In this paper a class of analog algorithms based on the concept of Cellular Neural Network (CNN) is applied in some processing operations of some important medical images, namely retina images, for detecting various symptoms connected with diabetic retinopathy. Some specific processing tasks like morphological operations, linear filtering and thresholding are proposed, the corresponding templat...

While shock filters are popular morphological image enhancement methods, no well-posedness theory is available for their corresponding partial differential equations (PDEs). By analysing the dynamical system of ordinary differential equations that results from a space discretisation of a PDE for 1-D shock filtering, we derive an analytical solution and prove well-posedness. Finally we show that...

Mathematical Morphology, partially ordered sets, complete semilattices, signal processing, image processing In this paper, we prove that several key signal processing tasks (linear filtering, quantization, decimation, JPEG coding, and others) are particular cases of morphological operators. This is obtained as a consequence of the recent extension of MM's framework, from complete lattices to co...

The road network detection is an emerging area in information extraction from high-resolution satellite images (HRSI). The proposed work in this paper consists of three steps: Pre-filtering, connected component-based operations, and removal of unwanted non-road pixels using morphological operations. The proposed algorithm is experimented on various satellite images and the performance of the sy...

We study hierarchical segmentations that are optimal in the sense of minimal spanning forests of the original image. We introduce a region-merging operation called uprooting, and we prove that optimal hierarchical segmentations are equivalent to the ones given by uprooting a watershed-cut based segmentation. Based on those theoretical results, we propose an efficient algorithm to compute such h...

We propose a method to handle the automatic detection of PET lesions based on asymmetry feature measurement after segmenting the PET images. It is an essentially initial step in automatic diagnosis and content-based retrieval applications. Our technique includes six steps: Image alignment, segmentation, image reflection & subtraction, thresholding, background removal using morphological filteri...

This paper deals with the roles of assumption, mathematical models, and experimental control in Pattern Recognition. In order to base the discussion on actual examples, three studies are first described. The first one presents filtering of bird singings via their sonograms. The second and the third ones deal with morphological operators on the unit circle, which are successively applied to hue ...

Monitoring the shape changes of the optic disk is crucial for the early detection of glaucoma. This requires the optic disk to be segmented from the surrounding fundus. Previous methods required the boundary to be identified manually, for example, by specifying four control points on the boundary. This is not practical for routine clinical analysis, intra-observer and inter-observer variations ...

We propose to apply our recently developed frame-based framework for group-invariant morphology to the problem of tensor field morphology. Group invariance (and particularly rotation invariance) have been, and are, motivated to be relevant for filtering tensor fields. This leads to the development of a rotation-invariant frame for tensors, which can be used to easily define rotation-invariant m...

The paper proposes soft morphological operators and algorithms to remove noise from binary images. The features of the algorithm are the presence of additional parameters, such as the threshold of filtering and number of iterations, and the ability to remove noise: islands, pin holes, mousebites and spurs in one pass. Application of these algorithms can bring less distortion during operation, e...