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The analysis of different representation levels has been largely used in several image analysis tasks to handle the multiscale nature of image data, allowing the extraction of specific features that become explicit at each scale. In this work, we explore the scale-space properties of a self-dual toggle operator defined on a scaled morphological framework. These properties conduce to a well-controlled image simplification where its maxima and minima interact at the same time during pixels' transformation, in contrast to other approaches that consider these extrema separately. In such a way, it is possible to identify significant image extrema information to be used in several high level tasks. To assess the robustness of our approach, we carry out tests on images of several classes and subjected to different lighting conditions for various applications, including segmentation and binarization.

Infrared imagery is extensively used in defense, remote sensing and medical applications. While the infrared images have many advantages over RGB images, the details in these images are usually blurred which in turn leads to some difficulties for human operators. In this paper, a new method based on Laplacian of Gaussian scale-space and local variance is presented to improve the visual quality of the infrared images. At the first step, the Gaussian scale-space is constructed by convolving the original image with different Gaussian kernels. Then, the two-dimensional Laplacian kernels are convolved with the Gaussian scale-space to achieve details with both positive as well as negative contrasts. The weighted details are added to the original image to deblur the dim areas. At the final step, to increase the dynamic range of the image and have better visual quality, the local variance of the image is also added to the output of the previous step. Since finding optimum weighting coefficients is a difficult task empirically, here, we use a population-based meta-heuristic optimization algorithm called particle swarm optimization (PSO) to find the optimum values for weighting coefficient values. Beside qualitative comparison, Structural Similarity (SSIM) and second-derivative-like measure of enhancement (SDME) are used to quantitatively investigate the images quality. The proposed method outperforms the baseline algorithms in both qualitative and quantitative perspectives.

Liver diseases are a common medical problem, especially amongst the population of developing countries. Magnetic Resonance Cholangio Pancreatography (MRCP) has become the popular non-invasive, non-ionizing examination for analysis of the hepatobiliary structure in the liver. Unfortunately, conventional 2D MRCP images can be difficult to analyze for biliary tree anomalies, especially with volume effect, artefacts and noise present in these images, whilst good 3D MRI systems are costly for less affluent nations. This paper proposes a scale-space multi-resolution approach to a segment-based implementation of the popular region growing algorithm, to identify the hierarchical structure of the biliary tree in conventional 2D MRCP images. Results obtained are promising in aiding automatic processing of these images to assist medical practitioners in analyzing the biliary tract more efficiently. Application of the algorithm may be extended for telemedicine.

In this paper, we present an algorithm to automatically detect meaningful modes in a histogram. The proposed method is based on the behavior of local minima in a scale-space representation. We show that the detection of such meaningful modes is equivalent in a two classes clustering problem on the length of minima scale-space curves. The algorithm is easy to implement, fast and does not require any parameter. We present several results on histogram and spectrum segmentation, grayscale image segmentation and color image reduction.