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Yang L, Zhang M, Cheng J, Zhang T, Lu F. Retina images classification based on 2D empirical mode decomposition and multifractal analysis. Heliyon 2024; 10:e27391. [PMID: 38509989 PMCID: PMC10950613 DOI: 10.1016/j.heliyon.2024.e27391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 02/21/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024] Open
Abstract
Diabetic retinopathy is an ocular disease caused by long-term damage to the retina due to high blood sugar levels. Elevated blood sugar can impair the microvasculature in the retina, leading to vascular abnormalities and the formation of abnormal new blood vessels. These changes can manifest in the retina as hemorrhages, leaks, vessel dilation, retinal edema, and retinal detachment. The retinas of individuals with diabetes exhibit different morphologies compared to those without the condition. Most histological images cannot be accurately described using traditional geometric shapes or methods. Therefore, this study aims to evaluate and classify the morphology of retinas with varying degrees of severity using multifractal geometry. In the initial experiments, two-dimensional empirical mode decomposition was employed to extract high-frequency detailed features, and the classification process was based on the most relevant features in the multifractal spectrum associated with disease factors. To eliminate less significant features, the random forest algorithm was utilized. The proposed method achieved an accuracy of 96%, sensitivity of 96%, and specificity of 95%.
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Affiliation(s)
- Lei Yang
- School of Mechatronic Engineering and Automation, Shanghai University, China
| | - Minxuan Zhang
- School of Mechatronic Engineering and Automation, Shanghai University, China
| | - Jing Cheng
- College of Electrical Engineering, Sichuan University, China
| | - Tiegang Zhang
- School of Mechatronic Engineering and Automation, Shanghai University, China
| | - Feng Lu
- School of Mechatronic Engineering and Automation, Shanghai University, China
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Lahmiri S, Boukadoum M, Di Ieva A. Fractals in Neuroimaging. ADVANCES IN NEUROBIOLOGY 2024; 36:429-444. [PMID: 38468046 DOI: 10.1007/978-3-031-47606-8_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Several natural phenomena can be described by studying their statistical scaling patterns, hence leading to simple geometrical interpretation. In this regard, fractal geometry is a powerful tool to describe the irregular or fragmented shape of natural features, using spatial or time-domain statistical scaling laws (power-law behavior) to characterize real-world physical systems. This chapter presents some works on the usefulness of fractal features, mainly the fractal dimension and the related Hurst exponent, in the characterization and identification of pathologies and radiological features in neuroimaging, mainly, magnetic resonance imaging.
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Affiliation(s)
- Salim Lahmiri
- Department of Supply Chain & Business Technology Management, John Molson School of Business, Concordia University, Montreal, Canada
| | - Mounir Boukadoum
- RESMIQ, Labo microPro, Université du Québec à Montréal (UQAM), Montreal, Canada
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab & Macquarie Neurosurgery, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.
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Tenti JM, Hernández Guiance SN, Irurzun IM. Fractal dimension of diffusion-limited aggregation clusters grown on spherical surfaces. Phys Rev E 2021; 103:012138. [PMID: 33601584 DOI: 10.1103/physreve.103.012138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 01/04/2021] [Indexed: 01/18/2023]
Abstract
In this work we study the fractal properties of diffusion-limited aggregation (DLA) clusters grown on spherical surfaces. Diffusion-limited aggregation clusters, or DLA trees, are highly branched fractal clusters formed by the adhesion of particles. In two-dimensional media, DLA clusters have a fractal dimension D_{f}=1.70 in the continuous limit. In some physical systems, the existence of characteristic lengths leads us to model them as discrete systems. Such characteristic lengths may result also from limitations in measuring instruments, for example, the resolution of biomedical imaging systems. We simulate clusters for different particle sizes and examine the influence of discretization by exploring the systems in terms of the relationship between the particle size r and the radius of the sphere R. We also study the effect of stereographic projection on the fractal properties of DLA clusters. Both discretization and projection alter the fractal dimension of DLA clusters grown on curved surfaces and must be considered in the interpretation of photographic biomedical images.
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Affiliation(s)
- J M Tenti
- Facultad de Ciencias Exactas, Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas, CCT La Plata, Universidad Nacional de La Plata, B1904 La Plata, Buenos Aires, Argentine Republic
| | - S N Hernández Guiance
- Facultad de Ciencias Exactas, Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas, CCT La Plata, Universidad Nacional de La Plata, B1904 La Plata, Buenos Aires, Argentine Republic
| | - I M Irurzun
- Facultad de Ciencias Exactas, Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas, CCT La Plata, Universidad Nacional de La Plata, B1904 La Plata, Buenos Aires, Argentine Republic
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Gnanasekaran VS, Joypaul S, Sundaram PM. A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms. Curr Med Imaging 2020; 16:639-652. [DOI: 10.2174/1573405615666190903141554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 07/08/2019] [Accepted: 07/17/2019] [Indexed: 01/22/2023]
Abstract
Breast cancer is leading cancer among women for the past 60 years. There are no effective
mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier
stages so that unnecessary biopsy can be reduced. Although there are several imaging modalities
available for capturing the abnormalities in breasts, mammography is the most commonly used
technique, because of its low cost. Computer-Aided Detection (CAD) system plays a key role in
analyzing the mammogram images to diagnose the abnormalities. CAD assists the radiologists for
diagnosis. This paper intends to provide an outline of the state-of-the-art machine learning algorithms
used in the detection of breast cancer developed in recent years. We begin the review with
a concise introduction about the fundamental concepts related to mammograms and CAD systems.
We then focus on the techniques used in the diagnosis of breast cancer with mammograms.
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Affiliation(s)
| | - Sutha Joypaul
- AAA College of Engineering and Technology, Sivakasi 626123, Virudhunagar District, Tamil Nadu, India
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Verdú S, Barat JM, Grau R. Fresh-sliced tissue inspection: Characterization of pork and salmon composition based on fractal analytics. FOOD AND BIOPRODUCTS PROCESSING 2019. [DOI: 10.1016/j.fbp.2019.04.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Cognitive Functioning in Chiari Malformation Type I Without Posterior Fossa Surgery. THE CEREBELLUM 2019; 17:564-574. [PMID: 29766459 DOI: 10.1007/s12311-018-0940-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Chiari Malformation type I (CM-I) is a neurological disorder characterized by a displacement of the cerebellar tonsils through the foramen magnum into the spinal canal. Most research has focused on physical symptomatology but few studies include neuropsychological examinations. Moreover, although current research highlights the involvement of the cerebellum on higher cognitive functions, little is known about cognitive consequences associated with CM-I. The aim of this study is to analyze cognitive functioning between 39 CM-I patients and 39 healthy controls, matched by gender, age and years of education. Participants have been examined on a large battery of neuropsychological tests, including executive functioning, verbal fluency, spatial cognition, language, verbal memory, processing speed, facial recognition and theory of mind. Results show a poorer performance of the clinical group compared to the control group, even after controlling the effect of physical pain and anxious-depressive symptomatology. The findings suggest the presence of a generalized cognitive deficit associated with CM-I, which makes it necessary to focus attention not only on physical consequences, but also on cognitive ones.
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Juliano AF, Policeni B, Agarwal V, Burns J, Bykowski J, Harvey HB, Hoang JK, Hunt CH, Kennedy TA, Moonis G, Pannell JS, Parsons MS, Powers WJ, Rosenow JM, Schroeder JW, Slavin K, Whitehead MT, Corey AS. ACR Appropriateness Criteria® Ataxia. J Am Coll Radiol 2019; 16:S44-S56. [PMID: 31054758 DOI: 10.1016/j.jacr.2019.02.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 02/08/2019] [Indexed: 01/14/2023]
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García M, Amayra I, Lázaro E, López-Paz JF, Martínez O, Pérez M, Berrocoso S, Al-Rashaida M. Comparison between decompressed and non-decompressed Chiari Malformation type I patients: A neuropsychological study. Neuropsychologia 2018; 121:135-143. [DOI: 10.1016/j.neuropsychologia.2018.11.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 10/29/2018] [Accepted: 11/04/2018] [Indexed: 12/09/2022]
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Akar E, Kara S, Akdemir H, Kırış A. 3D structural complexity analysis of cerebellum in Chiari malformation type I. Med Biol Eng Comput 2017; 55:2169-2182. [PMID: 28589373 DOI: 10.1007/s11517-017-1661-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Accepted: 05/21/2017] [Indexed: 11/25/2022]
Abstract
Chiari malformation type I (CM-I), described by a descent of the cerebellar tonsils, is assumed to be a neurological developmental disorder. The aim of the present study was to investigate morphological variance in cerebellar sub-structures, including gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), using magnetic resonance (MR) images with three-dimensional (3D) fractal dimension (FD) analysis in patients with CM-I. MRI data of 16 patients and 15 control subjects were obtained, and structural complexity analyses were performed using a box-counting FD algorithm. Results showed that patients with CM-I had significantly reduced FD values for WM and CSF in comparison with controls, and statistically significant differences in cerebellar GM and CSF volumes between patients and controls were found. Moreover, a significant difference was not found between the WM volumes. This may suggest that there are changes in structural complexity in WM even when its volume is unaffected. We conclude that the findings of this preliminary study indicate the possibility of using FD analysis to understand the pathophysiology of CM-I in patients.
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Affiliation(s)
- Engin Akar
- Independent Researcher, Adnan Kahveci Mh. Konak Cd., Beyaz İnci Evleri B Blok No:19, 34528 Beylikdüzü, Istanbul, Turkey.
| | - Sadık Kara
- Independent Researcher, Istanbul, Turkey
| | - Hidayet Akdemir
- Department of Neurosurgery, Medicana International Hospital, Istanbul, Turkey
| | - Adem Kırış
- Department of Radiology, Mehmet Akif Ersoy Cardio-Thoracic Surgery Training and Research Hospital, Istanbul, Turkey
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Akar E, Kara S, Akdemir H, Kırış A. Fractal analysis of MR images in patients with chiari malformation: The importance of preprocessing. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.07.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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