Significance: A growing body of research supports the significant role of cerebrovascular abnormalities in neurological disorders. As these insights develop, standardized tools for unbiased and high-throughput quantification of cerebrovascular structure are needed.
Aim: We provide a detailed protocol for performing immunofluorescent labeling of mouse brain vessels, using thin (25 μm) or thick (50 to 150 μm) tissue sections, followed respectively by two- or three-dimensional (2D or 3D) unbiased quantification of vessel density, branching, and tortuosity using digital image processing algorithms.
Approach: Mouse brain sections were immunofluorescently labeled using a highly selective antibody raised against mouse Cluster of Differentiation-31 (CD31), and 2D or 3D microscopy images of the mouse brain vasculature were obtained using optical sectioning. An open-source toolbox, called Pyvane, was developed for analyzing the imaged vascular networks. The toolbox can be used to identify the vasculature, generate the medial axes of blood vessels, represent the vascular network as a graph, and calculate relevant measurements regarding vascular morphology.
Results: Using Pyvane, vascular parameters such as endothelial network density, number of branching points, and tortuosity are quantified from 2D and 3D immunofluorescence micrographs.
Conclusions: The steps described in this protocol are simple to follow and allow for reproducible and unbiased analysis of mouse brain vascular structure. Such a procedure can be applied to the broader field of vascular biology.
Image processing tools have been widely used in systems supporting medical diagnosis. The use of mobile devices for the diagnosis of melanoma can assist doctors and improve their diagnosis of a melanocytic lesion. This study proposes a method of image analysis for melanoma discrimination from other types of melanocytic lesions, such as regular and atypical nevi. The process is based on extracting features related with asymmetry and border irregularity. It were collected 104 images, from medical database of two years. The images were obtained with standard digital cameras without lighting and scale control. Metrics relating to the characteristics of shape, asymmetry and curvature of the contour were extracted from segmented images. Linear Discriminant Analysis was performed for dimensionality reduction and data visualization. Segmentation results showed good efficiency in the process, with approximately 88:5% accuracy. Validation results presents sensibility and specificity 85% and 70% for melanoma detection, respectively.
This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks: one pixel as a node. Node degree and clustering coefficient, using with traditional and extended hierarchical measurements, are used to characterize "organisation" of textures.
Texture analysis represents one of the main areas in image processing and computer vision. The current article describes how complex networks have been used in order to represent and characterized textures. More specifically, networks are derived from the texture images by expressing pixels as network nodes and similarities between pixels as network edges. Then, measurements such as the node degree, strengths and clustering coefficient are used in order to quantify properties of the connectivity and topology of the analyzed networks. Because such properties are directly related to the structure of the respective texture images, they can be used as features for characterizing and classifying textures. The latter possibility is illustrated with respect to images of textures, DNA chaos game, and faces. The possibility of using the network representations as a subsidy for DNA characterization is also discussed in this work.
Texture characterization and classification remains an important issue in image processing and analysis. Much attention has been focused on methods involving spectral analysis and co-occurrence matrix, as well as more modern approaches such as those involving fractal dimension, entropy and criteria based in multiresolution. The present work addresses the problem of texture characterization in terms of complex networks: image pixels are represented as nodes and similarities between such pixels are mapped as links between the network nodes. It is verified that several types of textures present node degree distributions which are far distinct from those observed for random networks, suggesting complex organization of those textures. Traditional measurements of the network connectivity, including their respective hierarchical extensions, are then applied in order to obtain feature vectors from which the textures can be characterized and classified. The performance of such an approach is compared to co-occurrence methods, suggesting promising complementary perspectives.
KEYWORDS: 3D image processing, Cameras, Sensors, 3D image reconstruction, Image sensors, Point spread functions, 3D modeling, 3D acquisition, Imaging systems, Structured light
The 3D reconstruction of a scene with 2D images requires several scene acquisitions or the use of specific lighting (structured light). The solution choice depends on the scene to be reconstructed. Unfortunately in the case of face reconstruction, health standards forbid the use of structured light; the elaboration of a multi-sensor system, such as stereovision is then required. In this paper, primary results about depth reconstruction from defocused images are presented. The used method relies on inverse ray tracing and provides results better than those obtained by using the conventional gradient based method.
A spatially congruent new model for the striate visual cortex (SVC) is proposed which accounts for some of the known functional and organizational properties of the superior mammalian SVC. Even though there is a broad consensus that the topographical representation of the visual field is one of the principal structuring principles underlying the SVC organization, the orientation maps in the SVC have often been described as non-topographical maps. In the present model, the adopted foot-of-normal representation of straight lines has allowed full congruency between the visual field topographic map and the orientation maps in the SVC. The proposed computational model includes three neural layers and assumes that the ocular dominance columns are already established at birth; three possibilities of neural mechanisms leading to orientation encoding are outlined and discussed. The model provides reasonable explanation to some of the most intriguing recently verified properties of the SVC such as the increased neural activity at the cytochrome oxidase blobs, the reduced orientation selectivity at these same places, and the pinwheel-like organization of the orientation selectivity in the SVC.
The Binary Hough Transform (BHT) is a variation of the standard Hough transform for line detection with
slope/intercept parameterization which, for image and accumulator arrays whose dimensions are integer powers of two,
needs only additions and binary shifts during its calculation, allows full precision for the representation of the parameters
and uses integer arithmetic without rounding errors. This paper presents the BHT and its implementation in hardware
(two systolic array architectures and their combination) and software (a sequential algorithm).
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