Fractal texture analysis of the healing process after bone loss

Borowska, Marta; Szarmach, Janusz; Oczeretko, Edward

doi:10.1016/j.compmedimag.2015.08.001

Cited by 22 publications

(17 citation statements)

References 29 publications

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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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Section: Related Workmentioning

confidence: 99%

Caries detection enhancement using texture feature maps of intraoral radiographs

et al. 2018

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“…A similar approach has been reported for the analysis of panoramic images in order to enhance the recognition of caries [34]. Another study assessed the treatment effectiveness of guided bone regeneration in cases of post-resectal and post-cystal bone loss on DIR images obtained using RVG 6100 digital radiography equipment (Kodak) [16]. Fractal dimension measurements (power spectral density, triangular prism surface area, blanket, intensity difference scaling, and variogram methods) were performed, and the images became smoother during the healing process after bone loss [35].…”

Section: Discussionmentioning

confidence: 99%

“…Numerous features formed the basis for segmentation [13] and bone loss degree measurement [14]. Other applications of texture analysis were used for periapical bone healing [16][17][18], where radiological assessment of treatment effectiveness of guided bone regeneration was measured. The most similar research to the presented is considered in [19,20], where the authors tried to detect the type of cyst using the Gray-Level Co-occurrence Matrix and its related properties.…”

Section: Introductionmentioning

confidence: 99%

Use of Texture Feature Maps for the Refinement of Information Derived from Digital Intraoral Radiographs of Lytic and Sclerotic Lesions

et al. 2019

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The aim of this study was to examine whether additional digital intraoral radiography (DIR) image preprocessing based on textural description methods improves the recognition and differentiation of periapical lesions. (1) DIR image analysis protocols incorporating clustering with the k-means approach (CLU), texture features derived from co-occurrence matrices, first-order features (FOF), gray-tone difference matrices, run-length matrices (RLM), and local binary patterns, were used to transform DIR images derived from 161 input images into textural feature maps. These maps were used to determine the capacity of the DIR representation technique to yield information about the shape of a structure, its pattern, and adequate tissue contrast. The effectiveness of the textural feature maps with regard to detection of lesions was revealed by two radiologists independently with consecutive interrater agreement. (2) High sensitivity and specificity in the recognition of radiological features of lytic lesions, i.e., radiodensity, border definition, and tissue contrast, was accomplished by CLU, FOF energy, and RLM. Detection of sclerotic lesions was refined with the use of RLM. FOF texture contributed substantially to the high sensitivity of diagnosis of sclerotic lesions. (3) Specific DIR texture-based methods markedly increased the sensitivity of the DIR technique. Therefore, application of textural feature mapping constitutes a promising diagnostic tool for improving recognition of dimension and possibly internal structure of the periapical lesions.

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“…The proposed texture measures use statistical (the texture can be defined by the local statistical properties of pixel gray levels, e.g., histogram or methods based on secondorder statistics [5][6][7]) or structural approaches (texture can be represented by primitives and a hierarchy of spatial arrangements of those primitives, e.g., mathematical morphology [8,9]). Another possibility includes model-based approaches (e.g., fractals [10,11] and Markov fields [12] or on Wold decomposition [13]) and transform methods (Fourier-based [14], Gabor-based [11,15] or wavelets [11,16]). Recent studies have demonstrated that the fractal methods are considered appropriate for quantifying changes in bone structures.…”

Section: Introductionmentioning

confidence: 99%

“…Wagle et al [20] used fractal analysis to quantify the complex morphology of PDL-bone interface. In our investigations [10,21], fractal analysis was used in the case of the healing process after bone loss. During the healing process after bone loss, the surfaces of radiographic images became smooth.…”

Section: Introductionmentioning

confidence: 99%

Fractal dimension in textures analysis of xenotransplants

Borowska

Borys

Szarmach

et al. 2017

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Evaluation of the effectiveness of the healing process in postresectal and postcystal bone loss cases using techniques guided bone regeneration, observed within 1-year-long period. Radiographic images of 20 patients (17 females and 8 males) who had undergone xenotransplantation to fill jawbone losses were analyzed. The combination therapy of intraosseous deficits following xenotransplantation consisted of bone augmentation with xenogenic material together with covering regenerative membranes and tight wound closure. The bone regeneration process was estimated comparing the images taken on the day of the surgery and 12 months later, by means of digital radiography set Kodak RVG 6100. The interpretation of the RVG image depends on the assessment ability of the eye looking at it, which gives a large margin of uncertainty. Areas of interest were separated from radiographic images and binarized. On the basis of those fragments, box-counting dimension (D B ) and information dimension (D I ) were calculated. fractal dimension equals 1.0 right after the surgery and 1.6 after a year after the bone treatment, and we could use image segmentation to efficiently find fragments where this value differs from those acquired in the course of tests and quicken diagnostics of irregularities in bone tissue regeneration.

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Fractal texture analysis of the healing process after bone loss

Cited by 22 publications

References 29 publications

Caries detection enhancement using texture feature maps of intraoral radiographs

Caries detection enhancement using texture feature maps of intraoral radiographs

Use of Texture Feature Maps for the Refinement of Information Derived from Digital Intraoral Radiographs of Lytic and Sclerotic Lesions

Fractal dimension in textures analysis of xenotransplants

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