Using retinex for point selection in 3D shape registration

Inspired by retinex theory, we propose a novel method for selecting key points from a depth map of a 3D freeform shape; we also use these key points as a basis for shape registration. To find key points, first, depths are transformed using the Hotelling method and normalized to reduce their dependence on a particular viewpoint. Adaptive smoothing is then applied using weights which decrease with spatial gradient and local inhomogeneity; this preserves local features such as edges and corners while ensuring smoothed depths are not reduced. Key points are those with locally maximal depths, faithfully capturing shape. We show how such key points can be used in an efficient registration process, using two state-of-the-art iterative closest point variants. A comparative study with leading alternatives, using real range images, shows that our approach provides informative, expressive, and repeatable points leading to the most accurate registration results. © 2014 Elsevier Ltd

Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation

Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate estimation of the transformation relating different datasets. Inspired by AdaBoost, this paper proposes a novel iterative re-weighting method to tackle the challenging problem of evaluating point matches established by typical FEM methods. Weights are used to indicate the degree of belief that each point match is correct. Our method has three key steps: (i) estimation of the underlying transformation using weighted least squares, (ii) penalty parameter estimation via minimization of the weighted variance of the matching errors, and (iii) weight re-estimation taking into account both matching errors and information learnt in previous iterations. A comparative study, based on real shapes captured by two laser scanners, shows that the proposed method outperforms four other state-of-the-art methods in terms of evaluating point matches between overlapping shapes established by two typical FEM methods, resulting in more accurate estimates of the underlying transformation. This improved transformation can be used to better initialize the iterative closest point algorithm and its variants, making 3D shape registration more likely to succeed

Accurately Estimating Rigid Transformations in Registration using a Boosting-Inspired Mechanism

Feature extraction and matching provide the basis of many methods for object registration, modeling, retrieval, and recognition. However, this approach typically introduces false matches, due to lack of features, noise, occlusion, and cluttered backgrounds. In registration, these false matches lead to inaccurate estimation of the underlying transformation that brings the overlapping shapes into best possible alignment. In this paper, we propose a novel boosting-inspired method to tackle this challenging task. It includes three key steps: (i) underlying transformation estimation in the weighted least squares sense, (ii) boosting parameter estimation and regularization via Tsallis entropy, and (iii) weight re-estimation and regularization via Shannon entropy and update with a maximum fusion rule. The process is iterated. The final optimal underlying transformation is estimated as a weighted average of the transformations estimated from the latest iterations, with weights given by the boosting parameters. A comparative study based on real shape data shows that the proposed method outperforms four other state-of-the-art methods for evaluating the established point matches, enabling more accurate and stable estimation of the underlying transformation

Radiation Tolerant 3D Laser Scanner for Structural Inspections in Nuclear Reactor Vessels and Fuel Storage Pools

Accurate and timely assessment of displacements and/or structural damages in nuclear reactor vessels' components is a key action in routine inspections for planning maintenance and repairs but also in emergency situations for mitigating consequences of nuclear incidents. Nevertheless, all these components are maintained underwater and reside in high-radiation fields thus imposing harsh operative conditions to inspection devices which must cope with effects such as Cerenkov radiation background, Total Ionizing Radiation (TID), and occlusions in the detectors' field of view. To date, ultrasonic techniques and video cameras are in use for inspection of components' integrity and with measurements of volumetric and surface crack opening displacements, respectively. The present work reports the realization of a radiation tolerant laser scanner and the results of tests in a nuclear research reactor vessel for acquisition of 3D models of critical components. The device, qualified for underwater operation and for withstanding up to 1 MGy of TID, is based on a 515 nm laser diode and a fast-scanning electro-optic unit. To evaluate performances in a significant but controlled environment, the device has been deployed in the vessel of a research reactor operated by ENEA in the Casaccia Research Centre in Rome (Italy). A 3D model of the fuel rods assembly through a cooling water column of 7 m has been acquired. The system includes proprietary postprocessing software that automatically recognizes components of interest and provides dimensional analysis. Possible application fields of the system stretch to dimensional analysis also in spent nuclear fuel storage pools

RNA-Based Assay for Next-Generation Sequencing of Clinically Relevant Gene Fusions in Non-Small Cell Lung Cancer

Gene fusions represent novel predictive biomarkers for advanced non-small cell lung cancer (NSCLC). In this study, we validated a narrow NGS gene panel able to cover therapeutically-relevant gene fusions and splicing events in advanced-stage NSCLC patients. To this aim, we first assessed minimal complementary DNA (cDNA) input and the limit of detection (LoD) in different cell lines. Then, to evaluate the feasibility of applying our panel to routine clinical samples, we retrospectively selected archived lung adenocarcinoma histological and cytological (cell blocks) samples. Overall, our SiRe RNA fusion panel was able to detect all fusions and a splicing event harbored in a RNA pool diluted up to 2 ng/µL. It also successfully analyzed 46 (95.8%) out of 48 samples. Among these, 43 (93.5%) out of 46 samples reproduced the same results as those obtained with conventional techniques. Intriguingly, the three discordant results were confirmed by a CE-IVD automated real-time polymerase chain reaction (RT-PCR) analysis (Easy PGX platform, Diatech Pharmacogenetics, Jesi, Italy). Based on these findings, we conclude that our new SiRe RNA fusion panel is a valid and robust tool for the detection of clinically relevant gene fusions and splicing events in advanced NSCLC

Pulmonary Immunohistochemical Detection of Surfactant Protein A (SP-A) in Fatal Drowning

L'annegamento rimane ancora una delle diagnosi più difficili della patologia forense perché i risultati macroscopici e microscopici dell'autopsia sono aspecifici. Un marker diagnostico ideale per annegamento deve ancora essere sviluppato, ma alcuni autori hanno recentemente studiato l'SP-A come marcatore di soffocamento e annegamento. Lo scopo di questo studio è quello di confrontare le caratteristiche istopatologiche e la SP-A espressione immunoistochimica nei tessuti polmonari in caso di annegamento con quelli determinati da altre cause di discriminare tra sommersione di cadavere e annegamento.Drowning still remains one of the most difficult diagnoses in forensic pathology because macroscopic and microscopic autopsy findings are unspecific. An ideal diagnostic marker for drowning still needs to be developed, but some authors have recently studied SP-A as a marker of asphyxiation and drowning. The aim of this study is to compare the histopathological features and the SP-A immunohistochemical expression in lung tissue in the case of drowning with those determined by other causes to discriminate between cadaver submersion and drowning