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Wanliss JA, Wanliss GE. Efficient calculation of fractal properties via the Higuchi method. NONLINEAR DYNAMICS 2022; 109:2893-2904. [PMID: 35765369 PMCID: PMC9223273 DOI: 10.1007/s11071-022-07353-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 03/08/2022] [Indexed: 05/29/2023]
Abstract
Higuchi's method of determining fractal dimension is an important, well-used, research tool that, compared to many other methods, gives rapid, efficient, and robust estimations for the range of possible fractal dimensions. One major shortcoming in applying the method is the correct choice of tuning parameter (k max); a poor choice can generate spurious results, and there is no agreed upon methodology to solve this issue. We analyze multiple instances of synthetic fractal signals to minimize an error metric. This allows us to offer a new and general method that allows determination, a priori, of the best value for the tuning parameter, for a particular length data set. We demonstrate its use on physical data, by calculating fractal dimensions for a shell model of the nonlinear dynamics of MHD turbulence, and severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1 from the family Coronaviridae.
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Affiliation(s)
- J. A. Wanliss
- Department of Physics, Presbyterian College, 503 S. Broad St., Clinton, SC 29325 USA
| | - Grace E. Wanliss
- Department of Physics, Presbyterian College, 503 S. Broad St., Clinton, SC 29325 USA
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Michels M, Morais-Faria K, Rivera C, Brandão TB, Santos-Silva AR, Oliveira ML. Structural complexity of the craniofacial trabecular bone in multiple myeloma assessed by fractal analysis. Imaging Sci Dent 2022; 52:33-41. [PMID: 35387107 PMCID: PMC8967490 DOI: 10.5624/isd.20210160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/30/2021] [Accepted: 09/24/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose This study aimed to evaluate the structural complexity of craniofacial trabecular bone in multiple myeloma by fractal analysis of panoramic and lateral skull radiography, and to compare the fractal dimension values of healthy patients (HPs), pre-treatment patients (PTPs), and patients during bisphosphonate treatment (DTPs). Materials and Methods Pairs of digital panoramic and lateral skull radiographs of 84 PTPs and 72 DTPs were selected. After application of exclusion criteria, 43 panoramic and 84 lateral skull radiographs of PTPs, 56 panoramic and 72 lateral skull radiographs of DTPs, and 99 panoramic radiographs of age- and sex-matched HPs were selected. The fractal dimension values from panoramic radiographs were compared among HPs, PTPs, and DTPs and between anatomical locations within patient groups using analysis of variance with the Tukey test. Fractal dimension values from lateral skull radiographs were compared between PTPs and DTPs using the Student t-test. Pearson correlation coefficients were used to assess the relationship between the mandible from panoramic radiographs and the skull from lateral skull radiographs. Intra-examiner agreement was assessed using intraclass correlation coefficients (α=0.05). Results The fractal dimension values were not significantly different among HPs, PTPs, and DTPs on panoramic radiographs or between PTPs and DTPs on lateral skull radiographs (P>0.05). The mandibular body presented the highest fractal dimension values (P≤0.05). The fractal dimension values of the mandible and skull in PTPs and DTPs were not correlated. Conclusion Fractal analysis was not sensitive for distinguishing craniofacial trabecular bone complexity in multiple myeloma patients using panoramic and lateral skull radiography.
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Affiliation(s)
- Mariane Michels
- Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, SP, Brazil
| | - Karina Morais-Faria
- Dental Oncology Service, São Paulo State Cancer Institute, São Paulo University Medical School, São Paulo, SP, Brazil
| | - César Rivera
- Oral Medicine and Pathology Research Group, Department of Basic Biomedical Sciences, Faculty of Health Sciences, University of Talca, Talca, Maule, Chile
| | - Thaís Bianca Brandão
- Dental Oncology Service, São Paulo State Cancer Institute, São Paulo University Medical School, São Paulo, SP, Brazil
| | - Alan Roger Santos-Silva
- Division of Oral Medicine, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, SP, Brazil
| | - Matheus L Oliveira
- Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, SP, Brazil
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Kashyap A, Rapsomaniki MA, Barros V, Fomitcheva-Khartchenko A, Martinelli AL, Rodriguez AF, Gabrani M, Rosen-Zvi M, Kaigala G. Quantification of tumor heterogeneity: from data acquisition to metric generation. Trends Biotechnol 2021; 40:647-676. [PMID: 34972597 DOI: 10.1016/j.tibtech.2021.11.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 01/18/2023]
Abstract
Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.
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Affiliation(s)
- Aditya Kashyap
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | | | - Vesna Barros
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Anna Fomitcheva-Khartchenko
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland; Eidgenössische Technische Hochschule (ETH-Zurich), Vladimir-Prelog-Weg 1-5/10, 8099 Zurich, Switzerland
| | | | | | - Maria Gabrani
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | - Michal Rosen-Zvi
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Govind Kaigala
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland.
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Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI. Med Biol Eng Comput 2021; 60:121-134. [PMID: 34729681 DOI: 10.1007/s11517-021-02464-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 10/21/2021] [Indexed: 12/29/2022]
Abstract
Magnetic Resonance Imaging (MRI) is used in everyday clinical practice to assess brain tumors. Deep Convolutional Neural Networks (DCNN) have recently shown very promising results in brain tumor segmentation tasks; however, DCNN models fail the task when applied to volumes that are different from the training dataset. One of the reasons is due to the lack of data standardization to adjust for different models and MR machines. In this work, a 3D spherical coordinates transform during the pre-processing phase has been hypothesized to improve DCNN models' accuracy and to allow more generalizable results even when the model is trained on small and heterogeneous datasets and translated into different domains. Indeed, the spherical coordinate system avoids several standardization issues since it works independently of resolution and imaging settings. The model trained on spherical transform pre-processed inputs resulted in superior performance over the Cartesian-input trained model on predicting gliomas' segmentation on Tumor Core and Enhancing Tumor classes, achieving a further improvement in accuracy by merging the two models together. The proposed model is not resolution-dependent, thus improving segmentation accuracy and theoretically solving some transfer learning problems related to the domain shifting, at least in terms of image resolution in the datasets.
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Djuričić GJ, Rajković N, Milošević N, Sopta JP, Borić I, Dučić S, Apostolović M, Radulovic M. Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness. Biomark Med 2021; 15:929-940. [PMID: 34236239 DOI: 10.2217/bmm-2020-0876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs. Patients & methods: Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Results: We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for Λ'(G) provided the best predictive association (area under the ROC curve = 0.88; p <0.001), followed by Y-axis intersection of the regression line for box fractal dimension, r² for FDM and tumor circularity. Conclusion: This is the first full-scale study to indicate that computational analysis of pretreatment MRIs could provide imaging biomarkers for the classification of osteosarcoma according to their chemoresponsiveness.
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Affiliation(s)
- Goran J Djuričić
- Department of Radiology, University Children's Hospital, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia
| | - Nemanja Rajković
- Department of Biophysics, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia
| | - Nebojša Milošević
- Department of Biophysics, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia
| | - Jelena P Sopta
- Institute of Pathology, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia
| | - Igor Borić
- St. Catherine Specialty Hospital, Zagreb, 10000, Croatia
| | - Siniša Dučić
- Department of Radiology, University Children's Hospital, School of Medicine, University of Belgrade, Belgrade, 11000, Serbia
| | - Milan Apostolović
- Department of Orthopaedic, Institute for Orthopaedic Surgery, "Banjica", Belgrade, 11040, Serbia
| | - Marko Radulovic
- Department of Experimental Oncology, Institute for Oncology & Radiology of Serbia, Belgrade, 11000, Serbia
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Effects of Continuous Positive Airway Pressure on Sleep EEG Characteristics in Patients with Primary Central Sleep Apnea Syndrome. Can Respir J 2021; 2021:6657724. [PMID: 33976751 PMCID: PMC8084662 DOI: 10.1155/2021/6657724] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/19/2021] [Accepted: 04/05/2021] [Indexed: 11/17/2022] Open
Abstract
This study aimed to investigate the effects of continuous positive airway pressure (CPAP) on the electroencephalographic (EEG) characteristics of patients with primary central sleep apnea syndrome (CSAS). Nine patients with primary CSAS were enrolled in this study. The raw sleep EEG data were analyzed based on two main factors: fractal dimension (FD) and zero-crossing rate of detrended FD. Additionally, conventional EEG spectral analysis in the delta, theta, alpha, and beta bands was conducted using a fast Fourier transform. The FD in patients with primary CSAS who underwent CPAP treatment was significantly decreased during nonrapid eye movement (NREM) sleep but increased during rapid eye movement (REM) sleep (p < 0.05). Regarding the EEG spectral analysis, the alpha power increased, while the delta/alpha ratio decreased during REM sleep in patients with CSAS (p < 0.05). In conclusion, CPAP treatment can reduce FD in NREM sleep and increase FD during REM sleep in patients with primary CSAS. FD may be used as a new biomarker of EEG stability and improvement in brain function after CPAP treatment for primary CSAS.
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Lopci E, Burnelli R, Elia C, Piccardo A, Castello A, Borsatti E, Zucchetta P, Cistaro A, Mascarin M. Additional value of volumetric and texture analysis on FDG PET assessment in paediatric Hodgkin lymphoma: an Italian multicentric study protocol. BMJ Open 2021; 11:e041252. [PMID: 33782017 PMCID: PMC8009231 DOI: 10.1136/bmjopen-2020-041252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
INTRODUCTION Assessment of response to therapy in paediatric patients with Hodgkin lymphoma (HL) by 18F-fluorodeoxyglucose positron emission tomography/CT has become a powerful tool for the discrimination of responders from non-responders. The addition of volumetric and texture analyses can be regarded as a valuable help for disease prognostication and biological characterisation. Based on these premises, the Hodgkin Lymphoma Study Group of the Associazione Italiana Ematologia Oncologia Pediatrica (AIEOP) has designed a prospective evaluation of volumetric and texture analysis in the Italian cohort of patients enrolled in the EuroNet-PHL-C2. METHODS AND ANALYSIS The primary objective is to compare volumetric assessment in patiens with HL at baseline and during the course of therapy with standard visual and semiquantitative analyses. The secondary objective is to identify the impact of volumetric and texture analysis on bulky masses. The tertiary objective is to determine the additional value of multiparametric assessment in patients having a partial response on morphological imaging.The overall cohort of the study is expected to be round 400-500 patients, with approximately half presenting with bulky masses. All PET scans of the Italian cohort will be analysed for volumetric assessment, comprising metabolic tumour volume and total lesion glycolysis at baseline and during the course of therapy. A dedicated software will delineate semiautomatically contours using different threshold methods, and the impact of each segmentation techniques will be evaluated. Bulky will be defined on contiguous lymph node masses ≥200 mL on CT/MRI. All bulky masses will be outlined and analysed by the same software to provide textural features. Morphological assessment will be based on RECIL 2017 for response definition. ETHICS AND DISSEMINATION The current study has been ethically approved (AIFA/SC/P/27087 approved 09/03/2018; EudraCT 2012-004053-88, EM-04). The results of the different analyses performed during and after study completion the will be actively disseminated through peer-reviewed journals, conference presentations, social media, print media and internet.
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Affiliation(s)
- Egesta Lopci
- Nuclear Medicine Department, IRCCS - Humanitas Research Hospital, Rozzano, Italy
| | - Roberta Burnelli
- Pediatric Onco-hematologic Unit, University Hospital Arcispedale Sant'Anna of Ferrara, Ferrara, Italy
| | - Caterina Elia
- AYA Oncology and Pediatric Radiotherapy Unit, Centro di Riferimento Oncologico, Aviano, Italy
| | - Arnoldo Piccardo
- Nuclear Medicine Department, Ente Ospedaliero Ospedali Galliera, Genova, Italy
| | - Angelo Castello
- Nuclear Medicine Department, IRCCS - Humanitas Research Hospital, Rozzano, Italy
| | - Eugenio Borsatti
- Nuclear Medicine Department, Centro di Riferimento Oncologico, Aviano, Italy
| | - Pietro Zucchetta
- Nuclear Medicine Department, Padua University Hospital, Padova, Italy
| | - Angelina Cistaro
- Nuclear Medicine Department, Ente Ospedaliero Ospedali Galliera, Genova, Italy
| | - Maurizio Mascarin
- AYA Oncology and Pediatric Radiotherapy Unit, Centro di Riferimento Oncologico, Aviano, Italy
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Grigoreva L, Razdolsky A, Kazachenko V, Strakhova N, Grigorev V. Memory Effect in the Spatial Series Based on Diamond and Graphite Crystals. Molecules 2020; 25:molecules25225387. [PMID: 33217929 PMCID: PMC7698598 DOI: 10.3390/molecules25225387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 11/11/2020] [Accepted: 11/16/2020] [Indexed: 11/23/2022] Open
Abstract
To study the relation between the structure of a compound and its properties is one of the fundamental trends in chemistry and materials science. A classic example is the well-known influence of the structures of diamond and graphite on their physicochemical properties, in particular, hardness. However, some other properties of these allotropic modifications of carbon, e.g., fractal properties, are poorly understood. In this work, the spatial series (interatomic distance histograms) calculated using the crystal structures of diamond and graphite are investigated. Hurst exponents H are estimated using detrended fluctuation analysis and power spectral density. The values of H are found to be 0.27–0.32 and 0.37–0.42 for diamond and graphite, respectively. The calculated data suggest that the spatial series have long memory with a negative correlation between the terms of the series; that is, they are antipersistent.
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Affiliation(s)
- Ludmila Grigoreva
- Faculty of Fundamental Physical and Chemical Engineering, Lomonosov Moscow State University, Leninskiye Gory 1/51, 119991 Moscow, Russia;
| | - Alexander Razdolsky
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds of the Russian Academy of Sciences, Severny proezd 1, 142432 Chernogolovka, Russia; (A.R.); (V.K.); (N.S.)
| | - Vladimir Kazachenko
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds of the Russian Academy of Sciences, Severny proezd 1, 142432 Chernogolovka, Russia; (A.R.); (V.K.); (N.S.)
| | - Nadezhda Strakhova
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds of the Russian Academy of Sciences, Severny proezd 1, 142432 Chernogolovka, Russia; (A.R.); (V.K.); (N.S.)
| | - Veniamin Grigorev
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds of the Russian Academy of Sciences, Severny proezd 1, 142432 Chernogolovka, Russia; (A.R.); (V.K.); (N.S.)
- Correspondence:
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Castello A, Russo C, Grizzi F, Qehajaj D, Lopci E. Prognostic Impact of Intratumoral Heterogeneity Based on Fractal Geometry Analysis in Operated NSCLC Patients. Mol Imaging Biol 2020; 21:965-972. [PMID: 30478506 DOI: 10.1007/s11307-018-1299-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
PURPOSE To determine the heterogeneity of glucose uptake applying fractal analysis on positron emission tomography/computed tomography (PET/CT) with 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) images in patients with non-small cell lung carcinoma (NSCLC) before surgery, and to assess whether this heterogeneity was associated with disease-free survival (DFS). PROCEDURES [18F]FDG PET/CT scans of 113 patients' prior surgery were retrospectively revised. PET DICOM images were analyzed for fractal geometry using a ad hoc software to automatically determine the following indexes: (a) mean intensity value (MIV), (b) standard deviation (SD), (c) relative dispersion (RD), (d) three-dimensional (3D) histogram of the fractal dimension (3D HIST FR DIM), and (e) fractal dimension in 3D (3D-FD). All the fractal indexes were subsequently compared with metabolic parameters and disease-free survival (DFS). RESULTS We found a significant correlation between 3D-FD and SUVmax, SUVmean, MTV, and TLG. Additionally, positive correlations between MIV, SD, and all metabolic parameters were also detected. Patients with high 3D-FD tumor (≥ 1.62) showed significantly higher values of SUVmax, SUVmean, MTV, and TLG than those with lower 3D-FD. In univariate analysis, median 3D-FD and median TLG were significantly associated with DFS (p = 0.04 and p = 0.03, respectively). These findings were confirmed on log-rank test. On multivariate analysis, among age, stage disease, histotype, 3D-FD, and metabolic parameters, only 3D-FD was identified as independent prognostic factor for DFS (p = 0.032; HR 0.418, 95 % CI 0.189-0.926). 3D-FD was different between adenocarcinoma and squamous cell carcinoma (1.60 versus 1.88, p = 0.014), and 3D-FD value was found higher in advanced stage disease. CONCLUSIONS Metabolic heterogeneity determined applying fractal principles on PET images can be considered as a novel imaging biomarker for survival in patients with NSCLC.
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Affiliation(s)
- Angelo Castello
- Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Via Manzoni 56, 20089, Rozzano, MI, Italy
| | | | - Fabio Grizzi
- Department of Immunology and Inflammation, Humanitas Clinical and Research Hospital, Rozzano, MI, Italy
| | - Dorina Qehajaj
- Department of Immunology and Inflammation, Humanitas Clinical and Research Hospital, Rozzano, MI, Italy
| | - Egesta Lopci
- Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Via Manzoni 56, 20089, Rozzano, MI, Italy.
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Fractal Dimension Analysis of High-Resolution X-Ray Phase Contrast Micro-Tomography Images at Different Threshold Levels in a Mouse Spinal Cord. CONDENSED MATTER 2018. [DOI: 10.3390/condmat3040048] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Fractal analysis is a powerful method for the morphological study of complex systems that is increasingly applied to biomedical images. Spatial resolution and image segmentation are crucial for the discrimination of tissue structures at the multiscale level. In this work, we have applied fractal analysis to high-resolution X-ray phase contrast micro-tomography (XrPCμT) images in both uninjured and injured tissue of a mouse spinal cord. We estimated the fractal dimension (FD) using the box-counting method on tomographic slices segmented at different threshold levels. We observed an increased FD in the ipsilateral injured hemicord compared with the contralateral uninjured tissue, which was almost independent of the chosen threshold. Moreover, we found that images exhibited the highest fractality close to the global histogram threshold level. Finally, we showed that the FD estimate largely depends on the image histogram regardless of tissue appearance. Our results demonstrate that the pre-processing of XrPCμT images is critical to fractal analysis and the estimation of FD.
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