Morphological Residues And A General Framework For Image Filtering And Segmentation

Morphological residues represent an image in a hierarchical way by means of a decomposition of its structures and according to a size parameter λ. From this decomposition, we can obtain a relation between the different residual levels associated with the complexity of the image structures. In this work, we introduce a new method to filter out components of gray-scale images based on the morphological residue decomposition which takes into account a size parameter and a certain level of complexity of the different structures to be filtered. As we will illustrate, this complexity is associated with a set of new attributes of the image defined according to the information contained in its multi-resolution representation.20014219229Serra, J., (1982) Image Analysis and Mathematical Morphology, 1. , Academic PressSerra, J., (1988) Image Analysis and Mathematical Morphology: Theoretical Advances, 2. , Academic PressHeijmans, H.J.A.M., (1994) Morphological Image Operators, , Academic Press, Boston, MABreen, E.J., Jones, R., Attribute openings, thinnings, and granulometries (1996) Computer Vision and Image Processing, 64 (3), pp. 377-389Rosenfeld, A., Kak, A.C., (1982) Digital Picture Processing, 1. , Academic Press, 2nd editionGonzalez, R.C., Woods, R.E., (1993) Digital Image Processing, , Addison-WesleyVincent, L., Grayscale area opennings and closings, their efficient implementation and applications (1993) Mathematical Morphology and Its Applications to Signal Processing, pp. 22-27. , J. Serra and P. Salembier, Eds., UPC Publications, MayVachier, C., (1995) Extraction de caractéristiques, segmentation d'image et morphologie mathématique, , Ph.D. thesis, École Nationale des Mines de Paris, DecemberHaralick, R.M., Shapiro, L.G., Image segmentation techniques (1985) Computer, Vision, Graphics and Image Processing, 35, pp. 100-132Vincent, L., Soillet, P., Watersheds in digital spaces: An efficient algorithm based on immersion simmulations (1991) IEEE Trans. on Pattern Analysis and Machine Intelligence, 13 (6), pp. 583-598Beucher, S., Yu, X., Road recognition in complex traffic situations (1994) 7th IFAC/IFORS Simposium on Transportation Systems: Theory and Application of Advanced Technology, pp. 413-418. , Tianjin, China, AugustBeucher, S., Meyer, F., The morphological approach to segmentation: The watershed transformation (1993) Mathematical Morphology in Image Processing, 34, pp. 433-481. , Edward R. Dougherty, Ed., chapter 12, Marcel Dekker, New YorkGoutsias, J., Heijmans, H.J.A.M., (1997) Multiresolution signal decomposition schemes. Part 1: Linear and morphological pyramids, , Tech. Rep., Center of Imaging Science and Department of Electric and Computer EngineeringMatheron, G., (1975) Random Sets and Integral Geometry, , John Wiley, New YorkMatheron, G., (1967) Eléments pour une Théorie des Milieux Poreux, , MassonParis, ParisVincent, L., Fast opening functions and morphological granulometries (1994) SPIE Image Algebra and Morphological Image Processing V, 2300, pp. 253-267. , San Diego, CA, JulyTang, X., Vincent, L., Stewart, K., Automatic plankton image classification (1996) International Artificial Intelligence Review JournalDougherty, E., Pelz, J., Sand, F., Lent, A., Morphological image segmentation by local granulometric size distributions (1992) Journal of Electronic Imaging, 1 (1), pp. 46-60Regazzoni, C., Foresti, G., Venetsanopoulos, A., Statistical pattern spectrum for binary pattern recognition (1994) Mathematical Morphology and Its Applications to Image Processing, pp. 185-192. , Jean Serra and Pierre Soile, Eds., Computational Imaging and Vision, Kluwer Academic Publishers, The NetherlandsVincent, L., Local grayscale granulometries based on opening trees (1996) Mathematical Morphology and Its Applications to Image Signal and Processing, pp. 273-280. , Petro Maragos, Ronald W. Schafer, and Muhammad Akmal Butt, Eds., Computational Image and Vision, Kluwer Academic Publishers, The NetherlandsVincent, L., Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms (1993) IEEE Trans. Image Processing, 2 (2), pp. 176-201Serra, J., Salembier, P., Connected operators and pyramids (1993) Proceeding of SPIE Image Algebra and Mathematical Morphology, 93, pp. 164-175. , FebruarySalembier, P., Oliveiras, A., Garrido, L., Antiextensive connected operators for image and sequence processing (1998) IEEE Trans. Image Processing, 7 (4), pp. 555-57

Hierarchical morphological segmentation for image sequence coding

This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then, the segmentation quality is improved by introducing regions corresponding to more local information. The algorithm, considering sequences as being functions on a 3-D space, directly segments 3-D regions. A 3-D approach is used to get a segmentation that is stable in time and to directly solve the region correspondence problem. Each segmentation stage relies on four basic steps: simplification, marker extraction, decision, and quality estimation. The simplification removes information from the sequence to make it easier to segment. Morphological filters based on partial reconstruction are proven to be very efficient for this purpose, especially in the case of sequences. The marker extraction identifies the presence of homogeneous 3-D regions. It is based on constrained flat region labeling and morphological contrast extraction. The goal of the decision is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a modified watershed algorithm. Finally, the quality estimation concentrates on the coding residue, all the information about the 3-D regions that have not been properly segmented and therefore coded. The procedure allows the introduction of the texture and contour coding schemes within the segmentation algorithm. The coding residue is transmitted to the next segmentation stage to improve the segmentation and coding quality. Finally, segmentation and coding examples are presented to show the validity and interest of the coding approach.Peer ReviewedPostprint (published version

General Adaptive Neighborhood Image Processing for Biomedical Applications

In biomedical imaging, the image processing techniques using spatially invariant transformations, with fixed operational windows, give efficient and compact computing structures, with the conventional separation between data and operations. Nevertheless, these operators have several strong drawbacks, such as removing significant details, changing some meaningful parts of large objects, and creating artificial patterns. This kind of approaches is generally not sufficiently relevant for helping the biomedical professionals to perform accurate diagnosis and therapy by using image processing techniques. Alternative approaches addressing context-dependent processing have been proposed with the introduction of spatially-adaptive operators (Bouannaya and Schonfeld, 2008; Ciuc et al., 2000; Gordon and Rangayyan, 1984;Maragos and Vachier, 2009; Roerdink, 2009; Salembier, 1992), where the adaptive concept results from the spatial adjustment of the sliding operational window. A spatially-adaptive image processing approach implies that operators will no longer be spatially invariant, but must vary over the whole image with adaptive windows, taking locally into account the image context by involving the geometrical, morphological or radiometric aspects. Nevertheless, most of the adaptive approaches require a priori or extrinsic informations on the image for efficient processing and analysis. An original approach, called General Adaptive Neighborhood Image Processing (GANIP), has been introduced and applied in the past few years by Debayle & Pinoli (2006a;b); Pinoli and Debayle (2007). This approach allows the building of multiscale and spatially adaptive image processing transforms using context-dependent intrinsic operational windows. With the help of a specified analyzing criterion (such as luminance, contrast, ...) and of the General Linear Image Processing (GLIP) (Oppenheim, 1967; Pinoli, 1997a), such transforms perform a more significant spatial and radiometric analysis. Indeed, they take intrinsically into account the local radiometric, morphological or geometrical characteristics of an image, and are consistent with the physical (transmitted or reflected light or electromagnetic radiation) and/or physiological (human visual perception) settings underlying the image formation processes. The proposed GAN-based transforms are very useful and outperforms several classical or modern techniques (Gonzalez and Woods, 2008) - such as linear spatial transforms, frequency noise filtering, anisotropic diffusion, thresholding, region-based transforms - used for image filtering and segmentation (Debayle and Pinoli, 2006b; 2009a; Pinoli and Debayle, 2007). This book chapter aims to first expose the fundamentals of the GANIP approach (Section 2) by introducing the GLIP frameworks, the General Adaptive Neighborhood (GAN) sets and two kinds of GAN-based image transforms: the GAN morphological filters and the GAN Choquet filters. Thereafter in Section 3, several GANIP processes are illustrated in the fields of image restoration, image enhancement and image segmentation on practical biomedical application examples. Finally, Section 4 gives some conclusions and prospects of the proposed GANIP approach

General Adaptive Neighborhood Image Processing. Part II: Practical Applications Issues

23 pagesInternational audienceThe so-called General Adaptive Neighborhood Image Processing (GANIP) approach is presented in a two parts paper dealing respectively with its theoretical and practical aspects. The General Adaptive Neighborhood (GAN) paradigm, theoretically introduced in Part I [20], allows the building of new image processing transformations using context-dependent analysis. With the help of a specified analyzing criterion, such transformations perform a more significant spatial analysis, taking intrinsically into account the local radiometric, morphological or geometrical characteristics of the image. Moreover they are consistent with the physical and/or physiological settings of the image to be processed, using general linear image processing frameworks. In this paper, the GANIP approach is more particularly studied in the context of Mathematical Morphology (MM). The structuring elements, required for MM, are substituted by GAN-based structuring elements, fitting to the local contextual details of the studied image. The resulting morphological operators perform a really spatiallyadaptive image processing and notably, in several important and practical cases, are connected, which is a great advantage compared to the usual ones that fail to this property. Several GANIP-based results are here exposed and discussed in image filtering, image segmentation, and image enhancement. In order to evaluate the proposed approach, a comparative study is as far as possible proposed between the adaptive and usual morphological operators. Moreover, the interests to work with the Logarithmic Image Processing framework and with the 'contrast' criterion are shown through practical application examples

Ultimate opening and gradual transitions

ISBN: 978-364221568-1International audienceUltimate Opening (UO) is a powerful operator based on numerical residues. In a multi-scale framework, it analyzes an image under a series of increasing openings. Contrasted objects are detected when they are filtered out by an opening, generating an important residue. Gradual transitions make this operator underestimate the contrast of blurred objects. In this paper we propose a solution to this problem, integrating series of non-null residues. The resulting operator handles correctly blurred boundaries, without modifying the behavior on sharp transitions

Coronary Artery Tracking in 3D Cardiac CT Images Using Local Morphological Reconstruction Operators

Automatic segmentation and tracking of the coronary artery tree from Cardiac Multislice-CT images is an important goal to improve the diagnosis and treatment of coronary artery disease. This paper presents a semi-automatic algorithm (one input point per vessel) based on morphological grayscale local reconstructions in 3D images devoted to the extraction of the coronary artery tree. The algorithm has been evaluated in the framework of the Coronary Artery Tracking Challenge 2008 [1], obtaining consistent results in overlapping measurements (a mean of 70% of the vessel well tracked). Poor results in accuracy measurements suggest that future work should refine the centerline extraction. The algorithm can be efficiently implemented and its general strategy can be easily extrapolated to a completely automated centerline extraction or to a user interactive vessel extractio

Scanning electron microscopy image representativeness: morphological data on nanoparticles.

A sample of a nanomaterial contains a distribution of nanoparticles of various shapes and/or sizes. A scanning electron microscopy image of such a sample often captures only a fragment of the morphological variety present in the sample. In order to quantitatively analyse the sample using scanning electron microscope digital images, and, in particular, to derive numerical representations of the sample morphology, image content has to be assessed. In this work, we present a framework for extracting morphological information contained in scanning electron microscopy images using computer vision algorithms, and for converting them into numerical particle descriptors. We explore the concept of image representativeness and provide a set of protocols for selecting optimal scanning electron microscopy images as well as determining the smallest representative image set for each of the morphological features. We demonstrate the practical aspects of our methodology by investigating tricalcium phosphate, Ca3 (PO4 )2 , and calcium hydroxyphosphate, Ca5 (PO4 )3 (OH), both naturally occurring minerals with a wide range of biomedical applications

Classification of hyperspectral images by tensor modeling and additive morphological decomposition

International audiencePixel-wise classification in high-dimensional multivariate images is investigated. The proposed method deals with the joint use of spectral and spatial information provided in hyperspectral images. Additive morphological decomposition (AMD) based on morphological operators is proposed. AMD defines a scale-space decomposition for multivariate images without any loss of information. AMD is modeled as a tensor structure and tensor principal components analysis is compared as dimensional reduction algorithm versus classic approach. Experimental comparison shows that the proposed algorithm can provide better performance for the pixel classification of hyperspectral image than many other well-known techniques

Morphological tools for spatial and multiscale analysis of passive microwave remote sensing data

International audienceEarth Observation through microwave radiometry is particularly useful for various applications, e.g., soil moisture, ocean salinity, or sea ice cover. However, most of the image processing/data analysis techniques aiming to provide automatic measurement from remote sensing data do not rely on any spatial information, similarly to the early years of opti-cal/hyperspectral remote sensing. After more than a decade of research, it has been observed that spatial information can very significantly improve the accuracy of land use/land cover maps. In this context, the goal of this paper is to propose a few insights on how spatial information can benefit to (passive) microwave remote sensing. To do so, we focus here on mathematical morphology and provide some illustrative examples where morphological operators can improve the processing and analysis of microwave radiometric information. Such tools had great influence on multispectral/hyperspectral remote sensing in the past, and are expected to have a similar impact in the microwave field in the future, with the launch of upcoming missions with improved spatial resolution, e.g. SMOS-NEXT