Disconnected Skeleton: Shape at its Absolute Scale

We present a new skeletal representation along with a matching framework to address the deformable shape recognition problem. The disconnectedness arises as a result of excessive regularization that we use to describe a shape at an attainably coarse scale. Our motivation is to rely on the stable properties of the shape instead of inaccurately measured secondary details. The new representation does not suffer from the common instability problems of traditional connected skeletons, and the matching process gives quite successful results on a diverse database of 2D shapes. An important difference of our approach from the conventional use of the skeleton is that we replace the local coordinate frame with a global Euclidean frame supported by additional mechanisms to handle articulations and local boundary deformations. As a result, we can produce descriptions that are sensitive to any combination of changes in scale, position, orientation and articulation, as well as invariant ones.Comment: The work excluding {\S}V and {\S}VI has first appeared in 2005 ICCV: Aslan, C., Tari, S.: An Axis-Based Representation for Recognition. In ICCV(2005) 1339- 1346.; Aslan, C., : Disconnected Skeletons for Shape Recognition. Masters thesis, Department of Computer Engineering, Middle East Technical University, May 200

Perceptually Motivated Shape Context Which Uses Shape Interiors

In this paper, we identify some of the limitations of current-day shape matching techniques. We provide examples of how contour-based shape matching techniques cannot provide a good match for certain visually similar shapes. To overcome this limitation, we propose a perceptually motivated variant of the well-known shape context descriptor. We identify that the interior properties of the shape play an important role in object recognition and develop a descriptor that captures these interior properties. We show that our method can easily be augmented with any other shape matching algorithm. We also show from our experiments that the use of our descriptor can significantly improve the retrieval rates

Automatic segmentation of surface EMG images: Improving the estimation of neuromuscular activity

Surface electromyograms (EMGs) recorded with a couple of electrodes are meant to comprise representative information of the whole muscle activation. Nonetheless, regional variations in neuromuscular activity seem to occur in numerous conditions, from standing to passive muscle stretching. In this study, we show how local activation of skeletal muscles can be automatically tracked from EMGs acquired with a bi-dimensional grid of surface electrodes (a grid of 8 rows and 15 columns was used). Grayscale images were created from simulated and experimental EMGs, filtered and segmented into clusters of activity with the watershed algorithm. The number of electrodes on each cluster and the mean level of neuromuscular activity were used to assess the accuracy of the segmentation of simulated signals. Regardless of the noise level, thickness of fat tissue and acquisition configuration (monopolar or single differential), the segmentation accuracy was above 60%. Accuracy values peaked close to 95% when pixels with intensity below approximately 70% of maximal EMG amplitude in each segmented cluster were excluded. When simulating opposite variations in the activity of two adjacent muscles, watershed segmentation produced clusters of activity consistently centered on each simulated portion of active muscle and with mean amplitude close to the simulated value. Finally, the segmentation algorithm was used to track spatial variations in the activity, within and between medial and lateral gastrocnemius muscles, during isometric plantar flexion contraction and in quiet standing position. In both cases, the regionalization of neuromuscular activity occurred and was consistently identified with the segmentation method

Myofibre Segmentation in H&E Stained Adult Skeletal Muscle Images using Coherence-Enhancing Diffusion Filtering

BACKGROUND: The correct segmentation of myofibres in histological muscle biopsy images is a critical step in the automatic analysis process. Errors occurring as a result of incorrect segmentations have a compounding effect on latter morphometric analysis and as such it is vital that the fibres are correctly segmented. This paper presents a new automatic approach to myofibre segmentation in H&E stained adult skeletal muscle images that is based on Coherence-Enhancing Diffusion filtering. METHODS: The procedure can be broadly divided into four steps: 1) pre-processing of the images to extract only the eosinophilic structures, 2) performing of Coherence-Enhancing Diffusion filtering to enhance the myofibre boundaries whilst smoothing the interior regions, 3) morphological filtering to connect unconnected boundary regions and remove noise, and 4) marker controlled watershed transform to split touching fibres. RESULTS: The method has been tested on a set of adult cases with a total of 2,832 fibres. Evaluation was done in terms of segmentation accuracy and other clinical metrics. CONCLUSIONS: The results show that the proposed approach achieves a segmentation accuracy of 89% which is a significant improvement over existing methods

Integrated method for quantitative morphometry and oxygen transport modelling in striated muscle

Identifying structural limitations in O2 transport is primarily restricted by current methods employed to characterise the nature of physiological remodelling. Inadequate resolution or breadth of available data has impaired development of routine diagnostic protocols and effective therapeutic strategies. Understanding O2 transport within striated muscle faces major challenges, most notably in quantifying how well individual fibres are supplied by the microcirculation, which has necessitated exploring tissue O2 supply using theoretical modelling of diffusive exchange. Having identified capillary domains as a suitable model for the description of local O2 supply, and requiring less computation than numerically calculating the trapping regions that are supplied by each capillary via biophysical transport models, we sought to design a high throughput method for histological analysis. We present an integrated package that identifies optimal protocols for identification of important input elements, processing of digitised images with semi-automated routines, and incorporation of these data into a mathematical modelling framework with computed output visualised as the tissue partial pressure of O2 (PO2) distribution across a biopsy sample. Worked examples are provided using muscle samples from experiments involving rats and humans

Characterization of structural changes in spinal vertebrae based on perturbations to an adaptive model

Diffuse Idiopathic Skeletal Hyperostosis, or DISH, is a disease characterized by ossification of the entheses and the anterior longitudinal ligament. The diagnosis is made by visual analysis of an X-ray by a professional using the Resnick Criterion. The different experience among professionals and the fact that this criterion is only suitable in advanced stages of the disease make diagnosis difficult. Therefore, this work aims to contribute to the development of an auxiliary diagnostic tool for this disease. For this, a semi-automatic vertebral segmentation algorithm based on active morphological contours was proposed, comparing it with previous work and with segmentations made by experts on two radiographic images. Next, the corners of the vertebrae, where the disease manifests itself, were analyzed in order to characterize images with DISH. To accomplish this, it was assumed symmetry of the vertebrae and a Gaussian distribution of the histograms of those corners to analyze them and calculate two ratios: Left upper corner mean value / Right upper corner mean value (LS/RS) and Left lower corner mean value / Right lower corner mean value (LI/RI), in order to find a differentiating metric between vertebrae with pathology and those without. The results achieved by the algorithm were clearly superior to the previous work and similar to that of the experts. The analysis of pathologic vertebrae revealed a difference in the shift of the distributions of pathologic corners relative to non-pathologic ones, which is not seen in vertebrae without apparent pathology. Regarding the ratios, the LI/RI proved to be particularly effective in differentiating, being closer to 1 when pathology is not present.A Hiperostose Esquelética Idiopática Difusa, ou DISH, é uma doença caracterizada pela ossificação das entéses e do ligamento longitudinal anterior. O diagnóstico é realizado pela análise visual de um raio-X, por um profissional, utilizando o Critério de Resnick. A diferente experiência entre profissionais e o facto de este critério só ser adequado em fases avançadas da doença tornam o diagnóstico difícil. Por isso, este trabalho visa contribuir para o desenvolvimento de um instrumento auxiliar de diagnóstico desta doença. Para isso, foi proposto um algoritmo de segmentação de vertebras, semi-automático, baseado em contornos morfológicos ativos, comparando-o com o trabalho anterior e com as segmentações feitas por especialistas em duas imagens radiográficas. De seguida, foram analisadas as extremidades das vértebras, onde a doença se manifesta, com o objetivo de identificar imagens com DISH. Para tal, assumiu-se a simetria das vértebras e uma distribuição Gaussiana dos histogramas das extremidades para analisar as mesmas e calcular dois rácios: Valor médio do canto superior esquerdo / Valor médio do canto superior direito(LS/RS) e valor médio do canto inferior esquerdo /Valor médio do canto inferior direito(LI/RI), a fim de encontrar uma métrica diferenciadora das vértebras com patologia das não patológicas. Os resultados conseguidos pelo algoritmo foram claramente superiores ao do trabalho anterior e semelhantes ao dos peritos. A análise das vértebras patológicas revelou uma diferença na deslocação das distribuições dos cantos patológicos relativamente aos não patológicos, o que não se verifica em vértebras sem patologia aparente. Relativamente aos rácios, o LI/RI mostrou ser particularmente eficaz na diferenciação, estando mais próximo de 1 quando a patologia não está presente

An introduction to the constraints-led approach to learning in outdoor education

Participation in outdoor education is underpinned by a learner's ability to acquire skills in activities such as canoeing, bushwalking and skiing and consequently the outdoor leader is often required to facilitate skill acquisition and motor learning. As such, outdoor leaders might benefit from an appropriate and tested model on how the learner acquires skills in order to design appropriate learning contexts. This paper introduces an approach to skill acquisition based on ecological psychology and dynamical systems theory called the constraints-led approach to skills acquisition. We propose that this student-centred approach is an ideal perspective for the outdoor leader to design effective learning settings. Furthermore, this open style of facilitation is also congruent with learning models that focus on other concepts such as teamwork and leadership

Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats

Background: In developing countries, maternal undernutrition is the major intrauterine environmental factor contributing to fetal development and adverse pregnancy outcomes. Maternal nutrition restriction (MNR) in gestation has proven to impact overall growth, bone development, and proliferation and metabolism of mesenchymal stem cells in offspring. However, the efficient method for elucidation of fetal bone development performance through maternal bone metabolic biochemical markers remains elusive. Methods: We adapted goats to elucidate fetal bone development state with maternal serum bone metabolic proteins under malnutrition conditions in mid- and late-gestation stages. We used the experimental data to create 72 datasets by mixing different input features such as one-hot encoding of experimental conditions, metabolic original data, experimental-centered features and experimental condition probabilities. Seven Machine Learning methods have been used to predict six fetal bone parameters (weight, length, and diameter of femur/humerus). Results: The results indicated that MNR influences fetal bone development (femur and humerus) and fetal bone metabolic protein levels (C-terminal telopeptides of collagen I, CTx, in middle-gestation and N-terminal telopeptides of collagen I, NTx, in late-gestation), and maternal bone metabolites (low bone alkaline phosphatase, BALP, in middle-gestation and high BALP in late-gestation). The results show the importance of experimental conditions (ECs) encoding by mixing the information with the serum metabolic data. The best classification models obtained for femur weight (Fw) and length (FI), and humerus weight (Hw) are Support Vector Machines classifiers with the leave-one-out cross-validation accuracy of 1. The rest of the accuracies are 0.98, 0.946 and 0.696 for the diameter of femur (Fd), diameter and length of humerus (Hd, Hl), respectively. With the feature importance analysis, the moving averages mixed ECs are generally more important for the majority of the models. The moving average of parathyroid hormone (PTH) within nutritional conditions (MA-PTH-experim) is important for Fd, Hd and Hl prediction models but its removal for enhancing the Fw, Fl and Hw model performance. Further, using one feature models, it is possible to obtain even more accurate models compared with the feature importance analysis models. In conclusion, the machine learning is an efficient method to confirm the important role of PTH and BALP mixed with nutritional conditions for fetal bone growth performance of goats. All the Python scripts including results and comments are available into an open repository at https://gitlab.com/muntisa/goat-bones-machine-learning

Nutritional physical examination: historical, methodological and applied approach

The historical interest in the use of physical evaluation skills in clinical settings gained new notoriety at the end of the 20th century with evidence that patients in intensive care units experienced increased morbidity and mortality related to poor nutritional status before and/or during their admission. This awareness of the adverse effects of malnutrition led to the need for screening and evaluation tools to identify nutritional risk. no clinical finding of EFN should be considered a diagnosis per se. It is academic, scientific and clinical consensus that its results should be interpreted as suggestive, being crucial to consider the other methods of clinical evaluation of the patient\u27s nutritional status for the correct global nutritional diagnosis. However, the systematic and periodic repetition of the test may help to follow the evolution of the individual\u27s nutritional status, especially in the long term. In summary, although it requires specialized training and continuous practice of the evaluator and/or the team – in addition to requiring complementary nutritional information – the physical nutritional examination can still be considered an effective adjuvant method in the clinical evaluation of the patient’s nutritional status