Color Mathematical Morphology Research Articles

Three-Dimensional Generalized Discrete Fuzzy Number and Applications in Color Mathematical Morphology

In this paper, the definition of three-dimensional generalized discrete fuzzy number (3-GDFN) is introduced based on the representation theorem of one-dimensional discrete fuzzy number and the similarity measure definition of two 3-GDFNs is given. Then the concept above mentioned is applied to color image representation and color mathematical morphology (CMM) in RGB space. The basic morphology operators, erosion and dilation, are extended to the CMM by defining the total preorder relation based on similarity measure between two 3-GDFNs instead of general vector sorting methods. The corresponding structuring elements in CMM are variable. Finally, the effectiveness and potential of the theoretical results are verified by comparative experiments. The proposed CMM operators are efficiently used in color image processing.

Automatic Reference Color Selection for Adaptive Mathematical Morphology and Application in Image Segmentation.

This paper proposes a novel automatic reference color selection (ARCS) scheme for the adaptive mathematical morphology (MM) method, and is specifically designed for color image segmentation applications. Because of the main advantages of being intuitive and simple, in the past decade, it has contributed to the growing popularity of binary and gray-scale MM processing. However, the MM process typically neglects the details of reference color determination. Applying other ordering methods, which select only black as the reference color for sorting pixels, result in the problem in which the scope of the distance measurement is not optimal. The proposed ARCS scheme is used for determining the ideal reference color for MM and for color image segmentation application. In addition, we use both 1D histogram-based modeling scheme binning from 3D color spaces, such as red-green-blue and hue-saturation-intensity, and 2D color models, such as (H, S), (Cb, Cr), and (I, By). According to the results of the quartile analysis, the threshold determination reacts with less sensitivity to the context variations of the images tested. The experiments focused on color-based image segmentation using the proposed ARCS scheme for color MM processing through a bottom-up scenario. To evaluate the system, four quantitative indices were utilized for an ARCS comparison using advanced segmentation methods in the experiments. The cross validation with different system parameters and a comparison of the morphological gradient operation with different color models are also presented.

Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions

Different texture descriptors are proposed for the automatic classification of skin lesions from dermoscopic images. They are based on color texture analysis obtained from (1) color mathematical morphology (MM) and Kohonen self-organizing maps (SOMs) or (2) local binary patterns (LBPs), computed with the use of local adaptive neighborhoods of the image. Neither of these two approaches needs a previous segmentation process. In the first proposed descriptor, the adaptive neighborhoods are used as structuring elements to carry out adaptive MM operations which are further combined by using Kohonen SOM; this has been compared with a nonadaptive version. In the second one, the adaptive neighborhoods enable geometrical feature maps to be defined, from which LBP histograms are computed. This has also been compared with a classical LBP approach. A receiver operating characteristics analysis of the experimental results shows that the adaptive neighborhood-based LBP approach yields the best results. It outperforms the nonadaptive versions of the proposed descriptors and the dermatologists’ visual predictions.

Color and multiscale texture features from vectorial mathematical morphology

In this paper, we explore an original way to compute texture features for color images in a vector process. Using a dedicated approach for color ordering, we produce a complete framework for color mathematical morphology adapted to human visual system characteristics. Then, morphological multiscale texture features are defined. To understand the texture feature behavior, we present the feature response to basic images variations. Finally, we compare the texture feature performance in front of a classical classification task using Outex database.

Retinal image analysis: preprocessing and feature extraction

Image processing, analysis and computer vision techniques are found today in all fields of medical science. These techniques are especially relevant to modern ophthalmology, a field heavily dependent on visual data. Retinal images are widely used for diagnostic purposes by ophthalmologists. However, these images often need visual enhancement prior to apply a digital analysis for pathological risk or damage detection. In this work we propose the use of an image enhancement technique for the compensation of non-uniform contrast and luminosity distribution in retinal images. We also explore optic nerve head segmentation by means of color mathematical morphology and the use of active contours.