André Vital Saúde

Morphological Image Processing

Morphological processing (MP) has applications in diverse areas of image processing as filtering, segmentation, and pattern recognition, to both binary and grayscale images. One of the most important operations in morphological image processing is reconstruction from markers. The basic idea is to mark certain image components and then to reconstruct that portion of the image that contains the marked components. The basic fitting operation of morphology is the erosion of an image by a structuring element. Erosion is done by scanning the image with the structuring element. When the structuring element fits completely inside the object, the probe position is marked. The erosion result consists of all scanning locations, where the structuring element fits inside the object. The eroded image is usually a shrunken version of the image, and the shrinking effect is controlled by the structuring element size and shape. As an extension of the binary case, grayscale opening (closing) can be achieved simply by threshold decomposition, followed by binary opening (closing) and stack reconstruction. Grayscale opening and closing have the same properties as their binary equivalents.

Exact euclidean medial axis in higher resolution

The notion of skeleton plays a major role in shape analysis. Some usually desirable characteristics of a skeleton are: sufficient for the reconstruction of the original object, centered, thin and homotopic. The Euclidean Medial Axis presents all these characteristics in a continuous framework. In the discrete case, the Exact Euclidean Medial Axis (MA) is also sufficient for reconstruction and centered. It no longer preserves homotopy but it can be combined with a homotopic thinning to generate homotopic skeletons. The thinness of the MA, however, may be discussed. In this paper we present the definition of the Exact Euclidean Medial Axis on Higher Resolution which has the same properties as the MA but with a better thinness characteristic, against the price of rising resolution. We provide an efficient algorithm to compute it.

Discrete 2D and 3D euclidean medial axis in higher resolution

The notion of skeleton plays a major role in shape analysis. Some usually desirable characteristics of a skeleton are: centered, thin, homotopic, and sufficient for the reconstruction of the original object. The Euclidean medial axis presents all these characteristics in a continuous framework. In the discrete case, the exact Euclidean medial axis (MA) is also sufficient for reconstruction and centered. It no longer preserves homotopy but it can be combined with a homotopic thinning to generate homotopic skeletons. The thinness of the MA, however, may be discussed. In this paper, we present the definition of the exact Euclidean medial axis in higher resolution, which has the same properties as the MA but with a better thinness characteristic, against the price of rising resolution. We provide and prove an efficient algorithm to compute it.

On Generalized Differences for Biospeckle Image Analysis

Biospeckle is a technique whose purpose is to observe and study the underlying activity of some material. The technique has its roots on optical physics, and its first step is an image acquisition process that produces a video sequence whose characteristics allow researchers to have an interpretation of the activity of the observed material by an analysis of the video content. The recent literature on this subject presents several different measurements for analyzing the video sequence. One of the most popular measurement is the Generalized Difference (GD). The computation of the GD has an asymptotic complexity of O(n2). In this paper we propose: i) an alternative O(n) algorithm for the computation of the GD, and ii) an alternative measurement, that we call GD*. We discuss the qualitative similarities between the GD and the GD*. We conclude that the GD* is an alternative generalized difference measurement, and thus it can replace the GD in many applications. We show that the GD* is a function of the variance, and it can be computed in O(n). Finally, if the GD itself is desired as measurement, it can now be computed in O(n) by the novel algorithm presented in this paper.

Morphological Image Processing Applied in Biomedicine

This chapter presents the main concepts of morphological image processing. Mathematical morphology has application in diverse areas of image processing such as filtering, segmentation and pattern recognition, applied both to binary and gray-scale images. Section 4.2 addresses the basic binary morphological operations: erosion, dilation, opening and closing. We also present applications of the primary operators, paying particular attention to morphological reconstruction because of its importance and since it is still not widely known. In Sect. 4.3, the same concepts are extended to gray-scale images. Section 4.4 is devoted to watershed-based segmentation. There are many variants of the watershed transform. We introduce the watershed principles with real-world applications. The key to successful segmentation is the design of the marker to eliminate the over-segmentation problem. Finally, Sect. 4.5 presents the multi-scale watershed to segment brain structures from diffusion tensor imaging, a relatively recent imaging modality that is based on magnetic resonance.

New Higher-Resolution Discrete Euclidean Medial Axis in nD with Linear Time Parallel Algorithm

The notion of skeleton plays a major role in shape analysis since the introduction of the medial axis. The continuous medial axis is a skeleton with the following characteristics: centered, thin, homotopic, and reversible (sufficient for the reconstruction of the original object). The discrete Euclidean medial axis (MA) is also reversible and centered, but no longer homotopic nor thin. To preserve topology and reversibility, the MA is usually combined with homotopic thinning algorithms. Since there is a robust and well defined framework for fast homotopic thinning defined in the domain of abstract complexes, some authors have extended the MA to a doubled resolution grid and defined the discrete Euclidean Medial Axis in Higher Resolution (HMA), which can be combined to the framework defined on abstract complexes. Other authors gave an alternative definition of medial axis, which is a reversible subset of the MA, and is called Reduced Discrete Medial Axis (RDMA). The RDMA is thinner than the MA and can be computed in optimal time. In this paper we extend the RDMA to the doubled resolution grid and we define the High-resolution RDMA (HRDMA). The HRDMA is reversible and it can be computed in optimal time. The HRDMA can be combined with the algorithms in abstract complexes, so a reversible and homotopic Euclidean skeleton can be computed in optimal time.

Adessowiki: Collaborative Scientific Programming Environment

Adessowiki is a collaborative environment for teaching and research in image processing. Adessowiki is composed of a collection of collaborative web pages in the form of a wiki. The articles of this wiki can embed programming code that will be executed on the server when the page is rendered, incorporating the results as figures, texts and tables on the document. The integrated collaborative environment of Adessowiki, containing documentation, programming code and execution results is able to create several possibilities of applications. This paper presents some of the applications where Adessowiki has been used, such as Scientific Writing and Virtual Learning Environment.