• Applied Microscopy
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• v.51
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• pp.6.1-6.9
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• 2021

Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different (p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promising statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p < 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.

• Journal of Korea Multimedia Society
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• v.4 no.6
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• pp.518-523
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• 2001

This paper proposes the new method which is adopted in extracting character features and recognizing characters using fractal dimension of the Chaos theory which highly recolonizes a minute difference with strange attractor created from Henon system. This paper implements a high precision character recognition system. firstly, it gets features of mesh, projection and cross distance feature from character images. And their feature is converted into data of time series. Then using modified Henon system suggested in this paper, each characters attractor about standard Korean Character, KSC 5601 is reconstructed. Secondly, in order to analyze the Chaotic degree of each characters attractor, it gets last features of character image after calculating box-counting Dimension, Natural Measure, Information Bit, Information Dimension which are meant fractal dimension. An experimental result shows 97.49% character classification rates for 2350 Korean characters using proposed method in this paper.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.33 no.5
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• pp.494-498
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• 2007

Purpose: This study was done to know the usefulness of fractal analysis when evaluating the radiologic changes after decompression on jaw bone cystic lesions using fractal analysis. Materials and methods: 30cases of cystic lesions were followed up after decompression. Panoramic image was used to observe radiologic changes around the cystic lesion. The part of the panoramic image which showed radiologic change was defined as region of interest(ROI); The fractal dimension of the ROI was calculated using box-counting method. Results: Using sign-rank test, there was a statistically significant difference in fractal dimensions after decompression therapy(P<0.0001). The fractal dimensions statistically increased after decompression(the median of D:0.12). Conclusions: The ROI after decompression showed higher fractal dimensions which offer the objective proof of the bone healing around cystic lesions after decompression treatment.

• Imaging Science in Dentistry
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• v.36 no.4
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• pp.183-188
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• 2006

Purpose: The purpose of this research was to study bone changes after bilateral sagittal split osteotomy through fractal analysis and measurement of mandibular cortical thickness. Materials and Methods: This study included twenty-two prognathic patients who underwent bilateral sagittal split osteotomy. Panoramic radiographs of these patients were taken immediately before operation and at 1 month, 6 months, and 12 months postoperatively. The fractal dimension was measured by the box-counting method in the region of interest centered on both the basal and interdental bones between the first and second mandibular molars. Measurements of mandibular cortical thickness were taken both in the area between the first and second mandibular molars and at the osteotomy site. Changes of fractal dimension and cortical thickness over four stages were statistically analyzed. Results: The fractal dimension of the mandibular basal bone before surgery and after 1 month, 6 months and 12 months were $1.4099{\pm}0.0657,\;1.382{\pm}0.0595,\;1.2995{\pm}0.0949,\;and\;1.4166{\pm}0.0676$, respectively (Repeated-measures ANOVA, P<0.001). However, no statistically significant differences were noted in interdental fractal dimensions among the four stages. Mandibular cortical thickness between the first and second mandibular molars before operation and after 1 month, 6 months and 12 months was $3.74{\pm}0.48mm,\;3.63{\pm}0.47mm,\;3.41{\pm}0.61mm\;and\;3.55{\pm}0.66mm$ (P<0.01), respectively. Mandibular cortical thickness at the osteotomy site at each of the four stages was $3.22{\pm}0.44mm,\;2.87{\pm}0.59mm,\;2.37{\pm}0.61mm\;and\;2.64{\pm}0.62mm$, respectively (P<0.001). Conclusion: This study suggests that the mandibular tissue continued decreasing for 6 months postoperatively and then increased over the subsequent 6 months.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1825-1831
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• 1999

In this paper, to reduce misrecognized characters, we propose the new method that extract features from character to apply to the character recognition using features from character to apply to the character recognition using fractal dimensions and attractors. Firstly, to reduce the load of recognizer we classify the characters. For the classified character, we extract the features for Box-counting dimensions. Natural Measures, Information dimensions then recognize characters. With histogram, we generate attractors and calculate dimensions from attractors. Then we recognize characters with dimensions of characters and attractors. An experimental result that the overall recognition rates for the training data and testing data are 96.03% and 91.74% respectively. This result shows the effectiveness of proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.372-375
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• 2003

The incidence of blindness resulting from diabetic retinopathy has significantly increased despite the intervention of insulin to control diabetes mellitus. Early signs are microaneurysms, exudates, intraretinal hemorrhages, cotton wool patches, microvascular abnormalities, and venous beading. Advanced stages include neovascularization, fibrous formations, preretinal and vitreous microhemorrhages, and retinal detachment. Microaneurysm count is important because it is an indicator of retinopathy progression. The purpose of this paper is to apply data mining to detect diabetic retinopathy patterns in routine fundus fluorescein angiography. Early symptoms are of principal interest and therefore the emphasis is on detecting microaneurysms rather than vessel tortuosity. The analysis does not involve image-recognition algorithms. Instead, mathematical filtering isolates microaneurysms, microhemorrhages, and exudates as objects of disconnected sets. A neural network is trained on their distribution to return fractal dimension. Hausdorff and box counting dimensions grade progression of the disease. The field is acquired on fluorescein angiography with resolution superior to color ophthalmoscopy, or on patterns produced by physical or mathematical simulations that model viscous fingering of water with additives percolated through porous media. A mathematical filter and neural network perform the screening process thereby eliminating the time consuming operation of determining fractal set dimension in every case.

• Imaging Science in Dentistry
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• v.40 no.3
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• pp.123-129
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• 2010

Purpose : To evaluate the effect of surgical treatment of ligature-induced peri-implantitis in dogs using fractal analysis. Also, the capabilities of fractal analysis as bone analysis techniques were compared with those of histomorphometric analysis. Materials and Methods : A total of 24 implants were inserted in 6 dogs. After a 3-months, experimental periimplantitis characterized by a bone loss of about 3 mm was established by inducing with wires. Surgical treatment involving flap procedure, debridement of implants surface with chlorhexidine and saline (group 1), guided bone regeneration (GBR) with absorbable collagen membrane and mineralized bone graft (group 2), and $CO_2$ laser application with GBR (group 3) were performed. After animals were sacrificed in 8 and 16 weeks respectively, bone sections including implants were made. Fractal dimensions were calculated by box-counting method on the skeletonized images, made from each region of interest, including five screws at medial and distal aspects of implant, were selected. Results : Statistically significant differences in the fractal dimensions between the group 1($0.9340{\pm}0.0126$) and group 3($0.9783{\pm}0.0118$) at 16 weeks were found (P<0.05). The fractal dimension was statistically significant different between 8($0.9395{\pm}0.0283$) and 16 weeks in group 3 (P<0.05). These results were similar with the result of the evaluation of new bone formation in histomorphometric analysis. Conclusions : Treatment of experimental peri-implantitis by using $CO_2$ laser with GBR is more useful than other treatments in the formation of new bone and also the tendency of fractal dimension to increase relative to healing time may be a useful means of evaluating.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.135-140
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• 2000

The characteristics of mechanical behavior were estimated for Ti-6Al-4V alloy with four kinds of microstructure prepared with heat treatments. For this study, impact test, tensile test and fatigue crack growth test were performed, and then compared mechanical properties on the four microstructures. Furthermore, for quantitative evaluation, fractal dimensions of crack pass were obtained using the box counting method. The main results obtained are summarized as follows. (1) The microstructures exhibited equiaxed microstructure, bimodal-microstructure and lamellar microstructure by heat treatment. (2) The impact absorbed energy and elongation is superior in the bimodal-microstructure, and the hardness and tensile strength are superior in the lamellar microstructure. (3) The fatigue crack growth rate is similar to all microstructures in the low ${\Delta}K$ region. The fatigue crack growth rate of equiaxed microstructure is fastest, and that of lamellar microstructure is lowest in the high ${\Delta}K$ region. (4) The fractal dimension D of lamellar microstructure is higher then that of the equiaxed microstructure and bimodal microstructure.

• Journal of the Korean Society of Safety
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• v.16 no.1
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• pp.31-36
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• 2001

Surface crack growth characteristics and influence of the stress amplitude in rotary bending fatigue test were evaluated for annealed S35C steel, and than fractal dimensions of fatigue crack paths estimated using the box counting method. The following results that will be helpful to understand the fatigue crack growth mechanism were obtained. (1) Crack growth rate ds/dN and db/dN (s : half crack length at the surface crack, b : crack depth) depended on stress amplitude (${\Delta}{\sigma}/2$), stress intensity factor range (${\Delta}K_A, {\Delta}K_C$) and crack length. (2) At the effect area of 0.3 mm hole notch (s＜0.5 mm) crack growth rate did not depend on these factors. (3) The fractal dimensions (D) increased with stress amplitude (${\Delta}{\sigma}/2$) but decreased with cyclic number.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.131-135
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• 2007

The tidal channel is influenced by sediment type, grain size, grain composition and tidal currents in tidal flat. The development of tidal channel including density, shape and order can be used to analyze the characteristics of tidal channel. The quantitative investigation to the tidal channel is insufficiency. In this paper, we represented the fractal analysis method according to the quantitatively analysis in tidal channel and compared with the different intertidal channel patterns. The tidal channel was extracted from the IKONOS image of the southern part of the Kanghwa-do. We used the Box-counting method to estimate fractal dimensions for each tidal channel. As a result, the fractal dimension values (D) were 1.31 in the southern Kanghwa-Do. Linear pattern and less dense channel development area had low D values (from 1.0563 to 1.0672). Dendritic pattern and dense channel development area had high D values (from 1.2550 to 1.3016). In other words, fractal dimension values had difference about 0.2 values according to the characteristic of tidal channel development. We concluded that fractal analysis can be able to quantitatively classification in tidal channel.