Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications

In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.     

Architectures for multi-threaded MVC-compliant multi-view video decoding and benchmark tests

3D video based on stereo/multi-view representations is becoming widely popular. Real-time encoding/decoding of such video is an important concern as the number and spatial/temporal resolution of views increase. We present a systematic method for design and optimization of multi-threaded multi-view video encoding/decoding algorithms using multi-core processors and provide benchmark results for real-time decoding. The proposed multi-core decoding architectures are compliant with the current MVC extension of H.264/AVC international standard, and enable multi-threaded processing with negligible loss of encoding efficiency and minimum processing overhead. Benchmark results show that multi-core processors and multi-threading decoding are necessary for real-time high-definition multi-view video decoding and display.     

Two-step steam pyrolysis of biomass for hydrogen production

In this study, different char based catalysts were evaluated in order to increase hydrogen production from the steam pyrolysis of olive pomace in two stage fixed bed reactor system. Biomass char, nickel loaded biomass char, coal char and nickel or iron loaded coal chars were used as catalyst. Acid washed biomass char was also tested to investigate the effect of inorganics in char on catalytic activity for hydrogen production. Catalysts were characterized by using Brunauer–Emmet–Teller (BET) method, X-ray diffraction (XRD) analyzer, X-ray fluorescence (XRF) and thermogravimetric analyzer (TGA). The results showed that the steam in absence of catalyst had no influence on hydrogen production. Increase in catalytic bed temperature (from 500 °C to 700 °C) enhanced hydrogen production in presence of Ni-impregnated and non-impregnated biomass char. Inherent inorganic content of char had great effect on hydrogen production. Ni based biomass char exhibited the highest catalytic activity in terms of hydrogen production. Besides, Ni and Fe based coal char had catalytic activity on H₂ production. On the other hand, the results showed that biomass char was not thermally stable under steam pyrolysis conditions. Weight loss of catalyst during steam pyrolysis could be attributed to steam gasification of biomass char itself. In contrast, properties of coal char based catalysts after steam pyrolysis process remained nearly unchanged, leading to better thermal stability than biomass char.     

Translation, Scale, and Deformation Weighted Polar Active Contours

Polar active contours have proven to be a powerful segmentation method for many medical as well as other computer vision applications, such as interactive image segmentation or tracking. Inspired by recent work on Sobolev active contours we derive a Sobolev-type function space for polar curves, which is endowed with a metric that allows us to favor origin translations and scale changes over smooth deformations of the curve. The resulting translation, scale, and deformation weighted polar active contours inherit the coarse-to-fine behavior of Sobolev active contours as well as their robustness to local minima and are thus very useful for many medical applications, such as cross-sectional vessel segmentation, aneurysm analysis, or cell tracking.     

Information-Theoretic Active Polygons for Unsupervised Texture Segmentation

Curve evolution models used in image segmentation and based on image region information usually utilize simple statistics such as means and variances, hence can not account for higher order nature of the textural characteristics of image regions. In addition, the object delineation by active contour methods, results in a contour representation which still requires a substantial amount of data to be stored for subsequent multimedia applications such as visual information retrieval from databases. Polygonal approximations of the extracted continuous curves are required to reduce the amount of data since polygons are powerful approximators of shapes for use in later recognition stages such as shape matching and coding. The key contribution of this paper is the development of a new active contour model which nicely ties the desirable polygonal representation of an object directly to the image segmentation process. This model can robustly capture texture boundaries by way of higher-order statistics of the data and using an information-theoretic measure and with its nature of the ordinary differential equations. This new variational texture segmentation model, is unsupervised since no prior knowledge on the textural properties of image regions is used. Another contribution in this sequel is a new polygon regularizer algorithm which uses electrostatics principles. This is a global regularizer and is more consistent than a local polygon regularization in preserving local features such as corners.Supported by NSF grant CCR-0133736.Partially supported by AFOSR grant F49620-98-1-0190 and NSF grant CCR-9984067.     

Contactless Electrical and Structural Characterization of Semiconductor Nanowires with Axially Modulated Doping Profiles

Efficient characterization of semiconductor nanowires having complex dopant profiles or heterostructures is critical to fully understand these materials and the devices built from them. Existing electrical characterization techniques are slow and laborious, particularly for multisegment nanowires, and impede the statistical understanding of highly variable samples. Here, it is shown that electro‐orientation spectroscopy (EOS)—a high‐throughput, noncontact method for statistically characterizing the electrical properties of entire nanowire ensembles—can determine the conductivity and dimensions of two distinct segments in individual Si nanowires with axially encoded dopant profiles. This analysis combines experimental measurements and computational simulations to determine the electrical conductivity of the nominally undoped segment of two‐segment Si nanowires, as well as the ratio of the segment lengths. The efficacy of this approach is demonstrated by comparing results generated by EOS with conventional four‐point‐probe measurements. This work provides new insights into the control and variability of semiconductor nanowires for electronic applications and is a critical first step toward the high‐throughput interrogation of complete nanowire‐based devices.     

Design of a Virtual MCA and Its Use in Radiation Spectrometer

Instruments and Experimental Techniques - In the present study, a virtual MCA (multichannel analyzer) which could be used in a radiation detection spectrometer was developed by writing a code...     

Effect of particle size on microstructural and mechanical properties of UHMWPE–TiO2 composites produced by gelation and crystallization method

In this study, Ultra-high-molecular-weight polyethylene (UHMWPE) in 0.5 wt % concentration—0.5, 1, and 2 wt % nanosized and micron-sized TiO₂ composites were produced via gelation/crystallization method in decalin + antioxidant solution at 150 °C for 45 min by using magnetic stirrer. The gel composites were cooled in an aluminum tray embedded in iced water under ambient conditions and dried in an oven at 130 °C for 90 min to remove any residual trace of decalin and to strengthen the UHWMPE matrix. Scanning electron microscopy–EDS images indicate that TiO₂ particles were integrated well with the polymer matrix. differential scanning calorimetry studies revealed that the crystallinity of pure UHMWPE was calculated as 56% and an increase of 13.32% for micron sized and 19.25% for nano sized TiO₂. Crystalline and amorphous phases of UHMWPE–TiO₂ composites confirmed by Raman are in good agreement with the literature. The elastic modulus of test materials ranged from 610 to 791 MPa for micron sized and raised from 675 to 1085 for nano sized reinforcing agents. Ultimate tensile stress increased about 35% for micron sized and 60% for nano sized weight 1% TiO₂ reinforced composites. Biomineralization tests (performed in stimulated body fluid, at 37 °C and 6.5 pH during 1 month) have shown that produced composites are compatible as acetabular liner replacement for hipjoints due to no accumulation (Ca, P, Na, etc.) on UHMWPE–TiO₂ composites. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47402.