Fractal dimension and lacunarity analysis of mandibular bone on digital panoramic radiographs of tobacco users

Background. This study aimed to evaluate and compare changes in the mandibular trabecular bone pattern using the fractal dimension (FD) and lacunarity analysis in tobacco users with healthy controls. Methods. This study was carried out on digital panoramic radiographs of 225 subjects divided into three groups: smokeless tobacco users (SLTs), smokers, and control (n=75). ImageJ program with FracLac plugin was used to assess the FD and lacunarity of mandibular trabecular bone on the digital panoramic radiographs. Results. The differences in the mean FD values of the study and control groups were statistically significant (P<0.001). Mean FD was lower in the case groups than the control group, with SLTs having the least FD value. A significant difference in lacunarity was noted between SLTs and controls (P<0.001). On the contrary, there was no significant difference in lacunarity between smokers and controls. Conclusions. FD values were lower in tobacco users, suggesting that tobacco users have a less complex trabecular bone pattern than healthy controls. Higher lacunarity values in SLTs indicated a more heterogeneous bone pattern. These findings signify that FD and lacunarity analysis on digital panoramic radiographs can serve as promising predictive tools to assess bone quality for osteoporotic changes in tobacco users, thereby facilitating prompt referral for further management.

Estimation of spatial distribution and symmetry of textile materials using lacunarity

Numerical classification of textile materials, aramid, viscose, and PAN/WV, is proposed using lacunarity analysis of monochromatic digital representation of optical microscopic images. The method is sensitive to the spatial distribution of fibers, and equivalently, to the empty spaces between them. This means that lacunarity is able to quantitatively express a given level of spatial in-plane symmetries of single-face fabrics.

Surface texture characterization of selective laser melted Ti-6Al-4V components using fractal dimension and lacunarity analysis

The Selective Laser Melting (SLM) process based additive manufacturing has wide applications in medical, aerospace, defense, and automotive industries. To qualify the components for certain tribological applications, the characterization of surface texture is very important. But the applicability of traditional methods and parameters to characterize the surface texture were under evaluation. As the nature manufacturing the components were very different and complex, the unconventional surface characterization methods also under evaluation to reveal much more meaningful information. This study demonstrates the surface characterization of Ti-6Al-4V SLM components using fractal analysis of the surface images. The computed fractal dimension using the Fourier transform method showed a strong correlation of more than 0.8 with the measured 3D surface roughness parameters. The change in anisotropic nature of the surface images with the process parameter variation is studied and found that the surface textures showed a weaker anisotropic nature at lower laser power ranges, high scanning speed, and high hatch distance values. The lacunarity analysis is carried out using the gliding box algorithm to study the homogeneity nature of the surface texture and found that the surface texture is more homogeneous at higher surface roughness conditions. The study results can be utilized for the development of a quick, low-cost surface monitoring system in real-time for additive manufacturing industries.

3D soil void space lacunarity as an index of degradation after land use change

In this work, lacunarity analysis is performed on soil pores segmented by the pure voxel extraction method from soil tomography images. The conversion of forest to sugarcane plantation was found to result in higher sugarcane soil pore lacunarity than that of native forest soil, while the porosity was found to be lower. More precisely, this study shows that native forest has more porous soil with a more uniform spatial distribution of pores, while sugarcane soil has lower porosity and a more heterogeneous pore distribution. Moreover, validation through multivariate statistics demonstrates that lacunarity can be considered a relevant index of clustering and can explain the variability among soils under different land use systems. While porosity by itself represents a fundamental concept for quantification of the impact of land use change, the current findings demonstrate that the spatial distribution of pores also plays an important role and that pore lacunarity can be adopted as a complementary tool in studies directed at quantifying the effect of human intervention on soils.

Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Mitochondrial DNA Profiling of Parkinson’s Disease

Increasing evidence implicates mitochondrial dysfunction in the etiology of Parkinson’s disease (PD). Mitochondrial DNA (mtDNA) mutations are considered a possible cause and this mechanism might be shared with the aging process and with other age-related neurodegenerative disorders such as Alzheimer’s disease (AD). We have recently proposed a computerized method for mutated mtDNA characterization able to discriminate between AD and aging. The present study deals with mtDNA mutation-based profiling of PD. Peripheral blood mtDNA sequences from late-onset PD patients and age-matched controls were analyzed and compared to the revised Cambridge Reference Sequence (rCRS). The chaos game representation (CGR) method, modified to visualize heteroplasmic mutations, was used to display fractal properties of mtDNA sequences and fractal lacunarity analysis was applied to quantitatively characterize PD based on mtDNA mutations. Parameter β, from the hyperbola model function of our lacunarity method, was statistically different between PD and control groups when comparing mtDNA sequence frames corresponding to GenBank np 5713-9713. Our original method, based on CGR and lacunarity analysis, represents a useful tool to analyze mtDNA mutations. Lacunarity parameter β is able to characterize individual mutation profile of mitochondrial genome and could represent a promising index to discriminate between PD and aging.

Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review

Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.