Transfer function analysis (TFA) is extensively used to assess human physiological functions. However, extracting parameters from TFA is not usually optimized for detecting impaired function. In this study, we propose to use data-driven approaches to improve the performance of TFA in assessing blood flow control in the brain (dynamic cerebral autoregulation, dCA). Data were collected from two distinct groups of subjects deemed to have normal and impaired dCA. Continuous arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) were simultaneously recorded for approximately 10 mins in 82 subjects (including 41 healthy controls) to give 328 labeled samples of the TFA variables. The recordings were further divided into 4,294 short data segments to generate 17,176 unlabeled samples of the TFA variables. We optimized TFA post-processing with a generic semi-supervised learning strategy and a novel semi-supervised stacked ensemble learning (SSEL) strategy for classification into normal and impaired dCA. The generic strategy led to a performance with no significant difference to that of the conventional dCA analysis methods, whereas the proposed new strategy boosted the performance of TFA to an accuracy of 93.3%. To our knowledge, this is the best dCA discrimination performance obtained to date and the first attempt at optimizing TFA through machine learning techniques. Equivalent methods can potentially also be applied to assessing a wide spectrum of other human physiological functions.

We develop a framework to virtually unroll fragile historical parchment scrolls, which cannot be physically unfolded via a sequence of X-ray tomographic slices, thus providing easy access to those parchments whose contents have remained hidden for centuries. The first step is to produce a topologically correct segmentation, which is challenging as the parchment layers vary significantly in thickness, contain substantial interior textures and can often stick together in places. For this purpose, our method starts with linking the broken layers in a slice using the topological structure propagated from its previous processed slice. To ensure topological correctness, we identify fused regions by detecting junction sections, and then match them using global optimization efficiently solved by the blossom algorithm, taking into account the shape energy of curves separating fused layers. The fused layers are then separated using as-parallel-as-possible curves connecting junction section pairs. To flatten the segmented parchment, pixels in different frames need to be put into alignment. This is achieved via a dynamic programming-based global optimization, which minimizes the total matching distances and penalizes stretches. Eventually, the text of the parchment is revealed by ink projection. We demonstrate the effectiveness of our approach using challenging real-world data sets, including the water damaged fifteenth century Bressingham scroll.

A robust ellipse detector is proposed. The detector preprocesses the edge map by removing all the isolated points and conjunction points, and exploits polygonal curve to extract the elliptical arcs. A non‐iterative geometric distance computation method is presented to serve a criterion which identifies the elliptical arcs belonging to the same ellipse by likelihood ratio test and fit ellipses to those merged arcs. The authors test their algorithm on both synthetic and real images, and the experimental results show a good performance of their algorithm.