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We introduce a method for extracting hidden patterns seen in rheumatic diseases by using articles from the widely used biomedical database MEDLINE. Rheumatic diseases affect hundreds of millions of people worldwide and lead to substantial loss of functioning and mobility. Diagnosing rheumatic diseases can be difficult because some symptoms are common to many of them. We use Facta system as a biomedical text mining tool for finding symptoms and then create a dataset with the frequencies of symptoms for each disease and apply hierarchical clustering analysis to find similarities between diseases. Clustering analysis yields four distinct types or groups of rheumatic diseases. Although our results cannot remove all the uncertainty for the diagnosis of rheumatic diseases, we believe they can contribute to the diagnosis of rheumatic diseases to a certain extent. We hope that some similarities exposed can provide additional information at the stage of decision-making.

SummaryMitochondrial function plays an important role in the regulation of cellular life and death, including disease states. Disturbance in mitochondrial function and distribution can be accompanied by significant morphological alterations. Electron microscopy tomography (EMT) is a powerful technique to study the 3D structure of mitochondria, but the automatic detection and segmentation of mitochondria in EMT volumes has been challenging due to the presence of subcellular structures and imaging artifacts. Therefore, the interpretation, measurement and analysis of mitochondrial distribution and features have been time consuming, and development of specialized software tools is very important for high-throughput analyses needed to expedite the myriad studies on cellular events. Typically, mitochondrial EMT volumes are segmented manually using special software tools. Automatic contour extraction on large images with multiple mitochondria and many other subcellular structures is still an unaddressed problem. The purpose of this work is to develop computer algorithms to detect and segment both fully and partially seen mitochondria on electron microscopy images. The detection method relies on mitochondria's approximately elliptical shape and double membrane boundary. Initial detection results are first refined using active contours. Then, our seed point selection method automatically selects reliable seed points along the contour, and segmentation is finalized by automatically incorporating a live-wire graph search algorithm between these seed points. In our evaluations on four images containing multiple mitochondria, 52 ellipses are detected among which 42

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Recent studies reveal that mitochondria take substantial responsibility in cellular functions that are closely related to aging diseases caused by degeneration of neurons. These studies emphasize that the membrane and crista morphology of a mitochondrion should receive attention in order to investigate the link between mitochondrial function and its physical structure. Electron microscope tomography (EMT) allows analysis of the inner structures of mitochondria by providing highly detailed visual data from large volumes. Computerized segmentation of mitochondria with minimum manual effort is essential to accelerate the study of mitochondrial structure/function relationships. In this work, we improved and extended our previous attempts to detect and segment mitochondria from transmission electron microcopy (TEM) images. A parabolic arc model was utilized to extract membrane structures. Then, curve energy based active contours were employed to obtain roughly outlined candidate mitochondrial regions. Finally, a validation process was applied to obtain the final segmentation data. 3D extension of the algorithm is also presented in this paper. Our method achieved an average F-score performance of 0.84. Average Dice Similarity Coefficient and boundary error were measured as 0.87 and 14 nm respectively.

Automatic facial expression recognition, which has many applications such as drivers, patients, and criminals' emotions recognition, is a challenging task. This is due to the variety of individuals and facial expression variability in different conditions, for instance, gender, race, colour and changing illumination. In addition, there are many regions in a face image such as forehead, mouth, eyes, eyebrows, nose, cheeks and chin, and extracting features of all these regions are expensive in terms of computational time. Each of the six basic emotions of anger, disgust, fear, happiness, sadness and surprise affect some regions more than the other regions. The goal of this study is to evaluate the performance of enhanced local binary pattern, pyramid histogram of oriented gradients feature‐extraction algorithms and their combination in terms of recognition accuracy, feature vector length and computational time on one, two and three combined regions of a face image. Our experimental results show that the combination of both feature‐extraction algorithms yields an average recognition accuracy of 95.33% using three regions, that is, the mouth, nose and eyes on Cohn–Kanade dataset. Besides, the mouth region is the most important part in terms of accuracy in comparison to eyes, nose and combination of both eyes and nose regions.

In this paper, we describe an algorithm for generating three dimensional models of human faces from uncalibrated images. Input images are taken by a camera generally with a small rotation around a single axis which may cause degenerate solutions during auto-calibration. We describe a solution to this problem by a priori assumptions on the camera. To generate a specific person's head, a generic human head model is deformed according to the 3D coordinates of points obtained by reconstructing the scene using images calibrated with our algorithm. The deformation process is based on a physical based massless spring model and it requires local re-triangulation in the areas with high curvatures. This is achieved by locally applying Delaunay triangulation method. However, there may occur degeneracies in Delaunay triangulation such as encroaching of edges. We describe an algorithm for removing the degeneracies during triangulation by modifying the definition of the Delaunay cavity. This algorithm has also the effect of preserving the curvature in the face area. We have compared the models generated with our algorithm with the models obtained using cyberscanners. The RMS geometric error in these comparisons are less than 1.8 × 10 −2 .