QMaZda — Software tools for image analysis and pattern recognition

Szczypiński, Piotr M.; Klepaczko, Artur; Kociołek, Marcin

doi:10.23919/spa.2017.8166867

Cited by 27 publications

(21 citation statements)

References 14 publications

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“…In the first (i) and second steps (ii), the thermal images were acquired, saved as .jpg files, and segmented as described above. The thermal images were saved as .bmp files, opened using the QMazda Software [ 47 ], and converted to red (R), green (G), and blue (B) components as a way of image transformation to grayscale. As the result of the conversion, each thermal image was represented by three new images, each containing only one color component in grayscale reflecting the two-dimensional temperature assignation of the imaged thoracolumbar region.…”

Section: Methodsmentioning

confidence: 99%

“…As the result of the conversion, each thermal image was represented by three new images, each containing only one color component in grayscale reflecting the two-dimensional temperature assignation of the imaged thoracolumbar region. Afterwards, the third step (iii), conversion to color components, was completed [ 47 , 48 ]. After conversion, the fourth step (iv), the features extraction, was conducted individually for red, green, and blue components as before using the QMazda Software [ 47 , 49 ].…”

Section: Methodsmentioning

confidence: 99%

“…Afterwards, the third step (iii), conversion to color components, was completed [ 47 , 48 ]. After conversion, the fourth step (iv), the features extraction, was conducted individually for red, green, and blue components as before using the QMazda Software [ 47 , 49 ]. For each of the four ROIs, three analytical approaches: histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) were applied, thus the QMazda Software returned a total of thirty-one texture features: 13 HS features, 7 GLRLM features, and 11 GLCH features.…”

Section: Methodsmentioning

confidence: 99%

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

Domino

Borowska

Trojakowska

et al. 2022

Animals

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Domino

Borowska

Trojakowska

et al. 2022

Animals

View full text Add to dashboard Cite

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“…The fourth step, extraction of image texture features, was conducted using three approaches in annotated ROIs using QMazda Software [ 34 , 35 ]. Texture features of thermal images were calculated independently for individual R, G, B, Y, U, V, I, Q, H, and S components.…”

Section: Methodsmentioning

confidence: 99%

“…Thermal images are colorful, where the emitted infrared radiation is presented as the color gradient corresponding to the distribution of surface temperatures [ 15 ]. Conversely, image texture approaches require grayscale images as input [ 32 , 33 , 34 , 35 , 36 ]. Therefore, conversion of the IRT images to selected color components is required to transform the input to grayscale [ 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

Domino

Borowska

Kozłowska

et al. 2021

Sensors

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Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.

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The Influence of Age on Morphometric and Textural Vertebrae Features in Lateral Cervical Spine Radiographs

Mazur

Obuchowicz

Piórkowski

2020

Advances in Intelligent Systems and Computing

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QMaZda — Software tools for image analysis and pattern recognition

Cited by 27 publications

References 14 publications

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

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

Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

The Influence of Age on Morphometric and Textural Vertebrae Features in Lateral Cervical Spine Radiographs

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