QUAD FLAT NO-LEAD (QFN) DEVICE FAULTY DETECTION USING GABOR WAVELETS

Computer vision inspection system using image processing algorithms have been utilized by many manufacturing companies as a method of quality control. Since manufacturing industries comprise of many types of products, various image processing algorithms have been developed to suit different type of outputting products. In this paper, we explored Gabor wavelet feature extraction as a method for vision inspection. Unlike conventional vision inspection system which require manual human configuration of inspection algorithms, our experiment uses Gabor wavelets to fractionate the image into distinctive scales and orientations. Through chi-square distance computation, the physical quality of Quad Flan No-Lead (QFN) device can be distinguished by computing the dissimilarity of the test image with the trained database, thus eliminating the weakness of human errors in configuration of vision systems. We performed our algorithm testing using 64 real-world production images obtained from a 0.3 megapixel monochromatic industrial smart vision camera. The images consists a mixture of physically good and defected QFN units. The proposed algorithm achieved 98.46% accuracy rate with the average processing time of 0.457 seconds per image

Statistical anatomical modelling for efficient and personalised spine biomechanical models

Personalised medicine is redefining the present and future of healthcare by increasing treatment efficacy and predicting diseases before they actually manifest. This innovative approach takes into consideration patient’s unique genes, environment, and lifestyle. An essential component is physics-based simulations, which allows the outcome of a treatment or a disease to be replicated and visualised using a computer. The main requirement to perform this type of simulation is to build patient-specific models. These models require the extraction of realistic object geometries from images, as well as the detection of diseases or deformities to improve the estimation of the material properties of the studied object. The aim of this thesis was the design of a general framework for creating patient- specific models for biomechanical simulations using a framework based on statistical shape models. The proposed methodology was tested on the construction of spine models, including vertebrae and intervertebral discs (IVD). The proposed framework is divided into three well-defined components: The paramount and first step is the extraction of the organ or anatomical structure from medical images. In the case of the spine, IVDs and vertebrae were extracted from Magnetic Resonance images (MRI) and Computed Tomography (CT), respectively. The second step is the classification of objects according to different factors, for instance, bones by its type and grade of fracture or IVDs by its degree of degeneration. This process is essential to properly model material properties, which depends on the possible pathologies of the tissue. The last component of the framework is the creation of the patient-specific model itself by combining the information from previous steps. The behaviour of the developed algorithms was tested using different datasets of spine images from both computed tomography (CT) and Magnetic resonance (MR) images from different institutions, type of population and image resolution

Modelling the head and neck region for microwave imaging of cervical lymph nodes

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia), Universidade de Lisboa, Faculdade de Ciências, 2020O termo “cancro da cabeça e pescoço” refere-se a um qualquer tipo de cancro com início nas células epiteliais das cavidades oral e nasal, seios perinasais, glândulas salivares, faringe e laringe. Estes tumores malignos apresentaram, em 2018, uma incidência mundial de cerca de 887.659 novos casos e taxa de mortalidade superior a 51%. Aproximadamente 80% dos novos casos diagnosticados nesse ano revelaram a proliferação de células cancerígenas dos tumores para outras regiões do corpo através dos vasos sanguíneos e linfáticos das redondezas. De forma a determinar o estado de desenvolvimento do cancro e as terapias a serem seguidas, é fundamental a avaliação dos primeiros gânglios linfáticos que recebem a drenagem do tumor primário – os gânglios sentinela – e que, por isso, apresentam maior probabilidade de se tornarem os primeiros alvos das células tumorais. Gânglios sentinela saudáveis implicam uma menor probabilidade de surgirem metástases, isto é, novos focos tumorais decorrentes da disseminação do cancro para outros órgãos. O procedimento standard que permite o diagnóstico dos gânglios linfáticos cervicais, gânglios que se encontram na região da cabeça e pescoço, e o estadiamento do cancro consiste na remoção cirúrgica destes gânglios e subsequente histopatologia. Para além de ser um procedimento invasivo, a excisão cirúrgica dos gânglios linfáticos representa perigos tanto para a saúde mental e física dos pacientes, como para a sua qualidade de vida. Dores, aparência física deformada (devido a cicatrizes), perda da fala ou da capacidade de deglutição são algumas das repercussões que poderão advir da remoção de gânglios linfáticos da região da cabeça e pescoço. Adicionalmente, o risco de infeção e linfedema – acumulação de linfa nos tecidos intersticiais – aumenta significativamente com a remoção de uma grande quantidade de gânglios linfáticos saudáveis. Também os encargos para os sistemas de saúde são elevados devido à necessidade de monitorização destes pacientes e subsequentes terapias e cuidados associados à morbilidade, como é o caso da drenagem linfática manual e da fisioterapia. O desenvolvimento de novas tecnologias de imagem da cabeça e pescoço requer o uso de modelos realistas que simulem o comportamento e propriedades dos tecidos biológicos. A imagem médica por micro-ondas é uma técnica promissora e não invasiva que utiliza radiação não ionizante, isto é, sinais com frequências na gama das micro-ondas cujo comportamento depende do contraste dielétrico entre os diferentes tecidos atravessados, pelo que é possível identificar regiões ou estruturas de interesse e, consequentemente, complementar o diagnóstico. No entanto, devido às suas características, este tipo de modalidade apenas poderá ser utilizado para a avaliação de regiões anatómicas pouco profundas. Estudos indicam que os gânglios linfáticos com células tumorais possuem propriedades dielétricas distintas dos gânglios linfáticos saudáveis. Por esta razão e juntamente pelo facto da sua localização pouco profunda, consideramos que os gânglios linfáticos da região da cabeça e pescoço constituem um excelente candidato para a utilização de imagem médica por radar na frequência das micro-ondas como ferramenta de diagnóstico. Até à data, não foram efetuados estudos de desenvolvimento de modelos da região da cabeça e pescoço focados em representar realisticamente os gânglios linfáticos cervicais. Por este motivo, este projeto consistiu no desenvolvimento de dois geradores de fantomas tridimensionais da região da cabeça e pescoço – um gerador de fantomas numéricos simples (gerador I) e um gerador de fantomas numéricos mais complexos e anatomicamente realistas, que foi derivado de imagens de ressonância magnética e que inclui as propriedades dielétricas realistas dos tecidos biológicos (gerador II). Ambos os geradores permitem obter fantomas com diferentes níveis de complexidade e assim acompanhar diferentes fases no processo de desenvolvimento de equipamentos médicos de imagiologia por micro-ondas. Todos os fantomas gerados, e principalmente os fantomas anatomicamente realistas, poderão ser mais tarde impressos a três dimensões. O processo de construção do gerador I compreendeu a modelação da região da cabeça e pescoço em concordância com a anatomia humana e distribuição dos principais tecidos, e a criação de uma interface para a personalização dos modelos (por exemplo, a inclusão ou remoção de alguns tecidos é dependente do propósito para o qual cada modelo é gerado). O estudo minucioso desta região levou à inclusão de tecidos ósseos, musculares e adiposos, pele e gânglios linfáticos nos modelos. Apesar destes fantomas serem bastante simples, são essenciais para o início do processo de desenvolvimento de dispositivos de imagem médica por micro-ondas dedicados ao diagnóstico dos gânglios linfáticos cervicais. O processo de construção do gerador II foi fracionado em 3 grandes etapas devido ao seu elevado grau de complexidade. A primeira etapa consistiu na criação de uma pipeline que permitiu o processamento das imagens de ressonância magnética. Esta pipeline incluiu: a normalização dos dados, a subtração do background com recurso a máscaras binárias manualmente construídas, o tratamento das imagens através do uso de filtros lineares (como por exemplo, filtros passa-baixo ideal, Gaussiano e Butterworth) e não-lineares (por exemplo, o filtro mediana), e o uso de algoritmos não supervisionados de machine learning para a segmentação dos vários tecidos biológicos presentes na região cervical, tais como o K-means, Agglomerative Hierarchical Clustering, DBSCAN e BIRCH. Visto que cada algoritmo não supervisionado de machine learning anteriormente referido requer diferentes hiperparâmetros, é necessário proceder a um estudo pormenorizado que permita a compreensão do modo de funcionamento de cada algoritmo individualmente e a sua interação / performance com o tipo de dados tratados neste projeto (isto é, dados de exames de ressonâncias magnéticas) com vista a escolher empiricamente o leque de valores de cada hiperparâmetro que deve ser considerado, e ainda as combinações que devem ser testadas. Após esta fase, segue-se a avaliação da combinação de hiperparâmetros que resulta na melhor segmentação das estruturas anatómicas. Para esta avaliação são consideradas duas metodologias que foram combinadas: a utilização de métricas que permitam avaliar a qualidade do clustering (como por exemplo, o Silhoeutte Coefficient, o índice de Davies-Bouldin e o índice de Calinski-Harabasz) e ainda a inspeção visual. A segunda etapa foi dedicada à introdução manual de algumas estruturas, como a pele e os gânglios linfáticos, que não foram segmentadas pelos algoritmos de machine learning devido à sua fina espessura e pequena dimensão, respetivamente. Finalmente, a última etapa consistiu na atribuição das propriedades dielétricas, para uma frequência pré-definida, aos tecidos biológicos através do Modelo de Cole-Cole de quatro pólos. Tal como no gerador I, foi criada uma interface que permitiu ao utilizador decidir que características pretende incluir no fantoma, tais como: os tecidos a incluir (tecido adiposo, tecido muscular, pele e / ou gânglios linfáticos), relativamente aos gânglios linfáticos o utilizador poderá ainda determinar o seu número, dimensões, localização em níveis e estado clínico (saudável ou metastizado) e finalmente, o valor de frequência para o qual pretende obter as propriedades dielétricas (permitividade relativa e condutividade) de cada tecido biológico. Este projeto resultou no desenvolvimento de um gerador de modelos realistas da região da cabeça e pescoço com foco nos gânglios linfáticos cervicais, que permite a inserção de tecidos biológicos, tais como o tecidos muscular e adiposo, pele e gânglios linfáticos e aos quais atribui as propriedades dielétricas para uma determinada frequência na gama de micro-ondas. Estes modelos computacionais resultantes do gerador II, e que poderão ser mais tarde impressos em 3D, podem vir a ter grande impacto no processo de desenvolvimento de dispositivos médicos de imagem por micro-ondas que visam diagnosticar gânglios linfáticos cervicais, e consequentemente, contribuir para um processo não invasivo de estadiamento do cancro da cabeça e pescoço.Head and neck cancer is a broad term referring to any epithelial malignancies arising in the paranasal sinuses, nasal and oral cavities, salivary glands, pharynx, and larynx. In 2018, approximately 80% of the newly diagnosed head and neck cancer cases resulted in tumour cells spreading to neighbouring lymph and blood vessels. In order to determine cancer staging and decide which follow-up exams and therapy to follow, physicians excise and assess the Lymph Nodes (LNs) closest to the primary site of the head and neck tumour – the sentinel nodes – which are the ones with highest probability of being targeted by cancer cells. The standard procedure to diagnose the Cervical Lymph Nodes (CLNs), i.e. lymph nodes within the head and neck region, and determine the cancer staging frequently involves their surgical removal and subsequent histopathology. Besides being invasive, the removal of the lymph nodes also has negative impact on patients’ quality of life, it can be health threatening, and it is costly to healthcare systems due to the patients’ needs for follow-up treatments/cares. Anatomically realistic phantoms are required to develop novel technologies tailored to image head and neck regions. Medical MicroWave Imaging (MWI) is a promising non-invasive approach which uses non-ionizing radiation to screen shallow body regions, therefore cervical lymph nodes are excellent candidates to this imaging modality. In this project, a three-dimensional (3D) numerical phantom generator (generator I) and a Magnetic Resonance Imaging (MRI)-derived anthropomorphic phantom generator (generator II) of the head and neck region were developed to create phantoms with different levels of complexity and realism, which can be later 3D printed to test medical MWI devices. The process of designing the numerical phantom generator included the modelling of the head and neck regions according to their anatomy and the distribution of their main tissues, and the creation of an interface which allowed the users to personalise the model (e.g. include or remove certain tissues, depending on the purpose of each generated model). To build the anthropomorphic phantom generator, the modelling process included the creation of a pipeline of data processing steps to be applied to MRIs of the head and neck, followed by the development of algorithms to introduce additional tissues to the models, such as skin and lymph nodes, and finally, the assignment of the dielectric properties to the biological tissues. Similarly, this generator allowed users to decide the features they wish to include in the phantoms. This project resulted in the creation of a generator of 3D anatomically realistic head and neck phantoms which allows the inclusion of biological tissues such as skin, muscle tissue, adipose tissue, and LNs, and assigns state-of-the-art dielectric properties to the tissues. These phantoms may have a great impact in the development process of MWI devices aimed at screening and diagnosing CLNs, and consequently, contribute to a non-invasive staging of the head and neck cancer

Multiple Imaging Modalities for Investigating Soft Hard Tissue Interfaces

Interfaces of hard and soft tissues in the body play a crucial role in processes such as skeletal growth, as well as distributing stresses during load bearing ac- tivities. The mechanically dissimilar tissues are able to be studied individually, but how they integrate at the interface, both by collagen, and mineralisation, is an under explored research area. This is of importance due to these interfaces being particularly prone to damage. In the case of the endplate, hyperminerali- sation of the cartilaginous endplate has been correlated with degeneration of the intervertebral (IVD) discs and chronic lower back pain. For the skull of infants, abnormalities in mineralisation of the cranial sutures leads to deformities of the skull, resulting in increased inter-cranial pressure, and developmental complica- tions for the child. Specific questions addressed in this thesis include, how does the osteocyte lacunae canaliculi network (OLCN) in irregular bones compare to the previously studied long bone?, how are collagen fibres arranged at the soft- hard tissue interfaces?, and how does the mineral density change with distance from the soft-hard interfaces? This PhD project has investigated these research questions via experimental methods, with the spine experiments using the central section of the 1 year old Lumbar 4-5 ovine samples in the coronal plane, to assess the vertebral body - endplate - IVD interfaces cranial to the IVD. The skulls used intact 6 week old murine samples to assess the suture-cranial plate interface for the interfrontal, sagittal, squamous, and cranial sutures. These were dissected, dehydrated, stained, embedded, and polished in polymethylmethacrylate, followed by multi- modal imaging. The imaging techniques used have been confocal laser scanning microscopy, to assess the OLCN, scanning electron microscopy to map the spa- tial distribution of minerals, and second harmonic generation for investigating the collagen across these mechanically complex tissues. Analysis for the OLCN in the spine has used Python scripts to quantify the net- work density, the lacunae density, and the direction of the network with respect to the nearest blood vessel. Quantification of minerals in the skull used Quantitative Backscattered Electron Imaging to get the calcium weight % from the pixel in- tensity. Polarised second harmonic generation was used to quantify the principle direction of the collagen bundles, as well as the dispersion of the collagen fibres making up the bundles. Results have been both qualitative and quantitative in this project. Minerali- sation patterns in the vertebral endplate (VEP) show heterogeneity, with higher degrees of mineralisation in the mineralised cartilage. The values for canaliculi density within the VEP range from 0.05-0.14 μm/μm3, similar values reported in long bone, and the collagen across the cartilage and bone interface has the same principle direction, but the cartilage has a greater degree of dispersion. For the suture-cranial plate interface, the mineral density values ranged between 15-22%, with higher values located at the sites of growth, and edges close to non-mineralised tissues. The collagens have continuity across the mineralisation face, with changes in collagen structure to become more ordered once within the bone tissue, or as Sharpey’s fibres which span the soft-hard interface. The soft-hard interface, which defines the boundary of mineralised tissue, is spatially distinct from the interface between the major collagen types: type I and type II. This observation is seen in both the spine and the cranial sutures. This thesis outlines reliable methods to image and quantify the OLCN, miner- alisation, and collagen in mechanically dissimilar tissues, and establishes a base- line for future experiments to expand on how these features may change with age or disease. The results are in agreement with similar findings in literature, and are novel in that these specific tissues have not been quantified by their OLCN, min- eralisation, and collagen arrangement at this scale before. Findings in this thesis show that there are multiple spatially distinct interfaces of the different constituent components as tissues transition from mineralised to non-mineralised

Improving Radiotherapy Targeting for Cancer Treatment Through Space and Time

Radiotherapy is a common medical treatment in which lethal doses of ionizing radiation are preferentially delivered to cancerous tumors. In external beam radiotherapy, radiation is delivered by a remote source which sits several feet from the patient\u27s surface. Although great effort is taken in properly aligning the target to the path of the radiation beam, positional uncertainties and other errors can compromise targeting accuracy. Such errors can lead to a failure in treating the target, and inflict significant toxicity to healthy tissues which are inadvertently exposed high radiation doses. Tracking the movement of targeted anatomy between and during treatment fractions provides valuable localization information that allows for the reduction of these positional uncertainties. Inter- and intra-fraction anatomical localization data not only allows for more accurate treatment setup, but also potentially allows for 1) retrospective treatment evaluation, 2) margin reduction and modification of the dose distribution to accommodate daily anatomical changes (called `adaptive radiotherapy\u27), and 3) targeting interventions during treatment (for example, suspending radiation delivery while the target it outside the path of the beam). The research presented here investigates the use of inter- and intra-fraction localization technologies to improve radiotherapy to targets through enhanced spatial and temporal accuracy. These technologies provide significant advancements in cancer treatment compared to standard clinical technologies. Furthermore, work is presented for the use of localization data acquired from these technologies in adaptive treatment planning, an investigational technique in which the distribution of planned dose is modified during the course of treatment based on biological and/or geometrical changes of the patient\u27s anatomy. The focus of this research is directed at abdominal sites, which has historically been central to the problem of motion management in radiation therapy

Advances in Analysis and Exploration in Medical Imaging

With an ever increasing life expectancy, we see a concomitant increase in diseases capable of disrupting normal cognitive processes. Their diagnoses are difficult, and occur usually after daily living activities have already been compromised. This dissertation proposes machine learning methods for the study of the neurological implications of brain lesions. It addresses the analysis and exploration of medical imaging data, with particular emphasis to (f)MRI. Two main research directions are proposed. In the first, a brain tissue segmentation approach is detailed. In the second, a document mining framework, applied to reports of neuroscientific studies, is described. Both directions are based on retrieving consistent information from multi-modal data. A contribution in this dissertation is the application of a semi-supervised method, discriminative clustering, to identify different brain tissues and their partial volume information. The proposed method relies on variations of tissue distributions in multi-spectral MRI, and reduces the need for a priori information. This methodology was successfully applied to the study of multiple sclerosis and age related white matter diseases. It was also showed that early-stage changes of normal-appearing brain tissue can already predict decline in certain cognitive processes. Another contribution in this dissertation is in neuroscience meta-research. One limitation in neuroimage processing relates to data availability. Through document mining of neuroscientific reports, using images as source of information, one can harvest research results dealing with brain lesions. The context of such results can be extracted from textual information, allowing for an intelligent categorisation of images. This dissertation proposes new principles, and a combination of several techniques to the study of published fMRI reports. These principles are based on a number of distance measures, to compare various brain activity sites. Application to studies of the default mode network validated the proposed approach. The aforementioned methodologies rely on clustering approaches. When dealing with such strategies, most results depend on the choice of initialisation and parameter settings. By defining distance measures that search for clusters of consistent elements, one can estimate a degree of reliability for each data grouping. In this dissertation, it is shown that such principles can be applied to multiple runs of various clustering algorithms, allowing for a more robust estimation of data agglomeration

Management of Degenerative Cervical Myelopathy and Spinal Cord Injury

The present Special Issue is dedicated to presenting current research topics in DCM and SCI in an attempt to bridge gaps in knowledge for both of the two main forms of SCI. The issue consists of fourteen studies, of which the majority were on DCM, the more common pathology, while three studies focused on tSCI. This issue includes two narrative reviews, three systematic reviews and nine original research papers. Areas of research covered include image studies, predictive modeling, prognostic factors, and multiple systemic or narrative reviews on various aspects of these conditions. These articles include the contributions of a diverse group of researchers with various approaches to studying SCI coming from multiple countries, including Canada, Czech Republic, Germany, Poland, Switzerland, United Kingdom, and the United States

Automatic image analysis of C-arm Computed Tomography images for ankle joint surgeries

Open reduction and internal fixation is a standard procedure in ankle surgery for treating a fractured fibula. Since fibula fractures are often accompanied by an injury of the syndesmosis complex, it is essential to restore the correct relative pose of the fibula relative to the adjoining tibia for the ligaments to heal. Otherwise, the patient might experience instability of the ankle leading to arthritis and ankle pain and ultimately revision surgery. Incorrect positioning referred to as malreduction of the fibula is assumed to be one of the major causes of unsuccessful ankle surgery. 3D C-arm imaging is the current standard procedure for revealing malreduction of fractures in the operating room. However, intra-operative visual inspection of the reduction result is complicated due to high inter-individual variation of the ankle anatomy and rather based on the subjective experience of the surgeon. A contralateral side comparison with the patient’s uninjured ankle is recommended but has not been integrated into clinical routine due to the high level of radiation exposure it incurs. This thesis presents the first approach towards a computer-assisted intra-operative contralateral side comparison of the ankle joint. The focus of this thesis was the design, development and validation of a software-based prototype for a fully automatic intra-operative assistance system for orthopedic surgeons. The implementation does not require an additional 3D C-arm scan of the uninjured ankle, thus reducing time consumption and cumulative radiation dose. A 3D statistical shape model (SSM) is used to reconstruct a 3D surface model from three 2D fluoroscopic projections representing the uninjured ankle. To this end, a 3D SSM segmentation is performed on the 3D image of the injured ankle to gain prior knowledge of the ankle. A 3D convolutional neural network (CNN) based initialization method was developed and its outcome was incorporated into the SSM adaption step. Segmentation quality was shown to be improved in terms of accuracy and robustness compared to the pure intensity-based SSM. This allows us to overcome the limitations of the previously proposed methods, namely inaccuracy due to metal artifacts and the lack of device-to-patient orientation of the C-arm. A 2D-CNN is employed to extract semantic knowledge from all fluoroscopic projection images. This step of the pipeline both creates features for the subsequent reconstruction and also helps to pre-initialize the 3D-SSM without user interaction. A 2D-3D multi-bone reconstruction method has been developed which uses distance maps of the 2D features for fast and accurate correspondence optimization and SSM adaption. This is the central and most crucial component of the workflow. This is the first time that a bone reconstruction method has been applied to the complex ankle joint and the first reconstruction method using CNN based segmentations as features. The reconstructed 3D-SSM of the uninjured ankle can be back-projected and visualized in a workflow-oriented manner to procure clear visualization of the region of interest, which is essential for the evaluation of the reduction result. The surgeon can thus directly compare an overlay of the contralateral ankle with the injured ankle. The developed methods were evaluated individually using data sets acquired during a cadaver study and representative clinical data acquired during fibular reduction. A hierarchical evaluation was designed to assess the inaccuracies of the system on different levels and to identify major sources of error. The overall evaluation performed on eleven challenging clinical datasets acquired for manual contralateral side comparison showed that the system is capable of accurately reconstructing 3D surface models of the uninjured ankle solely using three projection images. A mean Hausdorff distance of 1.72 mm was measured when comparing the reconstruction result to the ground truth segmentation and almost achieved the high required clinical accuracy of 1-2 mm. The overall error of the pipeline was mainly attributed to inaccuracies in the 2D-CNN segmentation. The consistency of these results requires further validation on a larger dataset. The workflow proposed in this thesis establishes the first approach to enable automatic computer-assisted contralateral side comparison in ankle surgery. The feasibility of the proposed approach was proven on a limited amount of clinical cases and has already yielded good results. The next important step is to alleviate the identified bottlenecks in the approach by providing more training data in order to further improve the accuracy. In conclusion, the new approach presented gives the chance to guide the surgeon during the reduction process, improve the surgical outcome while avoiding additional radiation exposure and reduce the number of revision surgeries in the long term