model economètric utilitzant una mostra i després aplicant el model a la resta de la ciutat, i per elaborar estimacions inter-censals o inter-enquestes de mapes intraurbans de l'índex Slum. La darrera part analitza la relació entre el traçat urbà i el crim. L'enllaç entre el lloc i el crim està a la base de les teories socio-ecològiques del crim que es centren en la relació de les característiques de les àrees geogràfiques i les taxes de crims. La teoria de les finestres trencades afirma que les evidències visibles de desordre físic i social d'un barri pot portar a l'augment de crims més greus. Basant-se en la premissa de que l'aparença d'un assentament n'és el reflex de la societat, ens hi preguntem si el disseny del barri té un impacte quantificable quan s'observa des de el espai, utilitzant descriptors del teixit urbà obtinguts de imatges de molt alta resolució. Han resultat estadísticament significatius el percentatge de superfícies impermeables diferents a les teulades de argila, la fracció de teulades d'argila sobre les superfícies impermeables, dues variables d'estructura relacionades amb la homogeneïtat del traçat urbà i la variable de textura de uniformitat. Les àrees amb taxes d'homicidi més altes tendeixen a presentar una major variació local i una menor homogeneïtat general; és a dir, el traçats urbans són més desordenats i amuntonats, amb petites vivendes que tenen materials diferents a les seues teulades localitzades molt prop unes d'altres, i aquestes àrees manquen sovint d'altres superfícies homogènies, com ara espais verds oberts, vies amplies i grans construccions industrials o institucionals. Aquests resultats pareixen estar-hi d'acord amb la teoria de les finestres trencades i CPTED en el sentit de que els traçats urbans més desordenats i heterogenis estan associats amb taxes d'homicides mPatiño Quinchía, JE. (2015). Cityscape, poverty and crime: a quantitative assessment using VHR imagery [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59453TESI

Machine Learning of Scientific Events: Classification, Detection, and Verification

Classification and segmentation of objects using machine learning algorithms have been widely used in a large variety of scientific domains in the past few decades. With the exponential growth in the number of ground-based, air-borne, and space-borne observatories, Heliophysics has been taking full advantage of such algorithms in many automated tasks, and obtained valuable knowledge by detecting solar events and analyzing the big-picture patterns. Despite the fact that in many cases, the strengths of the general-purpose algorithms seem to be transferable to problems of scientific domains where scientific events are of interest, in practice there are some critical issues which I address in this dissertation. First, I discuss the four main categories of such issues and then in the proceeding chapters I present real-world examples and the different approaches I take for tackling them. In Chapter II, I take a classical path for classification of three solar events; Active Regions, Coronal Holes, and Quiet Suns. I optimize a set of ten image parameters and improve the classification performance by up to 36%. In Chapter III, in contrast, I utilize an automated feature extraction algorithm, i.e., a deep neural network, for detection and segmentation of another solar event, namely solar Filaments. Using an off-the-shelf algorithm, I overcome several of the issues of the existing detection module, while facing an important challenge; lack of an appropriate evaluation metric for verification of the segmentations. In Chapter IV, I introduce a novel metric to provide a more accurate verification especially for salient objects with fine structures. This metric, called Multi-Scale Intersection over Union (MIoU), is a fusion of two concepts; fractal dimension from Geometry, and Intersection over Union (IoU) which is a popular metric for segmentation verification. Through several experiments I examine the advantages of using MIoU over IoU, and I conclude this chapter by a follow-through on the segmentation results of the previously implemented filament detection module

Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique

In this paper, we present a novel feature extraction technique, termed Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH), and exploit it to detect breast cancer in volumetric medical images. The technique is incorporated as part of an intelligent expert system that can aid medical practitioners making diagnostic decisions. Analysis of volumetric images, slice by slice, is cumbersome and inefficient. Hence, 3D-LESH is designed to compute a histogram-based feature set from a local energy map, calculated using a phase congruency (PC) measure of volumetric Magnetic Resonance Imaging (MRI) scans in 3D space. 3D-LESH features are invariant to contrast intensity variations within different slices of the MRI scan and are thus suitable for medical image analysis.The contribution of this article is manifold. First, we formulate a novel 3D-LESH feature extraction technique for 3D medical images to analyse volumetric images. Further, the proposed 3D-LESH algorithmis, for the first time, applied to medical MRI images. The final contribution is the design of an intelligent clinical decision support system (CDSS) as a multi-stage approach, combining novel 3D-LESH feature extraction with machine learning classifiers, to detect cancer from breast MRI scans. The proposed system applies contrast-limited adaptive histogram equalisation (CLAHE) to the MRI images before extracting 3D-LESH features. Furthermore, a selected subset of these features is fed into a machine-learning classifier, namely, a support vector machine (SVM), an extreme learning machine (ELM) or an echo state network (ESN) classifier, to detect abnormalities and distinguish between different stages of abnormality. We demonstrate the performance of the proposed technique by its application to benchmark breast cancer MRI images. The results indicate high-performance accuracy of the proposed system (98%±0.0050, with an area under a receiver operating charactertistic curve value of 0.9900 ± 0.0050) with multiple classifiers. When compared with the state-of-the-art wavelet-based feature extraction technique, statistical analysis provides conclusive evidence of the significance of our proposed 3D-LESH algorithm

Computing Local Fractal Dimension Using Geographical Weighting Scheme

The fractal dimension (D) of a surface can be viewed as a summary or average statistic for characterizing the geometric complexity of that surface. The D values are useful for measuring the geometric complexity of various land cover types. Existing fractal methods only calculate a single D value for representing the whole surface. However, the geometric complexity of a surface varies across patches and a single D value is insufficient to capture these detailed variations. Previous studies have calculated local D values using a moving window technique. The main purpose of this study is to compute local D values using an alternative way by incorporating the geographical weighting scheme within the original global fractal methods. Three original fractal methods are selected in this study: the Triangular Prism method, the Differential Box Counting method and the Fourier Power Spectral Density method. A Gaussian density kernel function is used for the local adaption purpose and various bandwidths are tested. The first part of this dissertation research explores and compares both of the global and local D values of these three methods using test images. The D value is computed for every single pixel across the image to show the surface complexity variation. In the second part of the dissertation, the main goal is to study two major U.S. cities located in two regions. New York City and Houston are compared using D values for both of spatial and temporal comparison. The results show that the geographical weighting scheme is suitable for calculating local D values but very sensitive to small bandwidths. New York City and Houston show similar global D results for both year of 2000 and 2016 indicating there were not much land cover changes during the study period

How Does Wildfire Risk Differ Across a Landscape Given Heterogeneous Development Patterns?

This study explores the variation in wildfire risk from different development patterns. The analysis tests how an assortment of variables within the fire risk framework are affected by differing lateral development types. The study area covered Bastrop and Travis counties located in Texas. Two time periods were used in the assessment, 2001 and 2012. Lateral development was categorized into five categories: infill, radial, isolated, clustered, and linear. Within the fire risk framework, fire severity, ignition probability, and burn probability were assessed for the study area. Maximum Entropy was used to spatially predict ignition probability. Burn probability and conditional flame length were simulated using the Minimum Travel Time algorithm. Ignition probability variation was assessed using a one-way ANOVA and post hoc analysis. Burn probability and conditional flame length analyses were more robust. One-way ANOVAs and post hoc analyses were used to differentiate variation among lateral development types. Generalized Methods of Moments were used to estimate changes in burn probability and conditional flame length across time. Finally, the simulation’s fire perimeters were analyzed for initiation and exposure using social network analysis techniques. Analyses found that outlying development patterns: isolated, clustered, linear, were at higher wild fire risk than infill and radial development. However, most simulated fires initiated nearest radial development. Being closer to a road increased the likelihood of ignition, but increases in road density decreased burn probability. Changes in fuel loading had a positive correlation with changes in conditional flame length and burn probability. The analysis suggests that increasing populations in the wildland-urban interface are increasing their risk. Policies that reduce the outlying development patterns will reduce risk for the community