Generation of Retinal Pigmented Epithelium-Like Cells from Pigmented Spheres Differentiated from Bone Marrow Stromal Cell-Derived Neurospheres

BACKGROUND: Retinal degeneration causes blindness, and cell replacement is a potential therapy. The purpose of this study is to formation of pigmented neurospheres in a simple medium, low-cost, high-performance manner over a short period of time while expressing markers of RPE cells and the activation of specific genes of the pigment cells. Also, these neurospheres have the ability to produce a monolayer of retinal pigment epithelium-like cells (RPELC) with the ability of photoreceptor outer segment phagocytosis. METHODS: BMSC were isolated from pigmented hooded male rats and were immunoreactive to BMSC markers, then converted into neurospheres, differentiated into pigmented spheres (PS), and characterized using Retinal pigment epithelium-specific 65 kDa protein (RPE65), Retinaldehyde-binding protein 1 (CRALBP) and orthodenticle homeobox 2 (OTX2) markers by immunocytochemistry, RT-PCR and RT-qPCR. The PS were harvested into RPELC. The functionality of RPELC was evaluated by phagocytosis of fluorescein-labeled photoreceptor outer segment. RESULTS: The BMSC immunophenotype was confirmed by immunostained for fibronectin, CD90, CD166 and CD44. These cells differentiated into osteogenic and lipogenic cells. The generated neurospheres were immunoreactive to nestin and stemness genes. The PS after 7–14 days were positive for RPE65 (92.76–100%), CRALBP (95.21–100%) and OTX2 (94.88–100%), and after 30 days RT-PCR, qPCR revealed increasing in gene expression. The PS formed a single layer of RPELC after cultivation and phagocyte photoreceptor outer segments. CONCLUSION: Bone marrow stromal stem cells can differentiate into functional retinal pigmented epithelium cells in a simple, low-cost, high-performancemanner over a short period of time. These cells due to expressing theRPELCgenes andmarkers can be used in cell replacement therapy for degenerative diseases including age-relatedmacular degeneration as well as retinitis pigmentosa.

Influence of Color-to-Gray Conversion on the Performance of Document Image Binarization: Toward a Novel Optimization Problem.

This paper presents a novel preprocessing method of color-to-gray document image conversion. In contrast to the conventional methods designed for natural images that aim to preserve the contrast between different classes in the converted gray image, the proposed conversion method reduces as much as possible the contrast (i.e., intensity variance) within the text class. It is based on learning a linear filter from a predefined data set of text and background pixels that: 1) when applied to background pixels, minimizes the output response and 2) when applied to text pixels, maximizes the output response, while minimizing the intensity variance within the text class. Our proposed method (called learning-based color-to-gray) is conceived to be used as preprocessing for document image binarization. A data set of 46 historical document images is created and used to evaluate subjectively and objectively the proposed method. The method demonstrates drastically its effectiveness and impact on the performance of state-of-the-art binarization methods. Four other Web-based image data sets are created to evaluate the scalability of the proposed method.

Phase-based binarization of ancient document images: model and applications.

In this paper, a phase-based binarization model for ancient document images is proposed, as well as a postprocessing method that can improve any binarization method and a ground truth generation tool. Three feature maps derived from the phase information of an input document image constitute the core of this binarization model. These features are the maximum moment of phase congruency covariance, a locally weighted mean phase angle, and a phase preserved denoised image. The proposed model consists of three standard steps: 1) preprocessing; 2) main binarization; and 3) postprocessing. In the preprocessing and main binarization steps, the features used are mainly phase derived, while in the postprocessing step, specialized adaptive Gaussian and median filters are considered. One of the outputs of the binarization step, which shows high recall performance, is used in a proposed postprocessing method to improve the performance of other binarization methodologies. Finally, we develop a ground truth generation tool, called PhaseGT, to simplify and speed up the ground truth generation process for ancient document images. The comprehensive experimental results on the DIBCO'09, H-DIBCO'10, DIBCO'11, H-DIBCO'12, DIBCO'13, PHIBD'12, and BICKLEY DIARY data sets show the robustness of the proposed binarization method on various types of degradation and document images.