Image Compression Using Neural Network for Biomedical Applications

Rao, G. Sasibhushana; Kumari, G. Vimala; Rao, B. Prabhakara

doi:10.1007/978-981-13-1595-4_9

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…The compression techniques use lesser bits to represent each pixel of the image, so, the compressed imageries have lesser bits per pixel (BPP). Less important pieces of information are removed during lossy compression 12–18 …”

Section: Introductionmentioning

confidence: 99%

Bilateral anisotropic Gabor wavelet transformation based deep stacked auto encoding for lossesless image compression

Kumar¹,

Sarankumar²,

vignesh³

et al. 2022

Concurrency and Computation

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A highly challenging aspect of the data compression technique is maintaining the quality of data that reconstructs in high compression rates. To overcome these limitations, a bilateral anisotropic Gabor wavelet transformation with deep stacked auto encoding (BAGWT-DSAE) technique based lossesless image compression is proposed in this article to save the storage space and processing time during transferring the images. The proposed method contains three main processes namely preprocessing, compression and decompression. Initially input aerial image and digital image are taken and these images are given bilateral filter based preprocessing for eliminates the different types of noises and also multiple artifacts. Then the preprocessed images are given to anisotropic Gabor wavelet transformation based deep stacked auto encoding to compress and decompress the wavelet transform's sensitive sub-bands effectually. In DSAE, the decoder of the auto encoder achieves a better quality decompressed image. The proposed method is implemented in MATLAB simulations run in

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Section: Introductionmentioning

confidence: 99%

Bilateral anisotropic Gabor wavelet transformation based deep stacked auto encoding for lossesless image compression

Kumar¹,

Sarankumar²,

vignesh³

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…In the latter, the sum of the incoming impulses is transmitted directly to the axons if the threshold is exceeded, essentially behaving like a linear regression model, approximating the distribution of data with a straight line. On the contrary, the use of a non-linear function allows for a better representation of the signals, without considering the fact that sometimes a linear regression is not usable [46,47].…”

mentioning

confidence: 99%

Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring

et al. 2021

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The identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle. This is due to the considerable complexity of a sound that is made up of many contributions at different frequencies. Each sound has a specific frequency spectrum, but when many sounds overlap it becomes difficult to discriminate between the different contributions. In this case, it can be extremely useful to have a tool that is capable of identifying the operating conditions of an acoustic source. In this study, measurements were made of the noise emitted by a wind turbine in the vicinity of a sensitive receptor. To identify the operating conditions of the wind turbine, average spectral levels in one-third octave bands were used. A model based on a support vector machine (SVM) was developed for the detection of the operating conditions of the wind turbine; then a model based on an artificial neural network was used to compare the performance of both models. The high precision returned by the simulation models supports the adoption of these tools as a support for the acoustic characterization of noise in environments close to wind turbines.

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Ameliorated Fick’s law algorithm based multi-threshold medical image segmentation

Hu,

Zhao,

Hussien

et al. 2024

Artif Intell Rev

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Medical image segmentation is a critical and demanding step in medical image processing, which provides a solid foundation for subsequent medical image data extraction and analysis. Multi-threshold image segmentation, one of the most commonly used and specialized image segmentation techniques, limits its application to medical images because it requires demanding computational performance and is difficult to produce satisfactory segmentation results. To overcome the above problems, an ameliorated Fick's law algorithm (MsFLA) for multi-threshold image segmentation is developed in this paper. First, an optimized sine–cosine strategy is introduced to extend the molecular diffusion process to alleviate the problem of easily falling into local optima, thus improving the convergence accuracy of the Fick's law algorithm (FLA). Secondly, the introduction of local minimal value avoidance enriches the individual molecular information and enhances the local search ability, thus improving computational accuracy. In addition, the optimal neighborhood learning strategy is added to ensure a more careful and reasonable reliance on the optimal solution, thus reducing the chance of convergence of a local solution. The efficient optimization capability of MsFLA is comprehensively validated by comparing MsFLA with the original FLA and other algorithms in 23 classical benchmark functions. Finally, MsFLA is applied to image segmentation of grayscale images of COVID-19 and brain and color images of Lung and Colon cancer histopathology by using Cross entropy to validate its segmentation capability. The experimental results show that the MsFLA obtains the best segmentation results in three medical image cases compared to other comparison algorithms, which indicates that MsFLA can effectively solve the multi-threshold medical image segmentation problem. Graphical abstract

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Image Compression Using Neural Network for Biomedical Applications

Cited by 10 publications

References 4 publications

Bilateral anisotropic Gabor wavelet transformation based deep stacked auto encoding for lossesless image compression

Bilateral anisotropic Gabor wavelet transformation based deep stacked auto encoding for lossesless image compression

Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring

Ameliorated Fick’s law algorithm based multi-threshold medical image segmentation

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