An Enhencment Medical Image Compression Algorithm Based on Neural Network

Dridi, Manel; Hajjaji, Mohamed Ali; Bouallègue, Belgacem; Mtibaa, Abdellatif

doi:10.14569/ijacsa.2016.070565

Cited by 9 publications

(10 citation statements)

References 15 publications

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“…The performance of the proposed approach was validated by comparing it with other existing approaches and the observations are tabulated. From the results, it can be observed that the proposed approach achieved a dice score of 0.71 for whole tumor (WT), 0.80 for core tumor (CT), and 0.82 for active tumor (AT) compared to other approaches and the parameters for segmented tissues and its performance anal-sis are evaluated and the obtained results show the efficacy of the proposed approach [46][47][48][49][50][51][52].…”

Section: Conclusion and Future Scopementioning

confidence: 91%

“…Considering an example of knowledge obtained during the process of recognizing humans. This knowledge can be applied to learn and recognize different humans at various age levels [46]. This process is the learning process of the machine, using external sources of additional training information from one or more related tasks considering the basic training data.…”

Section: Training Neural Network Using Transfer Learningmentioning

confidence: 99%

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Dense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation

Sille¹,

Choudhury²,

Chauhan³

et al. 2021

RIA

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Brain tumor segmentation is an essential and challenging task because of the heterogeneous nature of neoplastic tissue in spatial and imaging techniques. Manual segmentation of the tumor in MRI images is prone to error and time-consuming tasks. An efficient segmentation mechanism is vital to the accurate classification and segmentation of tumorous cells. This study presents an efficient hierarchical clustering-based dense CNN approach for accurately classifying and segmenting the brain tumor cells in MRI images. The research focuses on improving the efficiency of the segmentation algorithms by considering the qualitative measures such as the dice score coefficient using quantitative parameters such as mean square error and peak signal to noise ratio. The experimental analysis states the efficacy and prominence of the proposed technique compared to other models are tabulated within the paper.

show abstract

Section: Conclusion and Future Scopementioning

confidence: 91%

Section: Training Neural Network Using Transfer Learningmentioning

confidence: 99%

Dense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation

Sille¹,

Choudhury²,

Chauhan³

et al. 2021

RIA

View full text Add to dashboard Cite

show abstract

“…The most common neural network used for image compression is feed-forward artificial neural networks. They contain a minimum of one hidden layer, with fewer neurons than the input and output layers, depending on the compression ratio required, and such structure is known as bottleneck architecture [14]. This bottleneck architecture allows the neural network to map the original information onto a lower-dimensional manifold from which the original information can be predicted i.e.…”

Section: Methodsmentioning

confidence: 99%

Pyramid based Progressive Transmission System with Hybrid Compression Scheme for Medical Images

Ravikiran

Jayanth

2021

SN COMPUT. SCI.

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Transmission of medical images over the internet has become common in telemedicine services. Telemedicine is an enabler for rural health care services where network quality varies over time and there is a need for robustness in presence of poor network quality and efficient use of bandwidth. At the same time, the quality of medical images cannot be compromised with network constraints. In this work, a progressive transmission system with an efficient compression scheme is proposed for the effectual transmission of medical images over the internet with consideration for optimal use of network bandwidth. The image quality can be fine-tuned based on network bandwidth availability and user requirements.

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“…Entropy is a magnitude characterizing the quantity of data contained in an image. In fact, if the dispensing of grey values is very uniform, the information entropy is greater [39,40]. e entropy E(s) is defined by the following equation:…”

Section: E Entropymentioning

confidence: 99%

Crypto-Watermarking Algorithm Using Weber’s Law and AES: A View to Transfer Safe Medical Image

Ajili

Hajjaji

Mtibaa

2021

Scientific Programming

Self Cite

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We propose a novel method for medical image watermarking in the DCT domain using the AES encryption algorithm. First, we decompose the original medical image into subblocks of 8 × 8. Besides, we apply the DCT and the quantization, respectively, to each subblock. However, in the DCT domain, an adequate choice of the DCT coefficients according to the quantization table in the middle frequencies band is performed. After that, we embed the patient’s data into the corresponding medical image. The insertion step is carried out just after the quantization phase. To increase the robustness, we encrypt the watermarked medical images by using the AES algorithm based on chaotic technique. Arnold’s cat map is used to shuffle the pixel values, and a chaotic Henon map is utilized to generate an aleatory sequence for the AES algorithm. The shuffled watermarked image is encrypted using the modified AES algorithm. The constant of Weber is used to choose the suitable visibility factor for embedding a watermark with high robustness. To control identification, after application of attacks, we use the serial turbo code for correction of the watermark to recover the data inserted. The average peak signal-to-noise ratio (PSNR) of the medical images obtained is 61,7769 dB. Experimental results demonstrate the robustness of the proposed schema against various types of attacks.

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An Enhencment Medical Image Compression Algorithm Based on Neural Network

Cited by 9 publications

References 15 publications

Dense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation

Dense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation

Pyramid based Progressive Transmission System with Hybrid Compression Scheme for Medical Images

Crypto-Watermarking Algorithm Using Weber’s Law and AES: A View to Transfer Safe Medical Image

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