One of the most important challenges in tissue engineering research is the development of biomimetic materials. In this present study, we have investigated the effect of the titanium dioxide (TiO ) nanoparticles on the properties of electrospun mats of poly (hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), to be used as scaffold. The morphology of electrospun fibers was observed by scanning electron microscopy (SEM). Both pure PHBV and nanocomposites fibers were smooth and uniform. However, there was an increase in fiber diameter with the increase of TiO concentration. Thermal properties of PHBV and nanocomposite mats were characterized by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). DSC analysis showed that the crystallization temperature for PHBV shifts to higher temperature in the presence of the nanoparticles, indicating that TiO nanoparticles change the process of crystallization of PHBV due to heterogeneous nucleation effect. TGA showed that in the presence of the nanoparticles, the curves are shifted to lower temperatures indicating a decreasing in thermal stability of nanocomposites compared to pure PHBV. To produce scaffolds for tissue engineering, it is important to evaluate the biocompatibility of the material. Cytotoxicity assay showed that TiO nanoparticles were not cytotoxic for cells at the concentration used to synthesize the mats. The proliferation of cells on the mats was evaluated by the MTT assay. Results showed that the nanocomposite samples increased cell proliferation compared to the pure PHBV. These results indicate that continuous electrospun fibrous scaffolds may be a good substrate for tissue regeneration.

Introduction: Statistical data reveal that approximately 140 million radiological exams are performed annually in Brazil. These exams are designed to detect and to analyze fractures, caused by different types of trauma; as well as, to diagnose pathologies such as pulmonary diseases. For better visualization of those lesions or abnormalities, methods of image segmentation can be implemented. Such methods lead to the separation of the region of interest, which allows extracting the characteristics and anomalies of the desired tissue. However, the methods developed by researchers in this area still have restrictions. Consequently, we present an automatic pulmonary segmentation approach that overcomes these constraints. Methods: This method is composed of a combination of Discrete Wavelet Packet Frame (DWPF), morphological operations and Gradient Vector Flow (GVF). The methodology is divided into four steps: Pre-processing -the original image is enhanced by discrete wavelet; Processing -where occurs a combination of the Otsu threshold with a series of morphological operations in order to identify the pulmonary object; Post-processing -an innovative form of using GVF improves the binary information of pulmonary tissue, and; Evaluation -the segmented images were evaluated for accuracy of detection the pulmonary region and border. Results: The evaluation was carried out by segmenting 247 digital X-ray challenging images of the thorax human. The results show high for values of Overlap (97,63% ± 3.34%), and Average Contour Distance (0.69mm ± 0.95mm). Conclusion:The results allow verifying that the proposed technique is robust and more accurate than other methods of lung segmentation, besides being a fully automatic method of lung segmentation. This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.How to cite this article: Vital DA, Sais BT, Moraes MC. Robust pulmonary segmentation for chest radiography, combining enhancement, adaptive morphology and innovative active contours Res Biomed Eng. 2018; 34(3):234-245.

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Introduction Magnetic resonance imaging (MRI) is the most used medical modality for diagnosis and monitoring of multiple sclerosis (MS). A segmentation process is an important task to quantify lesion and its progression. However, manual segmentation of 3D images is tedious, time-consuming, and often not reproducible. The state of the art presents results with room for improvements. Consequently, a semiautomatic segmentation process is proposed and described in this study. Methods The method consists on a 3D segmentation semiautomatic process for MS lesions in MRI. It initiates by firstly carrying out a preprocessing stage; thus, contrast adjustment is applied to enhance sclerosis regions from other brain information. Secondly, a feature extraction block based on fuzzy connectedness is performed so as to isolate sclerosis lesions from other brain regions. Finally, 3D brain reconstruction is executed along with sclerosis to provide a useful 3D information. Results The robustness of this approach is demonstrated by high correlation between the results and their corresponding gold standard. The results were also obtained by computing parameters of accuracy of image segmentation, as well as overlap Dice. The proposed method reached true positive of 75.61%, false positive of 16.37%, and DICE of 78.23%. Conclusion The high correlation between specialist and proposed approach outcome, a better monitoring of the disease, is provided; the specialist can understand the patient’s symptoms, thereby increasing the patient’s quality of life.

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