Analyse d'images Terahertz

Cette thèse à publications présente toutes nos contributions qui se rapportent à la segmentation d’images Térahertz. La thèse comprend quatre chapitres. Les deux premiers chapitres introduisent deux nouvelles approches de segmentation basées sur des techniques d'échantillonnage. Dans la première approche, nous formulons la technique de classification K-means dans le cadre de l'échantillon d'ensembles ordonnés pour surmonter le problème d'initialisation des centres. Le deuxième chapitre aborde la sélection des données à travers la pondération de caractéristiques et l'échantillonnage aléatoire simple pour la classification des pixels en vue d'une segmentation des images Térahertz. Une estimation automatique de la taille de l'échantillon aléatoire et du nombre de caractéristiques sélectionnées sont également proposés. Les deux chapitres suivants introduisent une autre famille de techniques de classification des séries chronologiques basées sur la régression et qui sont adaptées aux séries chronologiques. Nous supposons que les valeurs associées à chaque pixel d'une image Térahertz sont échantillonnées à partir d'un modèle autorégressif. La segmentation de l'image est alors vue comme un problème de classification de séries chronologiques. Ainsi, dans le troisième chapitre, la classification est formulée comme un problème d'optimisation non-linéaire. L'ordre du modèle et le nombre de classes sont estimés en utilisant un critère généralisé d'information. Finalement, le quatrième chapitre est une généralisation des résultats obtenus dans le troisième chapitre. Au lieu de considérer un problème de moindres carrés, nous proposons une approche de classification probabiliste basée sur le mélange de modèles autorégressifs. Les paramètres de l'approche proposée sont automatiquement estimés en utilisant un critère généralisé d'information

Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging

Breast conserving surgery (BCS) is a common breast cancer treatment option, in which the cancerous tissue is excised while leaving most of the healthy breast tissue intact. The lack of in-situ margin evaluation unfortunately results in a re-excision rate of 20-30% for this type of procedure. This study aims to design statistical and machine learning segmentation algorithms for the detection of breast cancer in BCS by using terahertz (THz) imaging. Given the material characterization properties of the non-ionizing radiation in the THz range, we intend to employ the responses from the THz system to identify healthy and cancerous breast tissue in BCS samples. In particular, this dissertation covers the description of four segmentation algorithms for the detection of breast cancer in THz imaging. We first explore the performance of one-dimensional (1D) Gaussian mixture and t-mixture models with Markov chain Monte Carlo (MCMC). Second, we propose a novel low-dimension ordered orthogonal projection (LOOP) algorithm for the dimension reduction of the THz information through a modified Gram-Schmidt process. Once the key features within the THz waveform have been detected by LOOP, the segmentation algorithm employs a multivariate Gaussian mixture model with MCMC and expectation maximization (EM). Third, we explore the spatial information of each pixel within the THz image through a Markov random field (MRF) approach. Finally, we introduce a supervised multinomial probit regression algorithm with polynomial and kernel data representations. For evaluation purposes, this study makes use of fresh and formalin-fixed paraffin-embedded (FFPE) heterogeneous human and mice tissue models for the quantitative assessment of the segmentation performance in terms of receiver operating characteristics (ROC) curves. Overall, the experimental results demonstrate that the proposed approaches represent a promising technique for tissue segmentation within THz images of freshly excised breast cancer samples

Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community

AN INTEGRATED FRAMEWORK FOR QUALITY EVALUATION OF FRUITS AND VEGETABLE STORE LOCATED IN THE SUPERMARKET UNDER UTOPIAN ENVIRONMENT

Customer satisfaction depends on the availability of different varieties of fruits and vegetables in a supermarket store as well as the quality of this supermarket store for fruits and vegetables. The store may contain different variety of fruits and vegetables in a utopian environment. Apart from this, there are several quality parameters of a fruits and vegetable store. The quality evaluation of fruits and vegetable stores located in a supermarket is a big challenge for managerial personnel. Here, a quality evaluation framework is proposed for the fruits and vegetable store. The committee of experts identifies and finalizes the quality evaluation parameters through a brainstorming session. Fuzzy AHP is used to calculate the weights of evaluation parameters. A fuzzy TOPSIS generally ranks for the alternative stores. An improved fuzzy TOPSIS, which is named fuzzy k-TOPSIS, is proposed here to evaluate the quality of fruits and vegetable stores located in a supermarket. The fuzzy k-TOPSIS will provide rank as well as classification of the alternatives. A numerical example is demonstrated for a better understanding of the proposed framework

Contrast in Terahertz Images of Archival Documents—Part I: Influence of the Optical Parameters from the Ink and Support

This study aims to objectively inform curators when terahertz time-domain (TD) imaging set in reflection mode is likely to give well-contrasted images of inscriptions in a complex archival document and is a useful non-invasive alternative to current digitisation processes. To this end, the dispersive refractive indices and absorption coefficients from various archival materials are assessed and their influence on contrast in terahertz images from historical documents is explored. Sepia ink and inks produced with bistre or verdigris mixed with a solution of Arabic gum or rabbit skin glue are unlikely to lead to well-contrasted images. However, dispersions of bone black, ivory black, iron gall ink, malachite, lapis lazuli, minium and vermilion are likely to lead to well-contrasted images. Inscriptions written with lamp black, carbon black and graphite give the best imaging results. The characteristic spectral signatures from iron gall ink, minium and vermilion pellets between 5 and 100 cm−1 relate to a ringing effect at late collection times in TD waveforms transmitted through these pellets. The same ringing effect can be probed in waveforms reflected from iron gall, minium and vermilion ink deposits at the surface of a document. Since TD waveforms collected for each scanning pixel can be Fourier-transformed into spectral information, terahertz TD imaging in reflection mode can serve as a hyperspectral imaging tool. However, chemical recognition and mapping of the ink is currently limited by the fact that the morphology of the document influences more the terahertz spectral response of the document than the resonant behaviour of the ink

Breast cancer image classification using pattern-based Hyper Conceptual Sampling method

The increase in biomedical data has given rise to the need for developing data sampling techniques. With the emergence of big data and the rise of popularity of data science, sampling or reduction techniques have been assistive to significantly hasten the data analytics process. Intuitively, without sampling techniques, it would be difficult to efficiently extract useful patterns from a large dataset. However, by using sampling techniques, data analysis can effectively be performed on huge datasets, to produce a relatively small portion of data, which extracts the most representative objects from the original dataset. However, to reach effective conclusions and predictions, the samples should preserve the data behavior. In this paper, we propose a unique data sampling technique which exploits the notion of formal concept analysis. Machine learning experiments are performed on the resulting sample to evaluate quality, and the performance of our method is compared with another sampling technique proposed in the literature. The results demonstrate the effectiveness and competitiveness of the proposed approach in terms of sample size and quality, as determined by accuracy and the F1-measure. 2018This contribution was made possible by NPRP-07-794-1-145 grant from the Qatar National Research Fund (a member of Qatar foundation). The statements made herein are solely the responsibility of the authors.Scopu

Electronic Imaging & the Visual Arts. EVA 2012 Florence

The key aim of this Event is to provide a forum for the user, supplier and scientific research communities to meet and exchange experiences, ideas and plans in the wide area of Culture & Technology. Participants receive up to date news on new EC and international arts computing & telecommunications initiatives as well as on Projects in the visual arts field, in archaeology and history. Working Groups and new Projects are promoted. Scientific and technical demonstrations are presented

Early Disease Detection Through Computational Pathology

This thesis presents computational pathology algorithms for enabling early cancer detection in Barrett’s Esophagus (BE) and early subtype diagnosis in Interstitial Lung Diseases (ILD). BE is a condition affecting 10% of heartburn sufferers, for which 0.1% of patients develop esophageal adenocarcinoma each year. For most of the 130-200 diseases included in the class of ILDs, a full recovery is expected, but for a few of these diseases, the survival rate is less than three years. For both disease classes, treatment of the malignant forms would be harmful in patients with other forms, thus diagnosis is necessary prior to beginning treatment, and early treatment is most effective in eradicating disease. Early diagnosis of both of these disease classes is complicated by a high degree of sharing of subtle disease phenotypes, leading to high pathologist disagreement rates. Computational pathology methods can aid early diagnosis of these diseases through unbiased, data-driven algorithms. To detect precancerous changes in patients with BE, we develop an automated algorithm which identifies epithelial nuclei in biopsy samples on which nano-scale optical biomarkers, related to cancer risk, can be quantified. The automated nuclei detector produces a higher quality selection of epithelial nuclei than manual detection, resulting in enhanced characterization of precancerous phenotype perturbations. To stratify ILD patients, we develop a novel quantitative representation of pathohistology samples that models lung architecture based on computed image features and insights from pathologists, and establish its utility as part of a diagnostic classifier. Algorithms such as these applied in a clinical setting can save pathologists time by filtering out obvious cases and providing unbiased reasoning to assist diagnoses