Goniochromatic assessment of gray scales for color change.

The dependence of color differences on the illumination and viewing directions for two widely used gray scales for color change (SDCE and AATCC) was evaluated through measuring the spectral bidirectional reflectance distribution function (BRDF) by a gonio-spectrophotometer of metrological quality. Large incidence and viewing angles must be specially avoided using these gray scales because, in these conditions, color differences vary considerably from those established in ISO 105-A02 and ASTM D2616-12. While the visual appearance of the SDCE and AATCC gray scales for color change is similar, our results indicate that their goniochromatic properties are different. Finally, some recommendations regarding observation distance and illumination angle are given to correctly use these gray scales for visual experiments.

New Journal prize to recognize the best paper from an emerging researcher: editorial.

Editor-in-Chief P. Scott Carney and Deputy Editor Christine Fernandez-Maloigne introduce a new prize for the best paper published by an emerging researcher in the Journal in 2018.

Classical optics in France: introduction.

This special issue of the Journal of the Optical Society of America A (JOSA A) is devoted to the wide array of French researchers from universities and state research organisms, offering them the opportunity to share and showcase their current research in the fields of optics and imaging sciences to the global community.

Gradient in spectral and color images: from the Di Zenzo initial construction to a generic proposition.

In this article, we define a generic gradient for color and spectral images, considering a proposed taxonomy of the state of the art. A full-vector gradient, taking into account the sensor's characteristics, is in compliance with the metrological properties of genericity, robustness, and reproducibility. Here, we construct a protocol to compare gradients from different sensors. The comparison is developed by simulating sensors using their spectral characteristics. We develop three experiments using this protocol. The first experiment shows the consistency of results for similar sensors; the second demonstrates the genericity of the approach, adapted to any kind of imaging sensors; and the third focuses on the channel inter-correlation considering sensors such as in the color vision deficiency case.

Diagnostic and Therapeutic Issues in Glioma Using Imaging Data: The Challenge of Numerical Twinning.

Glial tumors represent the leading etiology of primary brain tumors. Their particularities lie in (i) their location in a highly functional organ that is difficult to access surgically, including for biopsy, and (ii) their rapid, anisotropic mode of extension, notably via the fiber bundles of the white matter, which further limits the possibilities of resection. The use of mathematical tools enables the development of numerical models representative of the oncotype, genotype, evolution, and therapeutic response of lesions. The significant development of digital technologies linked to high-resolution NMR exploration, coupled with the possibilities offered by AI, means that we can envisage the creation of digital twins of tumors and their host organs, thus reducing the use of physical sampling.

Attention-guided neural network for early dementia detection using MRS data.

Imaging bio-markers have been widely used for Computer-Aided Diagnosis (CAD) of Alzheimer's Disease (AD) with Deep Learning (DL). However, the structural brain atrophy is not detectable at an early stage of the disease (namely for Mild Cognitive Impairment (MCI) and Mild Alzheimer's Disease (MAD)). Indeed, potential biological bio-markers have been proved their ability to early detect brain abnormalities related to AD before brain structural damage and clinical manifestation. Proton Magnetic Resonance Spectroscopy (1H-MRS) provides a promising solution for biological brain changes detection in a no invasive manner. In this paper, we propose an attention-guided supervised DL framework for early AD detection using 1H-MRS data. In the early stages of AD, features may be closely related and often complex to delineate between subjects. Hence, we develop a 1D attention mechanism that explicitly guides the classifier to focus on diagnostically relevant metabolites for classes discrimination. Synthetic data are used to tackle the lack of data problem and to help in learning the feature space. Data used in this paper are collected in the University Hospital of Poitiers, which contained 111 1H-MRS samples extracted from the Posterior Cingulate Cortex (PCC) brain region. The data contain 33 Normal Control (NC), 49 MCI due to AD, and 29 MAD subjects. The proposed model achieves an average classification accuracy of 95.23%. Our framework outperforms state of the art imaging-based approaches, proving the robustness of learning metabolites features against traditional imaging bio-markers for early AD detection.

Usefulness of computed tomography textural analysis in renal cell carcinoma nuclear grading.

Purpose: To evaluate the usefulness of computed tomography (CT) texture descriptors integrated with machine-learning (ML) models in the identification of clear cell renal cell carcinoma (ccRCC) and for the first time papillary renal cell carcinoma (pRCC) tumor nuclear grades [World Health Organization (WHO)/International Society of Urologic Pathologists (ISUP) 1, 2, 3, and 4]. Approach: A total of 143 ccRCC and 21 pRCC patients were analyzed in this study. Texture features were extracted from late arterial phase CT images. A complete separation of training/validation and testing subsets from the beginning to the end of the pipeline was adopted. Feature dimension was reduced by collinearity analysis and Gini impurity-based feature selection. The synthetic minority over-sampling technique was employed for imbalanced datasets. The ML classifiers were logistic regression, SVM, RF, multi-layer perceptron, and K -NN. The differentiation between low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and between all grades was assessed for ccRCC and pRCC datasets. The classification performance was assessed and compared by certain metrics. Results: Textures-based classifiers were able to efficiently identify ccRCC and pRCC grades. An accuracy and area under the characteristic operating curve (AUC) up to 91%/0.9, 91%/0.9, 90%/0.9, and 88%/1 were reached when discriminating ccRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. An accuracy and AUC up to 96%/1, 81%/0.8, 86%/0.9, and 88%/0.9 were found when differentiating pRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. Conclusion: CT texture-based ML models can be used to assist radiologist in predicting the WHO/ISUP grade of ccRCC and pRCC pre-operatively.

Hyperspectral Texture Metrology Based on Joint Probability of Spectral and Spatial Distribution.

Texture characterization from the metrological point of view is addressed in order to establish a physically relevant and directly interpretable feature. In this regard, a generic formulation is proposed to simultaneously capture the spectral and spatial complexity in hyperspectral images. The feature, named relative spectral difference occurrence matrix (RSDOM) is thus constructed in a multireference, multidirectional, and multiscale context. As validation, its performance is assessed in three versatile tasks. In texture classification on HyTexiLa, content-based image retrieval (CBIR) on ICONES-HSI, and land cover classification on Salinas, RSDOM registers 98.5% accuracy, 80.3% precision (for the top 10 retrieved images), and 96.0% accuracy (after post-processing) respectively, outcompeting GLCM, Gabor filter, LBP, SVM, CCF, CNN, and GCN. Analysis shows the advantage of RSDOM in terms of feature size (a mere 126, 30, and 20 scalars using GMM in order of the three tasks) as well as metrological validity in texture representation regardless of the spectral range, resolution, and number of bands.

Recent advances in medical image processing for the evaluation of chronic kidney disease.

Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.

Investigating efficient CNN architecture for multiple sclerosis lesion segmentation.

Purpose: The automatic segmentation of multiple sclerosis lesions in magnetic resonance imaging has the potential to reduce radiologists' efforts on a daily time-consuming task and to bring more reproducibility. Almost all new segmentation techniques make use of convolutional neural networks with their own different architecture. Architectural choices are rarely explained. We aimed at presenting the relevance of a U-net-like architecture for our specific task and at building an efficient and simple model. Approach: An experimental study was performed by observing the impact of applying different mutations and deletions to a simple U-net-like architecture. Results: The power of the U-net architecture is explained by the joint benefits of using an encoder-decoder architecture and by linking them with long skip connections. Augmenting the number of convolutional layers and decreasing the number of feature maps allowed us to build an exceptionally light and competitive architecture, the minimally parameterized U-net (MPU-net), with only ∼ 30,000 parameters. Conclusion: The empirical study of the U-net has led to a better understanding of its architecture. It has guided the building of the MPU-net, a model far less parameterized than others (at least by a factor of seven). This neural network achieves a human-level segmentation of multiple sclerosis lesions on fluid-attenuated inversion recovery images only. It shows that this segmentation task does not necessitate overly complicated models to be achieved. This gives the opportunity to build more explainable models that can help such methods to be adopted in a clinical environment.

Full-Vector Gradient for Multi-spectral or Multivariate Images.

Gradient extraction is important for a lot of metrological applications such as Control Quality by Vision. In this work, we propose a full-vector gradient for multi-spectral sensors. The full-vector gradient extends Di Zenzo expression to take into account the non-orthogonality of the acquisition channels thanks to a Gram matrix. This expression is generic and independent from channel count. Results are provided for a color and a multi-spectral snapshot sensor. Then, we show the accuracy improvement of the gradient calculation by creating a dedicated objective test and from real images.

Neovascularization detection in diabetic retinopathy from fluorescein angiograms.

Although a lot of work has been done on optical coherence tomography and color images in order to detect and quantify diseases such as diabetic retinopathy, exudates, or neovascularizations, none of them is able to evaluate the diffusion of the neovascularizations in retinas. Our work has been to develop a tool that is able to quantify a neovascularization and the fluorescein leakage during an angiography. The proposed method has been developed following a clinical trial protocol; images are taken by a Spectralis (Heidelberg Engineering). Detections are done using a supervised classification using specific features. Images and their detected neovascularizations are then spatially matched by an image registration. We compute the expansion speed of the liquid that we call diffusion index. This last one specifies the state of the disease, permits indication of the activity of neovascularizations, and allows a follow-up of patients. The method proposed in this paper has been built to be robust, even with laser impacts, to compute a diffusion index.

Vector extension of monogenic wavelets for geometric representation of color images.

Monogenic wavelets offer a geometric representation of grayscale images through an AM-FM model allowing invariance of coefficients to translations and rotations. The underlying concept of local phase includes a fine contour analysis into a coherent unified framework. Starting from a link with structure tensors, we propose a nontrivial extension of the monogenic framework to vector-valued signals to carry out a nonmarginal color monogenic wavelet transform. We also give a practical study of this new wavelet transform in the contexts of sparse representations and invariant analysis, which helps to understand the physical interpretation of coefficients and validates the interest of our theoretical construction.