Tissue engineering techniques using a combination of polymeric scaffolds and cells represent a promising approach for nerve regeneration. We fabricated electrospun scaffolds by blending of Poly (3-hydroxybutyrate) (PHB) and Poly (3-hydroxy butyrate-co-3- hydroxyvalerate) (PHBV) in different compositions in order to investigate their potential for the regeneration of the myelinic membrane. The thermal properties of the nanofibrous blends was analyzed by differential scanning calorimetry (DSC), which indicated that the melting and glass temperatures, and crystallization degree of the blends decreased as the PHBV weight ratio increased. Raman spectroscopy also revealed that the full width at half height of the band centered at 1725 cm−1 can be used to estimate the crystalline degree of the electrospun meshes. Random and aligned nanofibrous scaffolds were also fabricated by electrospinning of PHB and PHBV with or without type I collagen. The influence of blend composition, fiber alignment and collagen incorporation on Schwann cell (SCs) organization and function was investigated. SCs attached and proliferated over all scaffolds formulations up to 14 days. SCs grown on aligned PHB/PHBV/collagen fibers exhibited a bipolar morphology that oriented along the fiber direction, while SCs grown on the randomly oriented fibers had a multipolar morphology. Incorporation of collagen within nanofibers increased SCs proliferation on day 14, GDNF gene expression on day 7 and NGF secretion on day 6. The results of this study demonstrate that aligned PHB/PHBV electrospun nanofibers could find potential use as scaffolds for nerve tissue engineering applications and that the presence of type I collagen in the nanofibers improves cell differentiation.

This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image is transformed into three sets: (true), (indeterminate) that represents noise, and (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set , and a new -correction operation is introduced to compute the set in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively.

Color segmentation of infrared thermal images is an important factor in detecting the tumor region. The cancerous tissue with angiogenesis and inflammation emits temperature pattern different from the healthy one. In this paper, two color segmentation techniques, K-means and fuzzy c-means for color segmentation of infrared (IR) breast images are modeled and compared. Using the K-means algorithm in Matlab, some empty clusters may appear in the results. Fuzzy c-means is preferred because the fuzzy nature of IR breast images helps the fuzzy c-means segmentation to provide more accurate results with no empty cluster. Since breasts with malignant tumors have higher temperature than healthy breasts and even breasts with benign tumors, in this study, we look for detecting the hottest regions of abnormal breasts which are the suspected regions. The effect of IR camera sensitivity on the number of clusters in segmentation is also investigated. When the camera is ultra sensitive the number of clusters being considered may be increased.

Interactions between Schwann cells (SCs) and scaffolds are important for tissue development during nerve regeneration, because SCs physiologically assist in directing the growth of regenerating axons. In this study, we prepared electrospun scaffolds combining poly (3-hydroxybutyrate) (PHB) and poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) functionalized with either collagen I, H-Gly-Arg-Gly-Asp-Ser-OH (GRGDS), H-Tyr-Ile-Gly-Ser-Arg-NH2 (YIGSR), or H-Arg-Asn-Ile-Ala-Glu-Ile-Ile-Lys-Asp-Ile-OH (p20) neuromimetic peptides to mimic naturally occurring ECM motifs for nerve regeneration. Cells cultured on fibrous mats presenting these biomolecules showed a significant increase in metabolic activity and proliferation while exhibiting unidirectional orientation along the orientation of the fibers. Real-time PCR showed cells cultured on peptide-modified scaffolds had a significantly higher neurotrophin expression compared to those on untreated nanofibers. Our study suggests that biofunctionalized aligned PHB/PHBV nanofibrous scaffolds may elicit essential cues for SCs activity and could serve as a potential scaffold for nerve regeneration. From the clinical editor: Nanotechnology-based functionalized scaffolds represent one of the most promising approaches in peripheral nerve recovery, as well as spinal cord recovery. In this study, bio-functionalized and aligned PHB/PHBV nanofibrous scaffolds were found to elicit essential cues for Schwann cell activity, therefore could serve as a potential scaffold for nerve regeneration.

Abstract-We compare filter bank multicarrier (FBMC) and orthogonal frequency-division multiplexing (OFDM) in the uplink of a multiple access network. Our study reveals that the high sensitivity of OFDM to carrier frequency offset (CFO) among different users and the need for interference cancellation methods to reduce this sensitivity leads to very complex and yet not very high performance systems. In FBMC-based networks, on the other hand, near-perfect performance is achieved without any need for interference cancellation, thanks to the excellent frequency localized filters used in the realization of FBMC systems.Index Terms-Carrier frequency offset (CFO), computational complexity, filter bank multicarrier (FBMC), offset quadrature amplitude modulation (OQAM), orthogonal frequency division multiple access (OFDMA).

We present an automated method for segmentation of epithelial cells in images taken from ThinPrep scenes by a digital camera in a cytology lab. The method covers both steps of localization of cell objects in low resolution and detection of cytoplasm and nucleus boundary in high resolution. The underlying method makes use of geometric active contours as a powerful tool of segmentation. We also provide the analysis of the connected cells. For this purpose an automatic circular decomposition method is incorporated and adapted to the application by changing its segmentation condition. The results are evaluated numerically and compared with those of previous work in literature.

This paper introduces a new approach for the segmentation of skin lesions in dermoscopic images based on wavelet network (WN). The WN presented here is a member of fixed-grid WNs that is formed with no need of training. In this WN, after formation of wavelet lattice, determining shift and scale parameters of wavelets with two screening stage and selecting effective wavelets, orthogonal least squares algorithm is used to calculate the network weights and to optimize the network structure. The existence of two stages of screening increases globality of the wavelet lattice and provides a better estimation of the function especially for larger scales. R, G, and B values of a dermoscopy image are considered as the network inputs and the network structure formation. Then, the image is segmented and the skin lesions exact boundary is determined accordingly. The segmentation algorithm were applied to 30 dermoscopic images and evaluated with 11 different metrics, using the segmentation result obtained by a skilled pathologist as the ground truth. Experimental results show that our method acts more effectively in comparison with some modern techniques that have been successfully used in many medical imaging problems.

A fully-automated method based on graph shortest path, graph cut and neutrosophic (NS) sets is presented for fluid segmentation in OCT volumes for exudative age related macular degeneration (EAMD) subjects. The proposed method includes three main steps: 1) The inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) layers are segmented using proposed methods based on graph shortest path in NS domain. A flattened RPE boundary is calculated such that all three types of fluid regions, intra-retinal, sub-retinal and sub-RPE, are located above it. 2) Seed points for fluid (object) and tissue (background) are initialized for graph cut by the proposed automated method. 3) A new cost function is proposed in kernel space, and is minimized with max-flow/min-cut algorithms, leading to a binary segmentation. Important properties of the proposed steps are proven and quantitative performance of each step is analyzed separately. The proposed method is evaluated using a publicly available dataset referred as Optima and a local dataset from the UMN clinic. For fluid segmentation in 2D individual slices, the proposed method outperforms the previously proposed methods by 18%, 21% with respect to the dice coefficient and sensitivity, respectively, on the Optima dataset, and by 16%, 11% and 12% with respect to the dice coefficient, sensitivity and precision, respectively, on the local UMN dataset. Finally, for 3D fluid volume segmentation, the proposed method achieves true positive rate (TPR) and false positive rate (FPR) of 90% and 0.74%, respectively, with a correlation of 95% between automated and expert manual segmentations using linear regression analysis.