Statistical Analysis of Radiographic Textures Illustrating Healing Process After the Guided Bone Regeneration Surgery

Girejko, Gabriela; Borowska, Marta; Szarmach, Janusz

doi:10.1007/978-3-319-91211-0_19

Cited by 8 publications

(10 citation statements)

References 8 publications

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“…Texture analysis using in this study includes Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). Gray Level Co-Occurrence matrix is a mathematical tool based on the analysis of the spatial relationship of the intensity of two pixels in texture [ 17 , 22 , 38 ]. The distance between analyzed pixels was set to 5 in the vertical direction.…”

Section: Methodsmentioning

confidence: 99%

“…Gray Level Run Length Matrix is the second mathematical tool of the texture analysis, which aim is to find runs of consecutive pixels with the same gray level value in the given direction [ 22 , 39 ]. In Mazda software was calculated 7 features of GLRLM method: short-run emphasis (SRE), long-run emphasis (LRE), gray level non-uniformity (GLN), run-length nonuniformity (RLN), a fraction of image in runs (Fraction), run-length nonuniformity moment (MRLN), gray level non-uniformity moment (MGLN).…”

Section: Methodsmentioning

confidence: 99%

“…This second GLM approach counts the number of aligned pixels with equal gray levels [ 17 ]. Both approaches were also applied together in other medical applications such as an analyzing radiographic images during the healing process [ 22 ] or histopathology images in a case of brain cancer diagnosis [ 23 ]. Due to numerous recent medical applications, the calculation of selected features of GLCM and GLRLM descriptors is presented here as an example of how indices of structural complexity can be used in the analysis of thermographic images of a horse’s back.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Texture feature analysis was also applied to evaluation of the bone structure affected by osteoporosis [23][24][25], and similar techniques were used to characterize periapical bone loss [26][27][28][29], periapical bone healing [30][31][32][33], and focal periapical lesions [34,35]. Other research has focused on evaluation of the jaws [36] and cases in which sex-dependent structural differences in the jaw bone are visible [37].…”

Section: Related Workmentioning

confidence: 99%

Caries detection enhancement using texture feature maps of intraoral radiographs

et al. 2018

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Objectives Dental caries are caused by tooth demineralization due to bacterial plaque formation. However, the resulting lesions are often discrete and thus barely recognizable in intraoral radiography images. Therefore, more advanced detection techniques are in great demand among dentists and radiographers. This study was performed to evaluate the performance of texture feature maps in the recognition of discrete demineralization related to caries plaque formation. Methods Digital intraoral radiology image analysis protocols incorporating first-order features (FOF), co-occurrence matrices, gray tone difference matrices, run-length matrices (RLM), local binary patterns (LBP), and k-means clustering (CLU) were used to transform the digital intraoral radiology images of 10 patients with confirmed caries, which were retrospectively reviewed in a dental clinic. The performance of the resulting texture feature maps was compared with that of radiographic images by radiologists and dental specialists. Results Significantly improved detection of caries spots was achieved by employing the CLU and FOF texture feature maps. The caries-affected area with sharp margins was well defined using the CLU approach. A pseudo-three-dimensional effect was observed in outlining the demineralization zones inside the cavity with the FOF 5 protocol. In contrast, the LBP and RLM techniques produced less satisfactory results with unsharp edges and less detailed depiction of the lesions. Conclusions This study illustrated the applicability of texture feature maps to the recognition of demineralized spots on the tooth surface debilitated by caries and identified the best performing techniques.

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“…The effectiveness of implantological treatment largely depends on the achieved bone tissue anastomosis with the implant surface (osseointegration). It is a dynamic process in which, inter alia, through biomechanical forces occurring in the implantological system, a balance between bone tissue growing and resorption is maintained [3,8,13,21,22,24,35].…”

Section: Introductionmentioning

confidence: 99%

Analysis of X-rays in bone remodelling around Active and Replace dental implants

Trochim

Borowska

Szarmach

2021

SIViP

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Purpose of the study: The purpose of the study was to examine on the basis of radiographic images of bone tissue, whether there are differences in the rate of bone remodelling using different shapes of implants in the mandible and maxilla. Moreover, the study also compares texture features obtained on the basis of these images for healthy bone tissue, bone directly after implantation and after a 12-month period of prosthetic loading. Materials and Methods: The subject of the analysis was radiovisiogram images obtained from the Medical University of Bialystok from the Department of Dental Surgery. They are radiovisiogram photographs of 146 people aged 18–74, treated implantally due to missing teeth. The whole group of patients received two types of implants (Active and Replace) of the same company, made of titanium, intraosseous, screw-in. Results: It has been shown that both in the upper jaw and the mandible, the values of texture parameters obtained for bone images made after one year of prosthetic loading are closer to healthy tissue than immediately after implantation. These values for the mandible were relatively closer to those obtained on the basis of healthy tissue than those for the upper jaw. The bone around the implant with a single threading achieved better results in the mandible than the one with a double threading. Conclusion: The type of bone tissue and the shape of the implant have an impact on the achieved osseointegration. With the passage of time and the process of bone remodelling, the damaged tissue returns to its normal structure.

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Statistical Analysis of Radiographic Textures Illustrating Healing Process After the Guided Bone Regeneration Surgery

Cited by 8 publications

References 8 publications

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

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

Caries detection enhancement using texture feature maps of intraoral radiographs

Analysis of X-rays in bone remodelling around Active and Replace dental implants

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