Similarity measurements between spectral textures

6 Mai 2024

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Catégorie : Doctorant

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Job description

This PhD thesis focuses on spectral imaging (color, multi- and hyper-spectral). We are interested in the study of the texture of this type of image and its link with human perception [Cha19]. At present, texture processing and analysis is mainly carried out in grayscale. We want to work jointly on texture combined to spectral dimension.

Texture feature extraction is divided into four main categories. The transform features are based on the Fourier transform and its derivatives [Cog85]. The structural features rely on textons (the pattern that defines the texture). It is the family of the Local Binary Pattern ([Oja94]). The model-based features assume a model for the texture such as fractals [Pen84] or Markov random fields [Cro83]. The last category is based on statistical features with the analysis of probabilistic characteristics linked to texture [Har73].

The aim of this thesis is to propose a distance measure between multivalued textures. To achieve this, spectral texture features will have to be developed that combine both image's spectral and spatial dimensions [Chu21]. We will consider the spectral texture as a joint probability between the spectral and the spatial distributions of the spectra responses. To measure the distance between features, we need to consider the nature of the data. Depending on which category of features you are in, one should not use any distance measure. The Minkowski distance should be used for orthogonal data, while finite state distance (Hamming distance) is adapted to finite state data (usually binary). When dealing with statistical features, the use of probabilistic measured is needed [Ric16]. Nowadays, metric learning is used a lot [Kay19]. Several measures of distance between texture features will be proposed. A validation process will have to be defined. These measures will need to be validated according to the application.

In particular, this work will be applied to endoscopy images in order to offer physicians a richer perception of mucosal structure [Kre19]. The research team has an extensive background in medical endoscopy image analysis. Thanks to its collaborations, it has access to spectral videos, particularly in relation to inflammatory diseases of the digestive system. The inflammatory process induces various tissue modifications that can be investigated by several existing techniques during digestive endoscopy. Dye-free digital chromoendoscopy, or virtual chromoendoscopy, is based on the illumination of the mucosa with a specific light to recognize changes in mucosal structure. The very recent emergence on the market of endoscopic systems for highlighting textural features demonstrates the interest of such features in addition to spectral and shape one [Sat21], [Sug22]. In the context of the PhD, we will work on the interpretability of multivalued textures to characterize inflammatory lesions in relation to the physician's perception.

As well as texture features, a key element we would like to develop is the uncertainty associated to feature extraction. Indeed, the uncertainties due to acquisition are not the focus of this work. Yet, those coming from the computation side can be extracted and must be calculated to add value to the result presented to the physician. To estimate them, the model error will be measured such as a distance between the approached probability and the true one.

Contact

franck.marzani@u-bourgogne.fr