Assessment of Laser Effects on Skin Rejuvenation

Hazhir heidari beigvand.

1 Firoozabadi Hospital, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran

Mohammadreza Razzaghi

2 Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Mohammad Rostami-Nejad

3 Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Majid Rezaei-Tavirani

Saeed safari.

4 Proteomics Research Center, Department of Emergency Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Mostafa Rezaei-Tavirani

5 Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Vahid Mansouri

Mohammad hossein heidari.

Laser skin resurfacing has changed the approach of facial skin rejuvenation over the past decade. This article evaluates the laser effects on skin rejuvenation by the assessment of laser characteristics and histological and molecular changes, accompanied by the expression of proteins during and after laser-assisted rejuvenation of skin. It is important to note that different layers of skin with different cells are normally exposed to the sun’s UV radiation which is the most likely factor in aging and damaging healthy skin. To identify the expression of proteins, using validated databases and reviewing existing data could reveal altered proteins which could be analyzed and mapped to investigate their expression and their different effects on cell biological responses. In this regard, proteomics data can be used for better investigation of the changes in the proteomic profile of the treated skin. Different assessments have revealed the survival and activation of fibroblasts and new keratinocytes with an increase of collagen and elastin fibers in the dermis and the reduction of matrix metalloproteinases (MMPs) and heat shock proteins (HSPs) as a result of different low-power laser therapies of skin. There are a wide range of biological effects associated with laser application in skin rejuvenation; therefore, more safety considerations should be regarded in the application of lasers in skin rejuvenation.

Introduction

Laser applications in medicine have been promoted in different fields such as dermatology, dentistry, ophthalmology, and surgery. 1 - 4 There are many documents about the widespread use of lasers in skin treatment, especially in skin rejuvenation. 5 - 7 Skin aging is a natural process that occurs as people age. However, it could be accelerated by such factors as sunlight, stress, and chemicals. Skin aging is affected by numerous genetic and environmental factors that can appear as wrinkles, abnormal pigmentation, skin weakness, and telangiectasia. 8 Researchers are increasingly looking for different ways to rejuvenate skin. Recently, the use of laser radiation for skin rejuvenation has become commonplace and has apparently been effective. The expansion and application of lasers and light for medical procedures based on the selective principle of photothermolysis have increased exponentially over the past two decades. The fundamental principle of this procedure is that selective heating is attained by preferential laser light absorption and heat manufacture in the target chromophore, with heat being localized to the target by pulse duration shorter than the thermal relaxation time of tissue. 9 This study examines the effect of the laser beam on skin rejuvenation in different aspects and reviews the published articles in this field to present a new perspective of laser application in skin rejuvenation. The study includes the research method, skin aging phenomena, skin photoaging histology, skin aging treatment, laser features and skin aging treatment, ablative lasers, nonablative lasers, fractional lasers, Photobiomodulation (PBM) lasers, laser effects on tissues, photothermolysis, molecular aspects of laser effects in cell biology, and conclusion parts.

The search engines of Scopus, Google Scholar, and PubMed were applied to search such keywords as “Skin”, Laser therapy”, “Rejuvenation”, “Skin Aged”, and “Proteomics”. The titles in English were identified and studied in such a way that the relevant articles were selected for more evaluation and assessment. The abstracts of 155 documents were investigated and the full texts of 134 articles were selected. After the review of 134 articles, 84 documents were chosen to be included in this study.

Skin Aging Phenomena

The clinical signs of skin aging include thinning skin, cigarette paper-like wrinkles, elasticity loss, and benign overgrowth or vascular formations such as keratosis or angioma. 10 These clinical signs appear by genetic factors of aging. UV irradiation induces photoaging and gravity, leading to ECM matrix changing to appear wrinkles. Therefore, these aging processes are accompanied by the phenotypic exchange in cutaneous cells as well as structural and functional changes in extracellular matrix components such as collagen, elastin, and proteoglycans, which are necessary to provide tensile strength, elasticity, and hydration to skin respectively. 11 Also, they cause laxity and fragility of skin with reduced collagen syntheses and enzymatic degradation. 12 The degree of skin photoaging could be classified by Fitzpatrick skin types I to IV according to its severity from few wrinkles to deep wrinkles. We should also mention the vascular pattern changes as telangiectasia. 13

Skin Photoaging Histology

The chronology of histological change in skin aging indicates that events such as epidermal atrophy and reduced collagen amount and fibroblasts of dermis along with the epidermal atrophy, mainly with regards to the spinosum layer of epidermis according to prolonged cell cycles are happened. 14 The number of melanocytes and Langerhans cells decreases per decade after the age of thirty. 15 Subsequently, the amount of collagen and elastic fibers and also fibroblasts decreases in chronologically aged skin compared to younger skin. 16 , 17 In postmenopausal subjects, collagen synthesis is reduced by 30%. 18 However, the heterogeneity and thickness alteration of epidermis in photoaging are reported. 19 An increase in melanocytes and different keratinocytes and the regulation of the expression of free radicals are other consequences of photoaging histology. 20 It can be generally stated that changes in the aged skin occur in the dermis and between the epidermis and the dermis. It leads to the accumulation of glycosaminoglycans and proteoglycans in the area. However, it may be due to the accumulation of metalloproteinases in hypertrophic fibroblasts and it is in contrast to the photoaged skin in which the number of inflammatory cells such as eosinophils, mast cells, and other mononuclear cells increases. 21 , 22 Wrinkle formation may cause a reduction in collagen fibers. Mostly prominent histological feature of skin photoaging is the accumulation of elastic amorphous fibers and also thicked fibers in dermis named Solar Elastosis. 23

Several biological pathways and risk factors related to skin aging are determined as; telomerase shortening, 24 , 25 Matrix metalloproteinases (MMPs), signal transduction, 26 , 27 oxidative stress, 28 , 29 vascular alterations, 30 cytokines alterations 31 and UV radiation. 32

Skin Aging Treatments

Skin aging is affected by various factors including genetics, environmental exposure (UV, xenobiotics and mechanical stress), hormonal changes, and metabolic processes (production of reactive chemicals such as reactive oxygen species, sugars, and aldehydes). All factors work together to transform the skin, its function and appearance. However, solar UV is undoubtedly a major factor responsible for skin aging. Skin aging may cause psychological side effects, leading patients to seek a suitable solution. 33 Public desire to look good and young is inevitable and more than 8 million cosmetic treatments were performed in the United States in 2017. 34 The treatment of photoaged skin may be classified into two categories: one is the removal of pigmentation, erythema, irregular vessels, and sebaceous changes and the other one is the improvement of skin senescence. 35 The process of skin rejuvenation has been associated with aggressive elements such as peeling in the past, but in recent years the demand for non-invasive treatment of skin rejuvenation has increased dramatically. Public demand for faster healing treatments with better natural state maintenance has increased, leading to a shift in skin rejuvenation techniques at public requests.

Laser Features and Skin Aging Treatment

One of the techniques for rejuvenating the skin is to use lasers and other light beams. Lasers have been used for skin rejuvenation since 1980. 36 Different wavelengths of lasers have been used to treat skin aging (see Table 1 ). The use of high-power lasers and skin peeling by heat generation is one of the methods for skin rejuvenation. Since this process is accompanied with side effect; the adjacent damaged tissues recover with the same mechanism of wound healing, but recently the use of low-power lasers has become commercial. Different types of lasers for skin rejuvenation are ablative lasers, non-ablative lasers, and fractional lasers ( Figure 1 ).

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Different Types of Lasers Involved in Skin Rejuvenation.

Ablative Lasers

These kinds of lasers have been used to treat scars, pigmentations, and rhytides by removing the epidermis and heating dermis ( Table 2 ). Ablative lasers are generally used for skin resurfacing and rejuvenation. 39 Ablative lasers evaporate tissue and hence are more aggressive, in contrast with the mild non-ablative lasers that leave the skin intact. However, ablative lasers reduce time of treatment and cause a more difficult recovery process, they stay the lasers that create the most dramatic impressive. For severe facial wrinkles, pigmentation, and skin challenges, ablative lasers are often the preferred treatment. 33 Non-ablative lasers penetrate into the dermis and heat the dermis without heating epidermis. These types of lasers denature dermis proteins such as collagen, and stimulate collagen synthesis and finally tighten the skin bed ( Figure 2 ). 39 The most common ablative lasers used for skin rejuvenation are CO 2 , erbium-doped yttrium aluminium garnet (Er:YAG), and erbium doped yttrium scandium gallium garnet.

Abbreviations: Er:YAG, erbium: yttrium aluminum-garnet; Er:DYSGG, erbium-doped yttrium scandium gallium garnet; PPTP, pulsed potassium titanyl phosphate; Nd:YAG, neodymium-doped yttrium aluminum garnet; PBM, photobiomodulation; ILP, intense pulsed light; PDL, pulsed dye laser.

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Penetration of Different Lasers Into the Skin for Rejuvenation (A) Ablative; (B) Non-ablative; (C) Fractional lasers. 39

Non-ablative Lasers

Non-ablative laser resurfacing demonstrates one of the main developments in procedural dermatology over the past decade and has become the treatment of selection for a broad range of aesthetic indications. However, safety concerns related to their use in darker skin types have remained. 40 These lasers are less destructive than ablative lasers and stiffen the skin by stimulating collagen production in the dermis; the epidermis is protected through skin cooling. This type of laser is less aggressive than the optical laser and due to the stimulation of collagen in the dermis, it makes the skin firm ( Table 2 ). The epidermis remains cool when using this laser because the waves penetrate the dermis layer. The heat generated in the dermis coagulates the collagen and then begins the wound healing process. As a result, new collagen synthesis is performed on the substrate of the skin and extracellular matrix. 41 The side effects of these lasers, such as scars and infections, have decreased 42 ; however, the efficiency of non-ablative lasers is less than ablative ones and they have been used for patients with moderate photoaging. 43

Fractional Lasers

Fractional lasers including non-ablative and ablative fractional lasers generally provide columns at the depth of 1 and 2.5 mm into the skin, respectively. 34 Non-ablative lasers influence dermis and leave epidermis with no effects ( Table 2 ). A comparison between the action of fractional ablative lasers and that of non-ablative ones on facial skin revealed clinical improvements in both techniques; however, collagen and elastin formation and edema in skin treated by the ablative fractional erbium laser were more than non-ablative one. 44

Lasers resurfacing of skin as peeling could remove fine wrinkles of skin although, however potentially have the advantages to treat deep wrinkles by collagen making stimulation. 45 Skin healing in deep peeling and laser resurfacing is known as like wound healing mechanism and depends on the depth of the lesion. 46

PhotobiomodulationLasers

PBMs lasers mainly utilize red and near-red light spectra to activate biological processes used in a wide range of medical applications ( Table 2 ). Low-power sources as LEDs, broadband lights and lasers are the sources of the photochemical and photophysical phenomenon without thermal reactions. 47 Photon energy is converted to stimulate biological reactions as collagen synthesis. 48 Near-infrared irradiation assists fibroblasts in making collagen to increase the consistency of skin. 49 The PBM technique without thermal reactions has been able to dramatically increase patients’ satisfaction with skin rejuvenation. 50

Laser Effects on Tissues

Laser–skin interaction can be categorized as: photochemical, photothermal and photoplasmal pheromones. Photochemical reactions happen when the energy of photons made by the laser in the molecules of the cells causes chemical reactions in the molecules without changes in temperature. 53 In photothermal reaction, photon energy absorbed in a cell and converted to heat causes an increase in the temperature of the cell, associated with denaturation and necrosis. 53 Photoplasmal reaction occurs when irradiance energy is high enough (10 8 or 10 9 w/cm) to form plasma accompanied by high electric fields, dielectric reactions, shock waves, and tissue rupture. 54

Photothermolysis

This is a technique that targets tissue in a specific area without damaging other neighbor tissues. Different chromophores such as oxyhemoglobin, melanin, water, tattoos absorb different wavelengths. 34 Longer wavelengths can penetrate in deeper parts of skin. Chromophores absorb photon energy to heat and destroy targets; however, surrounding tissues need to be cool (Thermal relaxation time). For example, the target tissue cooling time is 3 to 10 ms for the epidermis and 1 µs for melanosomes. 39 Thus the properties of radiation and relaxation time between the periods of radiation are important for skin rejuvenation. 55

Molecular Aspects of Laser Effects in Cell Biology

Several factors are proposed to illustrate the molecular basis for skin aging, including the theory of cellular senescence, decrease in cellular DNA repair capacity and loss of telomeres, point mutations of extranuclear mitochondrial DNA, oxidative stress, increased frequency of chromosomal abnormalities, single-gene mutations, reduced sugar, chronic inflammation, and so on. 56 Some scientists have argued that most influences are caused by extrinsic factors and that only 3% of aging factors have an intrinsic background. 57 Researches have demonstrated that low-power laser therapy can deliver lower energy to the tissues. 58 The energy of low-power laser therapy could be absorbed by mitochondria and cytochrome C. 59 The energy of the red-NIR (Near-infrared) laser could primarily be absorbed by mammalian cells cytochrome C oxidase. 60 Excited electrons in cytochrome C oxidase lead to more electron transfer and subsequently more ATP production. 61 Investigations have revealed that NO can inhibit cytochrome C oxidase activation; 62 on the other hand, low-power lasers can inhibit NO activity, resulting in more oxidative activities of the cells. 62 , 63 More activation of the cells causes more production of ROS. 62 , 64 It is considered that ROS displays a necessary role in dermal extracellular matrix alterations of both intrinsic aging and photoaging. ROS can be made from various sources including the mitochondrial electron transport chain, peroxisomal and endoplasmic reticulum localized proteins, the Fenton reaction, and such enzymes as cyclooxygenases, lipoxygenases, xanthine oxidases, and nicotinamide-adenine dinucleotide phosphate oxidases. Low-power lasers are useful for the treatment of skin disorders like wrinkles, scars, and burns because low-power lasers could positively affect cell proliferation and remodeling, DNA repairing, ion channels, and membrane potentials. 65 - 67 Low-power lasers could change the expression of different genes as the Er:YAGlaser upregulates the expression of IL1B, IL8, keratin16, MMP3, and MMP1. 68 In this regard collagen synthesis increases. Picosecond infrared laser application leads to a reduction in neighbor’s tissue damage, a decrease in beta-catenin and TGF b signaling, and more cell viability to accelerate the wound healing process. 69 Ablative CO2 resurfacing skin revealed the upregulation of different MMPs. 70 In a large-scale study of skin aging and skin rejuvenation proteins, proteomics is efficient. Proteomics has less technical limitations on protein identification and a large number of proteins could be identified by this technique. 71 Proteomic analysis of foot skin compared to breast skin demonstrated the presence of 50 ECM common proteins in both skins, but there was a difference between the expressions of tenascin-x in breast skin and serum amyloid p component in foot skin. 72 By examining the proteomic profile of elderly epidermis, it was found that interferon-stimulating polypeptides expression increased, causing the stimulation of phosphatidylinositol 3-kinase and manganese superoxide dismutase. 73 The skin irritation proteomics approach demonstrated the upregulation of HSP27 and suggested it as the skin irritation marker. 74 Laser skin proteomics evaluation suggested a balance between skin cancer and laser irradiation. 75 Aging leads to a reduction in skin collagen and elastic fibers with MMPs upregulation; however, UV causes skin aging effectively. 76 , 77 A study on mouse skin exposure to the Er:YAG laser revealed skin water epidermal loss and the upregulation of p21 & p53 to repair DNA and skin survival. 78 Low-power laser therapy could downregulate the expression of cytokeratin and antigens related to proliferation. 79 , 80 Proteomics assay revealed the downregulation of Rho GDI 1 expression following by low-power laser therapy and the adjustment of Rho protein activities could disrupt actin cytoskeleton and kill keratinocytes following by new keratinocytes migration to replace the old ones. 81 Laser therapy could reduce HSP26 protein and cause surface cell death of skin after 24 hours of treatment. 81 In one study, low-level Er:YAG laser irradiation to gingival fibroblast cells caused galectin 7 wound healing protein upregulation and suggested reduced cell proliferation after laser therapy in gingival fibroblast cells. 82 Lee et al reported the long-pulsed 1064-nm neodymium-doped (Nd): YAG laser treatment of mouse skin. The results of their study indicated an increase in collagen and TGF-B and decreased expression of MMPs. 83 Findings from a study by De Filippis et al revealed an interaction between keratinocytes and fibroblast and overexpression of filaggrin, aquaporin, TGase, HSP70 with a reduction in MMP-1 and an increase in elastin and procollagen type1 with the use of the 1064 nm Nd:YAG non-ablative laser. 84 It can be generally assumed that non-invasive lasers are effective in enhancing the activity of fibroblasts and keratinocytes with the synthesis of collagen, elastin, and decreased expression of some metalloproteinases.

As the assessment of skin rejuvenation and laser therapy demonstrated, many proteins related to collagen synthesis, fibroblasts and keratinocytes proliferation, and apoptosis activities were introduced. However, more investigations into the proteomic and genomic analysis are required to interpret laser effects on the molecular biology of skin rejuvenation. It is recommended to provide a comprehensive genetic and protein map which will be suitable to find out different biological pathways of laser traded skins to improve better ways to rejuvenate aged skin because many proteins and genes are still unknown. On the other hand, the improvement of lasers for the treatment of different skins and sooner cooling of skin layers is suggested. The wide range of biological events which are accompanied by laser application in skin rejuvenation implies that more safety points should be considered in the therapeutic guidelines.

Ethical Considerations

Not applicable.

Conflict of Interests

The authors declare no conflict of interest.

Acknowledgment

Shahid Beheshti University of Medical Sciences supports this research.

Please cite this article as follows : Heidari Beigvand H, Razzaghi M, Rostami-Nejad M, Rezaei-Tavirani M, Safari S, Rezaei-Tavirani M. Assessment of laser effects on skin rejuvenation. J Lasers Med Sci . 2020;11(2):212-219. doi:10.34172/jlms.2020.35.

Assessment of Laser Effects on Skin Rejuvenation

Affiliations.

  • 1 Firoozabadi Hospital, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran.
  • 2 Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • 3 Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • 4 Proteomics Research Center, Department of Emergency Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • 5 Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • PMID: 32273965
  • PMCID: PMC7118506
  • DOI: 10.34172/jlms.2020.35

Laser skin resurfacing has changed the approach of facial skin rejuvenation over the past decade. This article evaluates the laser effects on skin rejuvenation by the assessment of laser characteristics and histological and molecular changes, accompanied by the expression of proteins during and after laser-assisted rejuvenation of skin. It is important to note that different layers of skin with different cells are normally exposed to the sun's UV radiation which is the most likely factor in aging and damaging healthy skin. To identify the expression of proteins, using validated databases and reviewing existing data could reveal altered proteins which could be analyzed and mapped to investigate their expression and their different effects on cell biological responses. In this regard, proteomics data can be used for better investigation of the changes in the proteomic profile of the treated skin. Different assessments have revealed the survival and activation of fibroblasts and new keratinocytes with an increase of collagen and elastin fibers in the dermis and the reduction of matrix metalloproteinases (MMPs) and heat shock proteins (HSPs) as a result of different low-power laser therapies of skin. There are a wide range of biological effects associated with laser application in skin rejuvenation; therefore, more safety considerations should be regarded in the application of lasers in skin rejuvenation.

Keywords: Laser; Laser therapy; Rejuvenation; Scars; Skin aging.

Copyright © 2020 J Lasers Med Sci.

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Hossein Safari

hossein nejad safari

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Clustered redundant keypoint elimination method for image mosaicing using a new Gaussian-weighted blending algorithm

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  • Published: 19 July 2021
  • Volume 38 , pages 1991–2007, ( 2022 )

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hossein nejad safari

  • Zahra Hossein-Nejad 1 &
  • Mehdi Nasri   ORCID: orcid.org/0000-0002-9254-3584 2  

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In this paper, a new method for image mosaicing (image stitching) is introduced based on Scale Invariant Feature transform (SIFT). One of the main drawbacks of SIFT is the redundancy of the extracted keypoints, which leads to lower image mosaicing quality. Recently, a new method called Redundant Keypoint Elimination (RKEM) was presented to remove these redundant features, and enhance image registration performance. Despite the applicability of RKEM, its threshold value is considered the same in all parts of the image. This characteristic leads to inappropriate removal of keypoints due to the fact that distribution of keypoints in the high-detailed region is denser than the low-detailed ones. This paper proposes a new method to improve RKEM called Clustered RKEM (CRKEM) which is based on keypoints distribution. Moreover, in this paper a new blending algorithm is proposed based on a Gaussian-weighted function. In the proposed blending method, the Gaussian function is proposed based on the mean and variance of the pixels in the overlapped region of images to be mosiaced. In comparison with the classical methods, the experimental results confirm the superiority of the proposed method in image mosaicing as well as to image registration and matching.

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Kekec, T., Yildirim, A., Unel, M.: A new approach to real-time mosaicing of aerial images. Robot. Auton. Syst. 62 , 1755–1767 (2014)

Article   Google Scholar  

Vaghela D, Naina P. A review of image mosaicing techniques. arXiv preprint arXiv:1405.2539 , (2014)

Sharma, S.K., Jain, K.: Image stitching using AKAZE features. J. Indian Soc. Remote Sens. 48 , 1389–1401 (2020)

Wang, Z., Yang, Z.: Review on image-stitching techniques. Multimed. Syst. 26 , 1–18 (2020)

Saha, M., Chakraborty, M., Biswas, T.: An improved approach for document image mosaicing. Int. J. 6 , 51–55 (2016)

Google Scholar  

Kaur, J.: A robust technique for image mosaicing using modified. Indian J. Sci. Technol. (2016). https://doi.org/10.17485/ijst/2016/v9i47/101722

Jinwei, C., Bin, G., Gangxiang, G.: Image registration and mosaicking based on the criterion of four collinear points. DEStech Trans. Eng. Technol. Res. (2016). https://doi.org/10.12783/dtetr/ICMITE20162016/4576

Yan, W., Yue, G., Fang, Y., Chen, H., Tang, C., Jiang, G.: Perceptual objective quality assessment of stereoscopic stitched images. Signal Process. 172 , 107541 (2020)

Zhang, Y., Lai, Y.-K., Zhang, F.-L.: Stereoscopic image stitching with rectangular boundaries. Vis. Comput. 35 , 823–835 (2019)

Irani, M., Hsu, S., Anandan, P.: Video compression using mosaic representations. Signal Process.: Image Commun. 7 , 529–552 (1995)

Hu R, Shi R, Shen I.-F, Chen W. Video stabilization using scale-invariant features. In Information Visualization, 2007. IV'07. 11th International Conference, (2007), pp. 871–877

Okade, M., Biswas, P.K.: Improving video stabilization using multi-resolution MSER features. IETE J. Res. 60 , 373–380 (2014)

Niu, C., Zhong, F., Xu, S., Yang, C., Qin, X.: Cylindrical panoramic mosaicing from a pipeline video through MRF based optimization. Vis. Comput. 29 , 253–263 (2013)

Choi Y.-H, Seong Y. K, Choi T.-S. Image mosaicing with automatic scene segmentation for video indexing. In: Consumer Electronics, 2002. ICCE. 2002 Digest of Technical Papers. International Conference on, (2002), pp. 74-75

Szeliski R, Shum H-Y. Creating full view panoramic image mosaics and environment maps. In: Proceedings of the 24th annual conference on Computer graphics and interactive techniques, pp. 251–258. (1997)

Zhang, T., Zhao, R., Chen, Z.: Application of migration image registration algorithm based on improved SURF in remote sensing image mosaic. IEEE Access 8 , 163637–163645 (2020)

Gracias N, Costeira J. P, Victor J. Linear global mosaics for underwater surveying. In 5th IFAC Symposium on Intelligent Autonomous Vehicles, pp. 78–83. (2004)

Zhang X, Zhu X. An accurate and efficient image registration algorithm in the aerial infrared images. In: Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), p. 113730W. (2020)

Deshmukh P, Paikrao P. A review of various image mosaicing techniques. In: 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), pp. 1–4. (2019)

Szeliski, R.: Image alignment and stitching: a tutorial. Found. Trends® Comput. Gr. Vis. 2 , 1–104 (2006)

MathSciNet   MATH   Google Scholar  

Shum, H.-Y., Szeliski, R.: Construction of panoramic image mosaics with global and local alignment. In: Benosman, R., Kang, S.B. (eds.) Panoramic Vision, pp. 227–268. Springer, New York (2001)

Chapter   Google Scholar  

Wei, L., Zhong, Z., Lang, C., Yi, Z.: A survey on image and video stitching. Virtual Real. Intell. Hardw. 1 , 55–83 (2019)

Jain, P.M., Shandliya, V.K.: A review paper on various approaches for image mosaicing. Int. J. Comput. Eng. Res. 3 , 106–109 (2013)

Monali R, Moonka S, Priya A, Tripathy S. S. Effects of noise and relative overlap on image mosaicing using SURF features. In: Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE International Conference on, pp. 773–777. (2016)

Adel E, Elmogy M, Elbakry H. Image stitching based on feature extraction techniques: a survey. In: International Journal of Computer Applications (0975–8887) Volume , pp. 1–8, (2014)

Krishnakumar, K., Gandhi, S.I.: Video stitching based on multi-view spatiotemporal feature points and grid-based matching. Vis. Comput. 36 , 1837–1846 (2020)

Pandey, A., Pati, U.C.: Panorama generation using feature-based mosaicing and modified graph-cut blending. In: Pant, M., Ray, K., Sharma, T.K., Rawat, S., Bandyopadhyay, A. (eds.) Soft Computing: Theories and Applications. Springer, Singapore (2018)

Mistry, S., Patel, A.: Image stitching using Harris feature detection. Int. Res. J. Eng. Technol. (IRJET) 3 , 2220–2226 (2016)

Bheda, D., Joshi, M., Agrawal, V.: A study on features extraction techniques for image mosaicing. Int. J. Innov. Res. Comput. Commun. Eng. 2 , 3432–3437 (2014)

Khan, H.A., Haider, M.A., Ansari, H.A., Ishaq, H., Kiyani, A., Sohail, K., et al.: Automated feature detection in dental periapical radiographs by using deep learning. Oral Surg., Oral Med., Oral Pathol. Oral Radiol. 131 , 711–720 (2020)

Bhowmik A, Gumhold S, Rother C, Brachmann E. Reinforced feature points: optimizing feature detection and description for a high-level task. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4948–4957. (2020)

Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60 , 91–110 (2004)

Derpanis K. G. The harris corner detector," York University , vol. 2, (2004)

Yang, A., Yang, X., Wu, W., Liu, H., Zhuansun, Y.: Research on feature extraction of tumor image based on convolutional neural network. IEEE Access 7 , 24204–24213 (2019)

Kamboj, A., Rani, R., Nigam, A.: A comprehensive survey and deep learning-based approach for human recognition using ear biometric. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02119-0

Scheidegger, F., Istrate, R., Mariani, G., Benini, L., Bekas, C., Malossi, C.: Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy. Vis. Comput. 37 , 1–18 (2020)

Hemanth, D.J., Estrela, V.V.: Deep Learning for Image Processing Applications. IOS Press, Amsterdam (2017)

Jiao, L., Zhao, J.: A survey on the new generation of deep learning in image processing. IEEE Access 7 , 172231–172263 (2019)

Joshi, K., Patel, M.I.: Recent advances in local feature detector and descriptor: a literature survey. Int. J. Multimed. Inf. Retr. 9 , 1–17 (2020)

Ghosh, D., Kaabouch, N.: A survey on image mosaicing techniques. J. Vis. Commun. Image Represent. 34 , 1–11 (2016)

Prathap K. S. V, Jilani S, Reddy P. R A critical review on image mosaicing. In: Computer Communication and Informatics (ICCCI), 2016 International Conference on, pp. 1–8 (2016)

Bhosle, U., Chaudhuri, S., Roy, S.D.: A fast method for image mosaicing using geometric hashing. IETE J. Res. 48 , 317–324 (2002)

Vishwakarma, A., Bhuy, M.: Image mosaicking using improved auto-sorting algorithm and local difference-based harris features. Multimed. Tools Appl. 79 , 1–18 (2020)

Zagrouba, E., Barhoumi, W., Amri, S.: An efficient image-mosaicing method based on multifeature matching. Mach. Vis. Appl. 20 , 139–162 (2009)

Kang P, Ma H. An automatic airborne image mosaicing method based on the SIFT feature matching. In: Multimedia Technology (ICMT), 2011 International Conference on, pp. 155–159. (2011)

Murali, Y., Madanapalle, M.: Image mosaic using speeded up robust feature detection. Image 1 , 40–45 (2012)

Prathap, K.S.V., Jilani, S., Reddy, P.R.: A real-time image mosaicing using scale invariant feature transform. Indian J. Sci. Technol. 9 , 1–6 (2016)

Hossein-nejad Z, Nasri M. Image registration based on SIFT features and adaptive RANSAC transform. In: Communication and Signal Processing (ICCSP), 2016 International Conference on. pp. 1087–1091. (2016)

Hossein-Nejad, Z., Nasri, M.: An adaptive image registration method based on SIFT features and RANSAC transform. Comput. Electr. Eng. 62 , 524–537 (2017)

Hossein-Nejad, Z., Agahi, H., Mahmoodzadeh, A.: Detailed review of the scale invariant feature transform (sift) algorithm; concepts, indices and applications. J. Mach. Vis. Image Process. 7 , 165–190 (2020)

Laraqui, A., Baataoui, A., Saaidi, A., Jarrar, A., Masrar, M., Satori, K.: Image mosaicing using voronoi diagram. Multimed. Tools Appl. 76 , 8803–8829 (2017)

Laraqui, A., Saaidi, A., Satori, K.: MSIP: multi-scale image pre-processing method applied in image mosaic. Multimed. Tools Appl. 77 , 7517–7537 (2018)

Ke Y, Sukthankar R. PCA-SIFT: A more distinctive representation for local image descriptors. In: Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, pp. II-506-II-513 Vol. 2. (2004)

Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) Computer Vision–ECCV 2006, pp. 404–417. Springer, Berlin (2006)

Cheung W, Hamarneh G. N-sift: N-dimensional scale invariant feature transform for matching medical images. In: Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on, pp. 720–723. (2007)

Yi, Z., Zhiguo, C., Yang, X.: Multi-spectral remote image registration based on SIFT. Electron. Lett. 44 , 107–108 (2008)

Lingua, A., Marenchino, D., Nex, F.: Performance analysis of the SIFT operator for automatic feature extraction and matching in photogrammetric applications. Sensors 9 , 3745–3766 (2009)

Morel, J.-M., Yu, G.: ASIFT: A new framework for fully affine invariant image comparison. SIAM J. Imag. Sci. 2 , 438–469 (2009)

Article   MathSciNet   MATH   Google Scholar  

Tamimi, H., Andreasson, H., Treptow, A., Duckett, T., Zell, A.: Localization of mobile robots with omnidirectional vision using particle filter and iterative sift. Robot. Auton. Syst. 54 , 758–765 (2006)

Sedaghat, A., Mokhtarzade, M., Ebadi, H.: Uniform robust scale-invariant feature matching for optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 49 , 4516–4527 (2011)

Hossein-Nejad, Z., Nasri, M.: RKEM: redundant keypoint elimination method in image registration. IET Image Proc. 11 , 273–284 (2017)

Hossein-Nejad, Z., Nasri, M.: A-RANSAC: adaptive random sample consensus method in multimodal retinal image registration. Biomed. Signal Process. Control 45 , 325–338 (2018)

Hossein-Nejad Z, Nasri M. Retinal image registration based on auto-adaptive SIFT and redundant keypoint elimination method. In: 2019 27th Iranian Conference on Electrical Engineering (ICEE), pp. 1294–1297 (2019)

Liu, Y., Yu, D., Chen, X., Li, Z., Fan, J.: TOP-SIFT: the selected SIFT descriptor based on dictionary learning. Vis. Comput. 35 , 667–677 (2019)

Hossein-Nejad, Z., Nasri, M.: Copy-move image forgery detection using redundant keypoint elimination method. In: Ramakrishnan, S. (ed.) Cryptographic and Information Security Approaches for Images and Videos, pp. 773–797. CRC Press, Boca Raton (2019)

Yonghong, J.: Fusion of landsat TM and SAR image based on principal component analysis. Remote Sens. Technol. Appl. 13 , 46–49 (1998)

Tian F, Shi P. Image mosaic using orb descriptor and improved blending algorithm. In: Image and Signal Processing (CISP), 2014 7th International Congress on, pp. 693–698 (2014)

Chipman L. J, Orr T. M, Graham L. N. Wavelets and image fusion. In: Image Processing, 1995. Proceedings., International Conference on, pp. 248-251 (1995)

Li, H., Manjunath, B., Mitra, S.K.: Multisensor image fusion using the wavelet transform. Gr Models Image Process. 57 , 235–245 (1995)

Burt, P.J., Adelson, E.H.: A multiresolution spline with application to image mosaics. ACM Trans. Gr. (TOG) 2 , 217–236 (1983)

Li A, Zhou S, Wang R. An improved method for eliminating ghosting in image stitching. In: 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp. 415–418 (2017)

Zhang, Q., Wang, Y., Wang, L.: Registration of images with affine geometric distortion based on maximally stable extremal regions and phase congruency. Image Vis. Comput. 36 , 23–39 (2015)

Hossein-Nejad, Z., Agahi, H., Mahmoodzadeh, A.: Image matching based on the adaptive redundant keypoint elimination method in the SIFT algorithm. Pattern Anal. Appl. 24 , 669–683 (2020)

Hong J, Lin W, Zhang H, Li L. Image mosaic based on surf feature matching. In: 2009 First International Conference on Information Science and Engineering, pp. 1287–1290 (2009)

Zhen Y, Sun Z, Li J, Peng Y. An airborne remote sensing image mosaic algorithm based on feature points. In: 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), pp. 202–205 (2016)

Zhang, W., Li, X., Yu, J., Kumar, M., Mao, Y.: Remote sensing image mosaic technology based on SURF algorithm in agriculture. J. Image Video Proc. 2018 (85), 2018 (2018)

Ai, Y., Kan, J.: Image mosaicing based on improved optimal seam-cutting (January 2020). IEEE Access 8 , 181526–181533 (2020)

Ma, W., Wen, Z., Wu, Y., Jiao, L., Gong, M., Zheng, Y., et al.: Remote sensing image registration with modified SIFT and enhanced feature matching. IEEE Geosci. Remote Sens. Lett. 14 , 3–7 (2017)

Tang, H., Pan, A., Yang, Y., Yang, K., Luo, Y., Zhang, S., et al.: Retinal image registration based on robust non-rigid point matching method. J. Med. Imag. Health Inform. 8 , 240–249 (2018)

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13 , 600–612 (2004)

Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20 , 2378–2386 (2011)

Zhang, L., Shen, Y., Li, H.: VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Trans. Image Process. 23 , 4270–4281 (2014)

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Hossein-Nejad, Z., Nasri, M. Clustered redundant keypoint elimination method for image mosaicing using a new Gaussian-weighted blending algorithm. Vis Comput 38 , 1991–2007 (2022). https://doi.org/10.1007/s00371-021-02261-9

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Mehdi Hossein-Nejad MBA, PhD

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Dr. Mehdi Hossein-Nejad received his PhD in Business Administration from Western University’s Ivey Business School. He also holds an MBA and a bachelor’s degree in Electrical Engineering. His research interests include topics in competitive dynamics, organizational attention and behavioural strategy. He is particularly interested in examining how individual level factors (e.g. managerial cognition) influence competition and strategy. He has presented his research at international conferences.

Dr. Hossein-Nejad loves to teach and enjoys his time in the classroom. He uses a discussion-oriented and highly interactive teaching style and prefers the case-based teaching approach. He encourages his students to be active participants in class discussions and to develop creative solutions for business problems. He has written a case on the smartphone industry (available through Ivey Publishing) and is interested in writing more cases that cover important issues in strategic management and international business/management.

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    Zahra Hossein-Nejad was born in Shiraz, Iran on January 6, 1991. She received the B.Sc. in Electrical Engineering from Islamic Azad University, Jahrom branch, Jahrom, Iran, in 2013 and the M.Sc. in Electrical Engineering from Islamic Azad University, Sirjan branch, Sirjan, Iran, in 2016. Her research interests include image processing and ...

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    RKEM-SIFT algorithm was introduced by Hossein-Nejad et al. in 2017 . This algorithm is the improved form of SIFT algorithm in the feature extraction step. In this method, the features distance identified in the SIFT algorithm are calculated and proposed as the redundancy index criterion according to the distance between features. Then the ...

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    Business and Information Technology Building - Room 4037 North Oshawa 2000 Simcoe Street North Oshawa, ON L1G 0C5. 905.721.8668 ext. 5375. [email protected].

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    Simulation results confirm the superiority of the proposed method based on some evaluation criteria such as precision, correct detection ratio and false alarm rate. In this paper, a new approach is proposed for object recognition in remote-sensing images. In the proposed approach, the matching process between the object in the template and test images is done based on Scale Invariant Feature ...

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    DOI: 10.1016/j.compeleceng.2016.11.034 Corpus ID: 21249473; An adaptive image registration method based on SIFT features and RANSAC transform @article{HosseinNejad2017AnAI, title={An adaptive image registration method based on SIFT features and RANSAC transform}, author={Zahra Hossein-Nejad and Mehdi Nasri}, journal={Comput.

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    454000. Dialing code (s) +7 35153. OKTMO ID. 75715000001. Website. karabash-go.ru. Karabash (Russian: Карабаш) is a town in Chelyabinsk Oblast, Russia, located 90 kilometers (56 mi) northwest of Chelyabinsk. Population: 13,152 (2010 Census); [1] 15,942 (2002 Census); [5] 17,006 (1989 Soviet census).

  22. Image matching based on the adaptive redundant keypoint elimination

    An adaptive RKEM is presented which considers type of the images and distortion thereof, while adjusting the threshold value, which can improve the efficiency of the RKEM-SIFT in eliminating redundant keypoints. Scale invariant feature transform (SIFT) is one of the most effective techniques in image matching applications. However, it has a main drawback: existing numerous redundant keypoints ...