Global and local characterization of rock classification by Gabor and DCT filters with a color texture descriptor

In the automatic classification of colored natural textures, the idea of proposing methods that reflect human perception arouses the enthusiasm of researchers in the field of image processing and computer vision. Therefore, the color space and the methods of analysis of color and texture, must be discriminating to correspond to the human vision. Rock images are a typical example of natural images and their analysis is of major importance in the rock industry. In this paper, we combine the statistical (Local Binary Pattern (LBP) with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion) and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptors named respectively Gabor Adjacent Local Binary Pattern Color Space Fusion (G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion (D-ALBPCSF) for the extraction of visual textural and colorimetric features from direct view images of rocks. The textural images from the two G-ALBPCSF and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2 and the intersection of histograms that we have adapted to color histograms. The results obtained allowed us to highlight the discrimination of the rock classes. The proposed extraction method provides better classification results for various direct view rock texture images. Then it is validated by a confusion matrix giving a low error rate of 0.8% of classification

Diversité Des Espèces De Bois D’oeuvre Menacées De Disparition De La Flore De La Côte d’Ivoire, Dans Le Massif Forestier Yapo-Abbé

La présente étude a été réalisée dans le Massif Forestier Yapo-abbé (MFYA), au Sud-Est de la Côte d’Ivoire. Elle vise à évaluer la diversité des espèces de bois d’oeuvre inscrites sur la liste rouge de l’UICN (2018). La méthode de relevé de surface a été utilisée pour l’inventaire et l’identification de ces espèces dans les trois blocs forestiers du massif. Les résultats montrent que le MFYA est riche de 22 espèces de bois d’oeuvre inscrites sur la liste rouge de l’UICN (2018). Ces 22 espèces sont réparties en quinze espèces vulnérables (68,2%), cinq espèces de préoccupation mineure (22,8%), une espèce quasi-menacée (4,5%) et une autre en danger de disparition (4,5%). La fréquence d’apparition des espèces a permis d’obtenir trois groupes d’espèces : le groupe 1 (espèces constantes) compte une espèce, le groupe 2 (espèces accessoires) est constitué de six espèces ; quant au groupe 3 (espèces rares), il est composé de quinze espèces. Les trois biotopes du MFYA sont faiblement diversifiés, d’où la nécessité de les enrichir avec les espèces du groupe 3, en particulier les blocs plantations forestières et jachères, pour une gestion durable dudit massif. This study was conducted in Yapo-Abbe forest in South-Eastern Côte d'Ivoire. It aims to assess the diversity of timber species, listed on the IUCN Red List (2018). The surface survey method was used for the inventory and identification of these species in the three blocks of the forest. The results show that Yapo-Abbe forest is rich in 22 species of timber listed on the IUCN Red List (2018). These 22 species are divided into fifteen vulnerable species (68.2%), five species of low concern (22.8%), a near-threatened species (4.5%) and another species at risk of extinction (4.5%).). The frequency of occurrence of species has resulted in three groups of species; group 1 (constant species) has one species, group 2 (accessory species) consists of six species. As for group 3 (rare species), it’s composed of fifteen species. The three biotopes of Yapo-Abbe forest are poorly diversified, hence the need to enrich them with the group 3 species, especially forest plantation and fallow blocks for sustainable management of this massif

A New Strategy for the Morphological and Colorimetric Recognition of Erythrocytes for the Diagnosis of Forms of Anemia based on Microscopic Color Images of Blood Smears

The detection of red blood cells based on morphology and colorimetric appearance is very important in improving hematology diagnostics. There are automatons capable of detecting certain forms, but these have limitations with regard to the formal identification of red blood cells because they consider certain cells to be red blood cells when they are not and vice versa. Other automata have limitations in their operation because they do not cover a sufficient area of the blood smear. In spite of their performance, biologists have very often resorted to the manual analysis of blood smears under an optical microscope for a morphological and colorimetric study. In this paper, we present a new strategy for semi-automatic identification of red blood cells based on their isolation, their automatic color segmentation using Otsu's algorithm and their morphology. The algorithms of our method have been implemented in the programming environment of the scientific software MATLAB resulting in an artificial intelligence application. The application, once launched, allows the biologist to select a region of interest containing the erythrocyte to be characterized, then a set of attributes are computed extracted from this target red blood cell. These attributes include compactness, perimeter, area, morphology, white and red proportions of the erythrocyte, etc. The types of anemia treated in this work concern the iron-deficiency, sickle-cell or falciform, thalassemia, hemolytic, etc. forms. The results obtained are excellent because they highlight different forms of anemia contracted in a patient.Comment: ISIS

A double-masked placebo-controlled trial of azithromycin to prevent child mortality in Burkina Faso, West Africa: Community Health with Azithromycin Trial (CHAT) study protocol.

BACKGROUND:Biannual, mass azithromycin distribution has previously been shown to reduce all-cause child mortality in sub-Saharan Africa. Subgroup analysis suggested that the strongest effects were in the youngest children, leading to the hypothesis that targeting younger age groups might be an effective strategy to prevent mortality. We present the methods of two randomized controlled trials designed to evaluate mass and targeted azithromycin distribution for the prevention of child mortality in Burkina Faso, West Africa. METHODS/DESIGN:The Child Health with Azithromycin Treatment (CHAT) study consists of two nested, randomized controlled trials. In the first, communities are randomized in a 1:1 fashion to biannual, mass azithromycin distribution or placebo. The primary outcome is under-5 all-cause mortality measured at the community level. In the second, children attending primary healthcare facilities during the first 5-12 weeks of life for a healthy child visit (e.g., for vaccination) are randomized in a 1:1 fashion to a single orally administered dose of azithromycin or placebo. The primary outcome is all-cause mortality measured at 6 months of age. The trial commenced enrollment in August 2019. DISCUSSION:This study is expected to provide evidence on two health systems delivery approaches (mass and targeted treatment) for azithromycin to prevent all-cause child mortality. The results will inform global and national policies related to azithromycin for the prevention of child mortality. TRIAL REGISTRATION:ClinicalTrials.gov, ID: NCT03676764. Registered on 19 September 2018; prospectively registered pre results

Cause-specific childhood mortality in Africa and Asia: evidence from INDEPTH health and demographic surveillance system sites

BACKGROUND: Childhood mortality, particularly in the first 5 years of life, is a major global concern and the target of Millennium Development Goal 4. Although the majority of childhood deaths occur in Africa and Asia, these are also the regions where such deaths are least likely to be registered. The INDEPTH Network works to alleviate this problem by collating detailed individual data from defined Health and Demographic Surveillance sites. By registering deaths and carrying out verbal autopsies to determine cause of death across many such sites, using standardised methods, the Network seeks to generate population-based mortality statistics that are not otherwise available. OBJECTIVE: To present a description of cause-specific mortality rates and fractions over the first 15 years of life as documented by INDEPTH Network sites in sub-Saharan Africa and south-east Asia. DESIGN: All childhood deaths at INDEPTH sites are routinely registered and followed up with verbal autopsy (VA) interviews. For this study, VA archives were transformed into the WHO 2012 VA standard format and processed using the InterVA-4 model to assign cause of death. Routine surveillance data also provided person-time denominators for mortality rates. Cause-specific mortality rates and cause-specific mortality fractions are presented according to WHO 2012 VA cause groups for neonatal, infant, 1-4 year and 5-14 year age groups. RESULTS: A total of 28,751 childhood deaths were documented during 4,387,824 person-years over 18 sites. Infant mortality ranged from 11 to 78 per 1,000 live births, with under-5 mortality from 15 to 152 per 1,000 live births. Sites in Vietnam and Kenya accounted for the lowest and highest mortality rates reported. CONCLUSIONS: Many children continue to die from relatively preventable causes, particularly in areas with high rates of malaria and HIV/AIDS. Neonatal mortality persists at relatively high, and perhaps sometimes under-documented, rates. External causes of death are a significant childhood problem in some settings.P. Kim Streatfield ... Yohannes A. Melaku ... et al

Stratégies de segmentation d'images multicomposantes par analyse d'histogrammes multidimensionnels : Application à des images couleur de coupes histologiques de pommes

Recent technical progresses supply sensors able to characterize a scene by multicomponent images. Most often, the segmentation of multicomponent images proceeds either through the analysis of their marginal histograms, ignoring the correlation between components, or by requantifying the components due to the difficulty of treating hudge n-dimensional (nD) histograms (n is the number of the image components). In a first step, we have developed a vectorial unsupervised and non parametric segmentation of multicomponent images (n ≥ 3), which is based on the hierarchical analysis of the compact nD histograms (an algorithmic data structure which diminishes the histogram size without losses). The modes of the histogram are obtained by labelling its connected components. The segmentation results are compared to those supplied by the classical K-means method, by using the criterions of Levine-Nazif, Zeboudj, Borsotti or Rosenberger. In a second step, to avoid the over-segmentation resulting from the diffuse character of the nD histograms, we propose to label their connected components in a fuzzy manner. The results are compared to those obtained by a requantification of the histogram. Thus we dispose of four segmentation strategies, and we compare their results on a set of natural as well as synthetic images. At last, this work is used to analyze histological cuts of apples in optical microscopy. The results show differences between three apples species, in relation with texture and firmness analyses.Des progrès techniques récents ont permis la mise en oeuvre de capteurs capables de caractériser une scène par un ensemble d'images appelé image multicomposantes (couleur, multispectrale et multisource ou multiprotocole). Les algorithmes de traitement développés en segmentation pour les images monocomposantes ne sont pas directement applicables aux images multicomposantes en raison de leur caractère vectoriel. Dans la littérature, la plupart des travaux s'appuient sur la segmentation d'images multicomposantes par analyse d'histogrammes marginaux en faisant fi de la corrélation existante entre les composantes de l'image ou sur des images requantifiées à cause de la difficulté de manipuler les histogrammes multidimensionnels (nD, n étant le nombre de composantes de l'image) dont le volume de données est considérable. Dans un premier temps, nous avons développé une méthode de segmentation d'images multicomposantes (n ≥ 3) à caractère vectoriel, non supervisée et non paramétrique appelée ImSegHier_nD. Elle repose sur une analyse hiérarchique d'histogrammes nD compacts, une structure de données algorithmique permettant de réduire sans perte de données l'espace mémoire occupé par les histogrammes nD classiques. Les modes sont obtenus grâce à la mise en oeuvre d'un algorithme d'étiquetage en composantes connexes (ECC) adapté à ce type d'histogrammes. L'évaluation de la qualité de segmentation d'ImSegHier_nD relativement à K-means a montré qu'en évaluation supervisée notre approche est meilleure que K-means et inversement en évaluation non supervisée. Les cas de moindres performances de ImSegHier_nD au regard des critères étudiés (Levine-Nazif, Zeboudj, Borsotti et Rosenberger) s'expliquent par l'aspect diffus des histogrammes nD et ont été étudiés pour différentes typologies d'histogrammes. Dans un deuxième temps, pour apporter une réponse plus générale à la problématique de la sur-segmentation engendrée par le caractère diffus des histogrammes nD quand n augmente, nous avons proposé un algorithme d'étiquetage en composantes connexes floues (ECCF) dont nous comparons les résultats sur la classification avec une requantification de l'histogramme. Nous aboutissons ainsi à quatre stratégies de segmentation dérivées d'ImSegHier_nD dont nous comparons les résultats sur des images naturelles et des images de synthèse. Enfin, nous avons appliqué ce travail à l'analyse de coupes histologiques de pommes en microscopie optique. Nos résultats ont mis en évidence la différence entre trois variétés de pommes en lien avec des analyses de texture et de fermeté

Stratégies de segmentation d'images multicomposantes par analyse d'histogrammes multidimensionnels (application à des images couleur de coupes histologiques de pommes)

Des progrès techniques récents ont permis la mise en oeuvre de capteurs capables de caractériser une scène par un ensemble d'images appelé image multicomposantes (couleur, multispectrale et multisource ou multiprotocole). Les algorithmes de traitement développés en segmentation pour les images monocomposantes ne sont pas directement applicables aux images multicomposantes en raison de leur caractère vectoriel. Dans la littérature, la plupart des travaux s'appuient sur la segmentation d'images multicomposantes par analyse d'histogrammes marginaux en faisant fi de la corrélation existante entre les composantes de l'image ou sur des images requantifiées à cause de la difficulté de manipuler les histogrammes multidimensionnels (nD, n étant le nombre de composantes de l'image) dont le volume de données est considérable. Dans un premier temps, nous avons développé une méthode de segmentation d'images multicomposantes (n >= 3) à caractère vectoriel, non supervisée et non paramétrique appelée ImSegHier_nD. Elle repose sur une analyse hiérarchique d'histogrammes nD compacts, une structure de données algorithmique permettant de réduire sans perte de données l'espace mémoire occupé par les histogrammes nD classiques. Les modes sont obtenus grâce à la mise en oeuvre d'un algorithme d'étiquetage en composantes connexes (ECC) adapté à ce type d'histogrammes. L'évaluation de la qualité de segmentation d'ImSegHier_nD relativement à K-means a montré qu'en évaluation supervisée notre approche est meilleure que K-means et inversement en évaluation non supervisée. Les cas de moindres performances de ImSegHier_nD au regard des critères étudiés (Levine-Nazif, Zeboudj, Borsotti et Rosenberger) s'expliquent par l'aspect diffus des histogrammes nD et ont été étudiés pour différentes typologies d'histogrammes. Dans un deuxième temps, pour apporter une réponse plus générale à la problématique de la sur-segmentation engendrée par le caractère diffus des histogrammes nD quand n augmente, nous avons proposé un algorithme d'étiquetage en composantes connexes floues (ECCF) dont nous comparons les résultats sur la classification avec une requantification de l'histogramme. Nous aboutissons ainsi à quatre stratégies de segmentation dérivées d'ImSegHier_nD dont nous comparons les résultats sur des images naturelles et des images de synthèse. Enfin, nous avons appliqué ce travail à l'analyse de coupes histologiques de pommes en microscopie optique. Nos résultats ont mis en évidence la différence entre trois variétés de pommes en lien avec des analyses de texture et de fermeté.Recent technical progresses supply sensors able to characterize a scene by multicomponent images. Most often, the segmentation of multicomponent images proceeds either through the analysis of their marginal histograms, ignoring the correlation between components, or by requantifying the components due to the difficulty of treating hudge n-dimensional (nD) histograms (n is the number of the image components). In a first step, we have developed a vectorial unsupervised and non parametric segmentation of multicomponent images (n >= 3), which is based on the hierarchical analysis of the compact nD histograms (an algorithmic data structure which diminishes the histogram size without losses). The modes of the histogram are obtained by labelling its connected components. The segmentation results are compared to those supplied by the classical K-means method, by using the criterions of Levine-Nazif, Zeboudj, Borsotti or Rosenberger. In a second step, to avoid the over-segmentation resulting from the diffuse character of the nD histograms, we propose to label their connected components in a fuzzy manner. The results are compared to those obtained by a requantification of the histogram. Thus we dispose of four segmentation strategies, and we compare their results on a set of natural as well as synthetic images. At last, this work is used to analyze histological cuts of apples in optical microscopy. The results show differences between three apples species, in relation with texture and firmness analyses.ANGERS-BU Lettres et Sciences (490072106) / SudocSudocFranceF

Unsupervised Image Segmentation by Multi-Dimensional Compact Histograms Analysis

We present here a fully vectorial&nbsp;nD image segmentation method by the mean of an unsupervised analysis of the multi-dimensional compact histogram.</p

Global and local characterization of rock classification by Gabor and DCT filters with a color texture descriptor

International audienceIn the automatic classification of colored natural textures, the idea of proposing methods that reflect human perception arouses the enthusiasm of researchers in the field of image processing and computer vision. Therefore, the color space and the methods of analysis of color and texture, must be discriminating to correspond to the human vision. Rock images are a typical example of natural images and their analysis is of major importance in the rock industry. In this paper, we combine the statistical (Local Binary Pattern (LBP) with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion) and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptors named respectively Gabor Adjacent Local Binary Pattern Color Space Fusion (G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion (D-ALBPCSF) for the extraction of visual textural and colorimetric features from direct view images of rocks. The textural images from the two G-ALBPCSF and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2 and the intersection of histograms that we have adapted to color histograms. The results obtained allowed us to highlight the discrimination of the rock classes. The proposed extraction method provides better classification results for various direct view rock texture images. Then it is validated by a confusion matrix giving a low error rate of 0.8% of classification.</p

FAST COMPUTATION OF ENTROPIES AND MUTUAL INFORMATION FOR MULTISPECTRAL IMAGES

Abstract: This paper describes the fast computation, and some applications, of entropies and mutual information for color and multispectral images. It is based on the compact coding and fast processing of multidimensional histograms for digital images.