3 research outputs found
Segmentation and measurement of lung pathological changes for COVID-19 diagnosis based on computed tomography
Coronavirus 2019 (COVID-19) spread internationally in early 2020, resulting from an existential health disaster. Automatic detecting of pulmonary infections based on computed tomography (CT) images has a huge potential for enhancing the traditional healthcare strategy for treating COVID-19. CT imaging is essential for diagnosis, the process of assessment, and the staging of COVID-19 infection. The detection in association with computed tomography faces many problems, including the high variability, and low density between the infection and normal tissues. Processing is used to solve a variety of diagnostic tasks, including highlighting and contrasting things of interest while taking color-coding into account. In addition, an evaluation is carried out using the relevant criteria for determining the alterations nature and improving a visibility of pathological changes and an accuracy of the X-ray diagnostic report. It is proposed that pre-processing methods for a series of dynamic images be used for these objectives. The lungs are segmented and parts of probable disease are identified using the wavelet transform and the Otsu threshold value. Delta maps and maps created with the Shearlet transform that have contrasting color coding are used to visualize and select features (markers). The efficiency of the suggested combination of approaches for investigating the variability of the internal geometric features (markers) of the object of interest in the photographs is demonstrated by analyzing the experimental and clinical material done in the work. The suggested system indicated that the total average coefficient obtained 97.64% regarding automatic and manual infection sectors, while the Jaccard similarity coefficient achieved 96.73% related to the segmentation of tumor and region infected by COVID-19
The impact of Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation: a natural long-term in situ experiment in a planted pine forest
Increased anthropogenic pressure including intensification of agricultural activities leads to long-term decline of natural biotopes, with planted forests often considered as promising compensatory response, although reduced biodiversity and ecosystem stability represent their common drawbacks. Here we present a complex investigation of the impact of a large Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation in a planted Scots pine forest representing a natural in situ experiment on an engineered ecosystem. After settling around 2006, the colony expanded for 15Β years, leading to the intensive deposition of nutrients with feces, food remains and feather thereby considerably altering the local soil biogeochemistry. Thus, lower pH levels around 4.5, 10- and 2-fold higher concentrations of phosphorous and nitrogen, as well as 1.2-fold discrepancies in K, Li, Mn, Zn and Co., respectively, compared to the surrounding control forest area could be observed. Unaltered total organic carbon (Corg) suggests repressed vegetation, as also reflected in the vegetation indices obtained by remote sensing. Moreover, reduced soil microbial diversity with considerable alternations in the relative abundance of Proteobacteria, Firmicutes, Acidobacteriota, Actinobacteriota, Verrucomicrobiota, Gemmatimonadota, Chujaibacter, Rhodanobacter, and Bacillus has been detected. The above alterations to the ecosystem also affected climate stress resilience of the trees indicated by their limited recovery from the major 2010 drought stress, in marked contrast to the surrounding forest (p = 3β10β5). The complex interplay between geographical, geochemical, microbiological and dendrological characteristics, as well as their manifestation in the vegetation indices is explicitly reflected in the Bayesian network model. Using the Bayesian inference approach, we have confirmed the predictability of biodiversity patterns and trees growth dynamics given the concentrations of keynote soil biogeochemical alternations with correlations R > 0.8 between observations and predictions, indicating the capability of risk assessment that could be further employed for an informed forest management
ΠΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ Π°Π³ΡΠΎΠΊΡΠ»ΡΡΡΡ ΠΏΠΎ ΠΊΠΎΡΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ ΡΡΠ΅Π΄Π½Π΅Π³ΠΎ ΡΠ°Π·ΡΠ΅ΡΠ΅Π½ΠΈΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π³Π°ΡΡΡΠΎΠ²ΡΠΊΠΈΡ ΡΠ»ΡΡΠ°ΠΉΠ½ΡΡ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ²
Agricultural applications of the Gaussian process (GP) based techniques is considered. A method
of classifying crops from multi-temporal Landsat 8 satellite imagery is proposed. The method
uses the model of spectral features based on GP regression with constant expectation and square
exponential covariance functions. Main steps of the classification procedure and examples of
recognition of culture species are represented. The ground based data are used for quantitative
validation of the proposed classification method. The highest overall classification accuracy in
three classes of crops is 77.78%Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ΅Π»ΡΡΠΊΠΎΡ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΡΡ
ΠΊΡΠ»ΡΡΡΡ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² ΠΠ°ΡΡΡΠ° Π΄Π»Ρ Π°Π½Π°Π»ΠΈΠ·Π° Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΡΠ΄ΠΎΠ² Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΈΠ½Π΄Π΅ΠΊΡΠ° NDVI ΠΏΠΎ Π΄Π°Π½Π½ΡΠΌ
ΡΠΏΡΡΠ½ΠΈΠΊΠ° Landsat 8. Π Π°Π»Π³ΠΎΡΠΈΡΠΌΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΡ Ρ Π½ΡΠ»Π΅Π²ΡΠΌ ΡΡΠ΅Π΄Π½ΠΈΠΌ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ΠΌ
ΠΈ ΠΊΠ²Π°Π΄ΡΠ°ΡΠΎΠΌ ΡΠΊΡΠΏΠΎΠ½Π΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΄ΡΠ°. ΠΠΏΠΈΡΠ°Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈ ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½
ΠΏΡΠΈΠΌΠ΅Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ Π²ΠΈΠ΄ΠΎΠ² ΠΊΡΠ»ΡΡΡΡ. ΠΠ°Π½Π° ΠΎΡΠ΅Π½ΠΊΠ° ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΊΡΠ»ΡΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠΌ
ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠΌ. Π‘Π°ΠΌΠ°Ρ Π²ΡΡΠΎΠΊΠ°Ρ ΠΎΠ±ΡΠ°Ρ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π² ΡΡΠ΅Ρ
ΠΊΠ»Π°ΡΡΠ°Ρ
ΠΊΡΠ»ΡΡΡΡ
ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ 77,78