Bio-medical and latent fingerprint enhancement and matching using advanced scalable soft computing models

Manickam, Adhiyaman; Ezhilmaran, D.; Manogaran, Gunasekaran; Chilamkurti, Naveen; Vijayan, V.; Saraff, Shubham; Samuel, R. Dinesh Jackson; Krishnamoorthy, Raja

doi:10.1007/s12652-018-1152-1

Cited by 13 publications

(5 citation statements)

References 37 publications

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“… Image quality and its impact on the accuracy of matchers [ 132 ]. Latent print matching using minutiae [ 133 ], sweat pores [ 134 ], pores in conjunction with ridge skeleton [ 135 ], extended minutiae types such as enclosures and crossings [ 136 ], improving on the minutiae matching algorithms [ 137 ], dealing with overlapping marks [ 138 ] or taking advantage of SIFT [ 139 , 140 ] or deep learning techniques [ 141 , 142 ]. A review of the minutiae-encoding systems for palm prints [ 143 ].…”

Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions 2016–2019

Bécue

Eldridge

Champod

2020

Forensic Science International: Synergy

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Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions 2016–2019

Bécue

Eldridge

Champod

2020

Forensic Science International: Synergy

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“…Among the existing fuzzy-based image contrast enhancement schemes, some of the important ones are brightness-preserving dynamic fuzzy histogram equalization (BPDFHE) [10], fuzzy enhancement for low-exposure images (FELI) [11], parameter-free fuzzy histogram equalisation (PFHE) [12][13], fuzzy contrast intensification (FCI) [14][15][16], fuzzy theory-based adaptive image enhancement (FTAIE) [17], optimum fuzzy system for image enhancement (OFSIE) [18], fuzzy logic-based image enhancement (FLIE) [19], type-2 fuzzy contrast enhancement (Type-2 FCE) [20] and contrast enhancement based on type-2 intuitionistic fuzzy set (Type 2 IFS FCE) [21].…”

Section: Review On Fuzzy-based Image Contrast Enhancement Techniquesmentioning

confidence: 99%

“…In the Type 2 FCE [20] and the Type 2 IFS FCE [21], adding uncertainty by mapping the normalized intensity values that constitute the type-1 fuzzy membership function to its type 2 counterpart, significantly alters the diagnostically important inherent brightness characteristics of the MRI. The block-wise processing method used in the Type 2 IFS FCE [21] causes computational overload and may induce blockiness in the enhanced images at small block sizes.…”

Section: Limitations Of the Existing Fuzzy-based Image Contrast Enhan...mentioning

confidence: 99%

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Minimum Intensity Error Intuitionistic Fuzzy Contrast Enhancement Transformation for Magnetic Resonance Images

Joseph

V.R.

2023

Preprint

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Background Low contrast in magnetic resonance (MR) images adversely affects the performance of software tools used for its automated analysis. Compared to the traditional crisp transformation functions often used for enhancing the MR images, it is easy for the users to customize the fuzzy contrast transformation functions in order to selectively improve the contrast among the grey levels within any desired region of the dynamic range. However, computationally fast fuzzy-based image contrast enhancement techniques with good ability to preserve the mean brightness level are rare. Objectives To resolve these issues, a computationally fast algorithm with good ability to preserve the mean brightness level, named minimum intensity error intuitionistic fuzzy contrast enhancement transformation (MIEIFCET) is proposed in this paper. Methods In the MIEIFCET, the fuzzy set obtained from the input image by normalizing the pixel values in it, is represented as an intuitionistic fuzzy set (IFS) with the help of the Atanassov intuitionistic fuzzy generator. The contrast among the membership values in the IFS is improved by stretching them to the extremes by applying a customized fuzzy decision rule. The contrast-enhanced image is obtained by expanding the stretched membership values in the IFS to the full possible dynamic range. Results On 100 MR images, the coefficient of variation (COV) of the output images of brightness-preserving dynamic fuzzy histogram equalization (BPDFHE), fuzzy contrast intensification (FCI), fuzzy enhancement for low-exposure images (FELI), fuzzy theory-based adaptive image enhancement (FTAIE), optimum fuzzy system for image enhancement (OFSIE), parameter-free fuzzy histogram equalisation (PFHE), type-2 fuzzy contrast enhancement (Type-2 FCE), fuzzy logic-based image enhancement (FLIE), contrast enhancement based on type‑2 intuitionistic fuzzy set (Type 2 IFS FCE), and MIEIFCET are 0.9023 ± 0.0751, 1.1219 ± 0.2526, 0.8992 ± 0.1263, 0.7236 ± 0.1644, 0.5315 ± 0.0445, 1.0664 ± 0.1517, 0.9199 ± 0.2441, 1.1895 ± 0.1863, 0.9331 ± 0.2312, and 1.2946 ± 0.1537, respectively. The computational time (sec) of BPDFHE, FCI, FELI, FTAIE, OFSIE, PFHE, Type 2 FCE, FLIE, Type 2 IFS FCE, and MIEIFCET are 0.1272 ± 0.1976, 0.0464 ± 0.0147, 0.7249 ± 0.2948, 0.0357 ± 0.0235, 0.3136 ± 0.1465, 0.3136 ± 0.1465, 0.0333 ± 0.0238, 0.0855 ± 0.0581, 0.0443 ± 0.0279 and 0.0169 ± 0.0155, respectively. Conclusion Objective results in terms of highest COV and lowest computational time prove that the MIEIFCET has the ability to enhance the contrast of MR images without causing any significant change in the mean brightness level and it is computationally fast.

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“…In addition to this discussed metrics, the excellence of the Oral cavity linked neurological disorders detection system efficiency is further evaluated using Matthews's correlation metric that is calculated using Eqn (17) [53].…”

Section: 4mentioning

confidence: 99%

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

et al. 2020

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Anatomically, oral cavity and central nervous system have a close relationship; the mouth and face are the location for 30-40% of the body's sensory and motor nerves. The identification of orofacial manifestations of neurological disorders is usually in direct relation with the responsibilities of a dental surgeon. Therefore, familiarizing dental surgeons with theses manifestations is essential to have better recognition, diagnosis, and correct decisions upon treating their associated Neurological Disorders. These manifestations should be efficiently analyzed using novel effective techniques since their related neurological disorders need to be early identified to avoid serious consequences. Furthermore, preventive dental care for patients with neurological disorders and all kind of rehabilitative treatments necessitates well-planned and effective novel approaches. The Internet of Medical Thing (IoMT) is a relatively new technology that has revolutionized health care sector drastically since last decade. It's a combination of medical sensors and wearable devices that can connect to health care storage systems via networking technologies allowing the transfer of medical data over a secure network. The data transferred are of utmost importance in diseases diagnosis and treatment. In this paper, an IoMT-based Intelligent Guided Particle Local Search with Optimized Neural Networks (IGPLONN) approach is proposed. Firstly, dental data are collected from the International Collaboration on Cancer Reporting (ICCR) oral cavity and central nervous system. Secondly, features are extracted from data and IGPLONN algorithm is utilized to select the effective features by minimizing the feature dimension that helps improve the overall prediction rate. Finally, the obtained features are transferred to the central health application through the IoMT platform where they can be analyzed by dental practitioners for neurological disorders prediction. The hybrid optimized technique improves the overall oral-linked neurological diseases detection rate. Moreover, it efficiently manages the forecast parameters that are used to predict the dental metastasis with minimum computational complexity. The performance of the proposed system has been experimentally evaluated on MATLAB to verify its excellence. The results revealed that proposed IoMT-based IGPLONN method attains the maximum accuracy of 98.3% compared to other methods. INDEX TERMS Oral cavity, neurological disorder, central nervous system, intelligent guided particle local search algorithm, neural network, Internet of Medical Things (IoMT).

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Bio-medical and latent fingerprint enhancement and matching using advanced scalable soft computing models

Cited by 13 publications

References 37 publications

Interpol review of fingermarks and other body impressions 2016–2019

Interpol review of fingermarks and other body impressions 2016–2019

Minimum Intensity Error Intuitionistic Fuzzy Contrast Enhancement Transformation for Magnetic Resonance Images

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

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