scholarly journals A novel approach to thermographic images analysis of equine thoracolumbar region: the effect of effort and rider’s body weight on structural image complexity

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Malgorzata Masko ◽  
Marta Borowska ◽  
Malgorzata Domino ◽  
Tomasz Jasinski ◽  
Lukasz Zdrojkowski ◽  
...  
Keyword(s):  
Body Weight ◽  
Gray Level ◽  
Analysis Methods ◽  
Maximal Load ◽  
Novel Approach ◽  
Thoracolumbar Region ◽  
Before And After ◽  
Thermographic Camera ◽  
Back Problems ◽  
Occurrence Matrix

Abstract Background The horses’ backs are particularly exposed to overload and injuries due to direct contact with the saddle and the influence of e.g. the rider’s body weight. The maximal load for a horse’s back during riding has been suggested not to exceed 20% of the horses’ body weight. The common prevalence of back problems in riding horses prompted the popularization of thermography of the thoracolumbar region. However, the analysis methods of thermographic images used so far do not distinguish loaded horses with body weight varying between 10 and 20%. Results The superficial body temperature (SBT) of the thoracolumbar region of the horse’s back was imaged using a non-contact thermographic camera before and after riding under riders with LBW (low body weight, 10%) and HBW (high body weight, 15%). Images were analyzed using six methods: five recent SBT analyses and the novel approach based on Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). Temperatures of the horse’s thoracolumbar region were higher (p < 0.0001) after then before the training, and did not differ depending on the rider’s body weight (p > 0.05), regardless of used SBT analysis method. Effort-dependent differences (p < 0.05) were noted for six features of GLCM and GLRLM analysis. The values of selected GLCM and GLRLM features also differed (p < 0.05) between the LBW and HBW groups. Conclusion The GLCM and GLRLM analyses allowed the differentiation of horses subjected to a load of 10 and 15% of their body weights while horseback riding in contrast to the previously used SBT analysis methods. Both types of analyzing methods allow to differentiation thermal images obtained before and after riding. The textural analysis, including selected features of GLCM or GLRLM, seems to be promising tools in considering the quantitative assessment of thermographic images of horses’ thoracolumbar region.

scholarly journals The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

Animals ◽  
2022 ◽  
Vol 12 (2) ◽  
pp. 195
Author(s):  
Małgorzata Domino ◽  
Marta Borowska ◽  
Anna Trojakowska ◽  
Natalia Kozłowska ◽  
Łukasz Zdrojkowski ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Texture Features ◽  
Image Texture ◽  
Gray Level ◽  
Image Texture Analysis ◽  
Thermal Images ◽  
Thoracolumbar Region ◽  
Thermal Features ◽  
Occurrence Matrix ◽  
The Impact

Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses’ care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body’s surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.

scholarly journals A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

2017 ◽  
Vol 7 (5) ◽  
pp. 2502 ◽  
Author(s):  
G. S. N. Murthy ◽  
Srininvasa Rao. V ◽  
T. Veerraju
Keyword(s):  
Nearest Neighbor ◽  
Texture Classification ◽  
Texture Features ◽  
Significant Feature ◽  
Gray Level ◽  
Texture Image ◽  
K Nearest Neighbor ◽  
Classification Rate ◽  
Novel Approach ◽  
Occurrence Matrix

The human eye can easily identify the type of textures in flooring of the houses and in the digital images visually.  In this work, the stone textures are grouped into four categories. They are bricks, marble, granite and mosaic. A novel approach is developed for decreasing the dimension of stone image and for reducing the gray level range of the image without any loss of significant feature information. This model is named as “Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM)” model. The DDRGRM model consists of 3 stages.  In stage 1, each 5×5 sub dimension of the stone image is reduced into 2×2 sub dimension without losing any important qualities, primitives, and any other local stuff.  In stage 2, the gray level of the image is reduced from 0-255 to 0-4 by using fuzzy concepts.  In stage 3, Co-occurrence Matrix (CM) features are derived from the DDRGRM model of the stone image for stone texture classification.  Based on the feature set values, a user defined algorithm is developed to classify the stone texture image into one of the 4 categories i.e. Marble, Brick, Granite and Mosaic. The proposed method is tested by using the K-Nearest Neighbor Classification algorithm with the derived texture features.  To prove the efficiency of the proposed method, it is tested on different stone texture image databases.  The proposed method resulted in high classification rate when compared with the other existing methods.

Detection of image region-duplication forgery based on gray level co-occurrence matrix

2012 ◽  
Vol 31 (6) ◽  
pp. 1628-1630
Author(s):  
Jia-jia OU ◽  
Bi-ye CAI ◽  
Bing XIONG ◽  
Feng LI
Keyword(s):  
Gray Level ◽  
Image Region ◽  
Region Duplication ◽  
Occurrence Matrix

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

Evaluation of Dexmedetomidine Dosing in Obese Critically Ill Patients

2021 ◽  
pp. 089719002110215
Author(s):  
Sara A. Atyia ◽  
Keaton S. Smetana ◽  
Minh C. Tong ◽  
Molly J. Thompson ◽  
Kari M. Cape ◽  
...  
Keyword(s):  
Body Weight ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Statistical Difference ◽  
Ideal Body Weight ◽  
Weight Percentage ◽  
Before And After ◽  
Light Sedation ◽  
Icu Sedation ◽  
Dose Dependent

Background: Dexmedetomidine is a highly selective α2-adrenoreceptor agonist that produces dose-dependent sedation, anxiolysis, and analgesia without respiratory depression. Due to these ideal sedative properties, there has been increased interest in utilizing dexmedetomidine as a first-line sedative for critically ill patients requiring light sedation. Objective: To evaluate the ability to achieve goal intensive care unit (ICU) sedation before and after an institutional change of dosing from actual (ABW) to adjusted (AdjBW) body weight in obese patients on dexmedetomidine. Methods: This study included patients ≥ 18 years old, admitted to a surgical or medical ICU, required dexmedetomidine for at least 8 hours as a single continuous infusion sedative, and weighed ≥ 120% of ideal body weight. Percentage of RASS measurements within goal range (−1 to +1) during the first 48 hours after initiation of dexmedetomidine as the sole sedative agent or until discontinuation dosed on ABW compared to AdjBW was evaluated. Results: 100 patients were included in the ABW cohort and 100 in the AdjBW cohort. The median dosing weight was significantly higher in the ABW group (95.9 [78.9-119.5] vs 82.2 [72.1-89.8] kg; p = 0.001). There was no statistical difference in percent of RASS measurements in goal range (61.5% vs 69.6%, p = 0.267) in patients that received dexmedetomidine dosed based on ABW versus AdjBW. Conclusion: Dosing dexmedetomidine using AdjBW in obese critically ill patients for ongoing ICU sedation resulted in no statistical difference in the percent of RASS measurements within goal when compared to ABW dosing. Further studies are warranted.

Antitumor effects of Auraptene in 4T1 tumor‐bearing Balb/c mice

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohammad Reza. Shiran ◽  
Davar Amani ◽  
Abolghasem Ajami ◽  
Mahshad Jalalpourroodsari ◽  
Maghsoud Khalizadeh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Body Weight ◽  
Tumor Volume ◽  
Serum Levels ◽  
Ki 67 ◽  
Tumor Bearing ◽  
Paraffin Oil ◽  
Anti Cancer ◽  
Before And After ◽  
Ifn Γ

Abstract Objectives Breast cancer is a common malignant tumor in women with limited treatment options and multiple side effects. Today, the anti-cancer properties of natural compounds have attracted widespread attention from researchers worldwide. Methods In this study, we treated 4T1 tumor-bearing Balb/c mice with intraperitoneal injection of Auraptene, paraffin oil, and saline as two control groups. Body weight and tumor volume were measured before and after treatment. Hematoxylin and eosin (H & E) staining and immunohistochemistry of Ki-67 were used as markers of proliferation. In addition, ELISA assays were performed to assess serum IFN-γ and IL-4 levels. Results There was no significant change in body weight in all animal groups before and after treatment. 10 days after the last treatment, Auraptene showed its anti-cancer effect, which was confirmed by the smaller tumor volume and H & E staining. In addition, Ki-67 expression levels were significantly reduced in tumor samples from the Auraptene-treated group compared to the paraffin oil and saline-treated groups. In addition, in tumor-bearing and normal mice receiving Auraptene treatment, IL-4 serum production levels were reduced, while serum levels of IFN-γ were significantly up-regulated in tumor-bearing mice after Auraptene treatment. Conclusions In the case of inhibition of tumor volume and Ki-67 proliferation markers, Auraptene can effectively inhibit tumor growth in breast cancer animal models. In addition, it might increases Th1 and CD8 + T cell responses after reducing IL-4 serum levels and IFN-γ upregulation, respectively. However, further research is needed to clarify its mechanism of action.

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