Computational techniques in biomedical image analysis: overview

Koundal, Deepika; Kadyan, Virender; Dutta, Parul; Anand, Vatsala; Aggarwal, Shankar; Gupta, Sharut

doi:10.1016/b978-0-12-820024-7.00001-3

Cited by 8 publications

(5 citation statements)

References 59 publications

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“…In impercibility steganography systems should have high embedded capacity and capability to stand against the various attacks. The authors in [13,14] provided medical image analysis for image steganography. The preprocessing of the images can also be obtained by using adaptive thresholding [15].…”

Section: Literature Surveymentioning

confidence: 99%

A Transfer learning model for high secure image steganography using ResNet-50

Gujjula¹,

Kellampalli²

2023

IJSMIEN

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Cryptography is commonly employed in data security, but one of the main disadvantages of the scheme would be that it attracts attention to just the perpetrator, which results throughout the exposure of the data. In order to solve these issues, digital image steganography is among the several techniques used to provide protection during data transmission. Capacity embedding and distortion are indeed the two key factors that play a key role when hiding a message in image. Therefore, this paper explores strategies consisting of two steps leading to the creation of that very stable framework with a large embedding ability. The first step determines an arbitrary image by morphing through hybrid discrete wavelet transform (HDWT) in order to produce a cover image that relies on the characteristics of both the chosen image. That second step integrates a text message of adequate duration using the transfer learning based ResNet50 architecture, integrating multi-keys and compound operators into the algorithm to provide robust protection. Simulation results shows that the proposed method resulted in superior PSNR and SSIM performance as compared to state of art approaches.Finally, the robustness and imperceptibility of the proposed method is improved, which reduced the attacks on the system.

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Section: Literature Surveymentioning

confidence: 99%

A Transfer learning model for high secure image steganography using ResNet-50

Gujjula¹,

Kellampalli²

2023

IJSMIEN

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“…Several denoising techniques occur in image processing. The denoising technique in images relies on the noise present type in digital images [19]. Numerous studies have been performed in the domain of digital image denoising using different methods and techniques.…”

Section: Related Workmentioning

confidence: 99%

“…In general terms, these techniques aim to remove noise to keep all fine details in the digital image while preserving the essential signal features as much as possible [19,20]. In this paper, the works will be reviewed according to the following categories: wavelet domain techniques, spatial frequency domain, and wavelet domain and filtering denoising techniques.…”

Section: Figure 1 Structural Description Of Different Kinds Of Digital Image Denoising Techniquesmentioning

confidence: 99%

“…Spatial filters that are categorized into non-linear and linear filters are a standard approach for removing noise from degraded image data. These filter types allow low-pass filtering on unit pixel sets, assuming that noise occupies the higher-frequency spectrum region [19,20]. In general, spatial filters eliminate noise to a fair degree, but at the cost of blurring images, they make the edges of the images opaque in turn.…”

Section: Spatial Domain Filteringmentioning

confidence: 99%

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Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Ismael¹,

Baykara²

2021

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Recently, with the explosion in the number of digital images captured every day in all life aspects, there is a growing demand for more detailed and visually attractive images. However, the images taken by current sensors are inevitably degraded by noise in various fields, such as medical, astrophysics, weather forecasting, etc., which contributes to impaired visual image quality. Therefore, work is needed to reduce noise by preserving the textural, information, and structural features of the image. So far, there are different techniques for reducing noise that various researchers have done. Each technique has its advantages and disadvantages. In this paper, a review of some significant work in the field of image denoising based on that the denoising methods is presented. These methods can be classified as wavelet domain, spatial domain, or both methods can combine to obtain the advantage them. After a brief discussion, the classification of image denoising techniques is explained. A comparative analysis of various image denoising methods is also performed to help researchers in the image denoising area. Besides, standard measurement parameters have been used to compute the results and to evaluate the performance of the used denoising techniques. This review paper aims to provide functional knowledge of image denoising methods in a nutshell for applications using images to provide ease for selecting the ideal strategy according to the necessity.

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“…Computational image analysis is now increasingly used in biomedical research, offering a precise quantitative approach in describing complex objects [10][11][12][13]. Fractal-based image analysis methods can be used, solely or in combination with other methods such as the gray-level co-occurrence matrix (GLCM) analysis [14,15], discrete wavelet transform (DWT) [14], and the finite-element method (FEM) [16] to point out the structural alterations, i.e., different degrees of irregularity in the structure of signals and images of human biological samples obtained from a wide range of clinical conditions, predominantly carcinoma and chronic noncommunicable diseases.…”

Section: Introductionmentioning

confidence: 99%

Structural Characterization of Toxoplasma gondii Brain Cysts in a Model of Reactivated Toxoplasmosis Using Computational Image Analysis

Bauman,

Srbljanović,

Čolović Čalovski

et al. 2024

Fractal Fract

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Toxoplasma gondii is an obligate intracellular parasite existing in three infectious life stages—tachyzoites, bradyzoites, and sporozoites. Rupture of tissue cysts and re-conversion of bradyzoites to tachyzoites leads to reactivated toxoplasmosis (RT) in an immunocompromised host. The aim of this study was to apply ImageJ software for analysis of T. gondii brain cysts obtained from a newly established in vivo model of RT. Mice chronically infected with T. gondii (BGD1 and BGD26 strains) were treated with cyclophosphamide and hydrocortisone (experimental group—EG) or left untreated as infection controls (ICs). RT in mice was confirmed by qPCR (PCR+); mice remaining chronically infected were PCR−. A total of 90 images of cysts were analyzed for fractal dimension (FD), lacunarity (L), diameter (D), circularity (C), and packing density (PD). Circularity was significantly higher in PCR+ compared to IC mice (p < 0.05 for BGD1, p < 0.001 for the BGD26 strain). A significant negative correlation between D and PD was observed only in IC for the BGD1 strain (ρ = −0.384, p = 0.048), while fractal parameters were stable. Significantly higher D, C, and PD and lower lacunarity, L, were noticed in the BGD1 compared to the more aggressive BGD26 strain. In conclusion, these results demonstrate the complexity of structural alterations of T. gondii cysts in an immunocompromised host and emphasize the application potential of ImageJ in the experimental models of toxoplasmosis.

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Computational techniques in biomedical image analysis: overview

Cited by 8 publications

References 59 publications

A Transfer learning model for high secure image steganography using ResNet-50

A Transfer learning model for high secure image steganography using ResNet-50

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Structural Characterization of Toxoplasma gondii Brain Cysts in a Model of Reactivated Toxoplasmosis Using Computational Image Analysis

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