Fully automatic brain tumor extraction and tissue segmentation from multimodal MRI brain images

Segmentation of Brain tumor from the magnetic resonance imaging (MRI) of head scans is an essential requirement for clinical diagnosis since manual segmentation is a fatigue and time‐consuming process. Recent computer‐aided diagnosis systems depend on the development of fully automatic methods to overcome these problems. In the present work, a fully automated algorithm is proposed to extract and segment tumor regions from multimodal magnetic resonance imaging (MMMRI) sequences. The algorithm has three phases: (a) tumor portion extraction, (b) tumor substructure segmentation, and (c) 3D postprocessing. First, the algorithm extracts tumor portion using a set of image processing operations from T2, fluid‐attenuated inversion recovery (FLAIR), and T1C images. Here, the proposed modified fuzzy c means clustering algorithm is used for enhancing the tumor portion extraction process. Then, the substructures of tumor such as edema, enhancing tumor, and necrotic regions are segmented from MMMRI sequences, T2, FLAIR, and T1C using region‐wise set operations in Phase II. Finally, 3D visualization of the segmented tumor and volume estimation is performed as postprocessing in Phase III. The proposed work was experimented on BraTS 2013 dataset. The quantitative analysis is performed using William's Index, Dice, sensitivity, specificity, and accuracy and is compared with 19 state‐of‐the‐art methods. The proposed method yields comparable results as 77%, 53%, and 59% of Dice for complete, core, and enhancing tumor regions, respectively.     

Edge detection using Chebyshev's orthogonal polynomial and brain extraction from magnetic resonance images of human head

In this article, we propose a new edge detecting method based on the transform coefficients obtained by a point spread function constructed out of Chebyshev's orthogonal polynomials. This edge detector finds edges similar to that of Prewitt and Roberts but is robust against additive and multiplicative noises. We also propose a new scheme to extract brain portion from the magnetic resonance images (MRI) of human head scan by making use of the of the new edge detector. The proposed scheme involves edge detection, morphological operations, and largest connected component analysis. Experiments conducted by applying the proposed scheme on 19 volumes of MRI collected from Internet Brain Segmentation Repository (IBSR) show that the proposed brain extraction scheme performed better than the popular Brain Extraction Tool (BET). The performance of the proposed scheme is measured by computing the Dice coefficient (D) and Jaccard similarity index (J). The proposed method produced a value of 0.9068 for D and 0.8321 for J.     

Generation and quality improvement of 3D models from silhouettes of 2D images

The shape-from-silhouette (SFS) method has been widely used in 3D shape reconstruction. It uses silhouettes of a series of 2D images captured from multiple viewpoints of an object to generate a 3D model that describes the visual hull of the object. The SFS method faces an inherent problem that virtual features appear all over the model. In addition, concavities on the object may wrongly be modeled as convex shapes because they are invisible on image silhouettes. The purpose of this study is to propose a method to generate a 3D model from silhouettes of multiple images and propose a quality improvement method to overcome the above-mentioned problems. The 3D modeling method focuses on accurate evaluation of 3D points intersected by all polyhedra from different views and the removal of poor meshes on triangulation. The quality improvement method is essentially an iterative procedure, which for smoothing the model and eliminating virtual features and artifacts, while preserving the consistency of all silhouettes. The proposed method is to be used for product presentations in e-commerce, in which the 3D model must be covered with color texture of an object. Several examples are presented to illustrate the capability of the proposed method.     

Thermoplastic composite from innovative flat knitted 3D multi-layer spacer fabric using hybrid yarn and the study of 2D mechanical properties

Due to their improved mechanical properties, 3D multi-layer spacer fabrics could be used for lightweight applications such as textile-based sandwich preforms. Modern flat knitting machines using high performance yarns are able to knit complex 3D multi-layer spacer fabrics consisting of individual surface and connecting layers. This paper reports on the development of 3D flat knitted spacer fabric for 3D thermoplastic composites using hybrid yarns made of glass (GF) and polypropylene (PP) filaments. Moreover, mechanical properties of reinforcement yarns, 2D knit fabrics and 2D composites manufactured using various integration methods of reinforcement yarns were also studied. The integration of reinforcement yarns as biaxial inlays (warp and weft yarns) is found to be the best solution for knitting, whereas the tuck stitches show optimal results.