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A Review of Image Denoising and Segmentation Methods Based on Medical Images

In the medical sector, the digital image is multimedia data that contain secret information. However, designing an efficient secure cryptosystem to protect the confidential images in sharing is a challenge. In this work, we propose an improved chaos-based cryptosystem to encrypt and decrypt rapidly secret medical images. A complex chaos-based PRNG is suggested to generate a high-quality key that presents high randomness behaviour, high entropy, and high complexity. An improved architecture is proposed to encrypt the secret image that is based on permutation, substitution, and diffusion properties. In the first step, the image’s pixels are randomly permuted through a matrix generated using the PRNG. Next, pixel’s bits are permuted using an internal condition. After that, the pixels are substituted using two different S-boxes with an internal condition. In the final step, the image is diffused by XORing pixels with the key stream generated by the PRNG in order to acquire an encrypted image. R rounds of encryption can be performed in a loop to increase the complexity. The cryptosystem is evaluated in depth by his application on several medical images with different types, contents, and sizes. The obtained simulation results demonstrate that the system enables high-level security and performance. The information entropy of the encrypted image has achieved an average of 7.9998 which is the most important feature of randomness. The algorithm can take full advantage of parallelism and pipeline execution in hardware implementation to meet real-time requirements. The PRNG was tested by NIST 800-22 test suit, which indicates that it is suitable for secure image encryption. It provides a large key space of 2192 which resists the brute-force attack. However, the cryptosystem is appropriate for medical image securing.

Improved Chaos-Based Cryptosystem for Medical Image Encryption and Decryption

The magnetic resonance imaging (MRI) modality is an effective tool in the diagnosis of the brain. These MR images are introduced with noise during acquisition which reduces the image quality and limits the accuracy in diagnosis. Elimination of noise in medical images is an important task in preprocessing and there exist different methods to eliminate noise in medical images. In this article, different denoising algorithms such as nonlocal means, principal component analysis, bilateral, and spatially adaptive nonlocal means (SANLM) filters are studied to eliminate noise in MR. Comparative analysis of these techniques have been with help of various metrics such as signal-to-noise ratio, peak signal-to-noise ratio (PSNR), mean squared error, root mean squared error, and structure similarity (SSIM). This comparative study shows that the SANLM denoising filter gives the best performance in terms of better PSNR and SSIM in visual interpretation. It also helps in clinical diagnosis of the brain.

Analysis of denoising filters on MRI brain images

The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this study is to test a systematic procedure for implementing artificial neural networks to predict academic performance in higher education. The second objective is to analyze the importance of several well-known predictors of academic performance in higher education. The sample included 162,030 students of both genders from private and public universities in Colombia. The findings suggest that it is possible to systematically implement artificial neural networks to classify students’ academic performance as either high (accuracy of 82%) or low (accuracy of 71%). Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score. Furthermore, it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education. Finally, this study discusses recommendations for implementing artificial neural networks and several considerations for the analysis of academic performance in higher education.

Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation

This paper proposes the discrete wavelet transform (DWT) block-based scrambling algorithm used for medical image encryption which applies the edge maps extracted from a source image. The method comprises three major stages: DWT-plane decomposition, generation of edge map sequences, and DWT-level scrambling. In the first stage, the original medical images are decomposed into numerous DWT-planes. In the second stage, the deriche edge detector method has been presented to estimate the edge maps, which must be a similar size to the DWT-bit scales. The DWT-level scrambling has been used to isolate the neighboring pixels into various rows and columns, thus it weakens the strong correlation among the neighboring pixels efficiently. This proposed method shows the Number of Pixel Change Rate as 99.592% and Unified Average Hanged Intensity of 34.268%. Furthermore, simulations and security analysis shows strong resistance to different security attacks and perform better other conventional methods.

Novel medical image encryption using DWT block-based scrambling and edge maps

Segmentation of tumors in human brain aims to classify different abnormal tissues necrotic core, edema, active cells from normal tissues cerebrospinal fluid, gray matter, white matter of the brain. In existence, detection of abnormal tissues is easy for studying brain tumor, but reproducibility, characterization of abnormalities and accuracy are complicated in the process of segmentation. The magnetic resonance imaging MRI-based segmentation of tumors in brain images is more enhancing and attracting in current years of research studies. It is due to non-invasive examination and good contrast prone to soft tissues of images obtained from MRI modality. Medical approval of different segmentation techniques depends on the benchmark and simplicity of the method. This article incorporates both fully-automatic and semi-automatic methods for segmentation. The outlook study of this article is to provide the summary of most significant segmentation methods of tumors in brain using MRI. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 295-304, 2016

A comprehensive review: Segmentation of MRI images-brain tumor

Peak-to-average power ratio (PAPR) is found to be a significant role in multiple input, multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Partial transmit sequence (PTS) is one of the effective methods to reduce the PAPR in OFDM signals. However, finding an optimum phase factor in PTS scheme is considered to be a significant issue. In order to overcome this problem, we use orthogonal initialization method to identify the initial population for enhanced Artificial Bee Colony (ABC) algorithm. In addition, PTS technique uses exhaustive search method to find the better combination of phase factors. This would also increase the computational complexity of the system. In order to overcome this issue, a newly suboptimal method is also used in the enhanced ABC algorithm. Finally, we have combined the space time block coding (STBC) and the PTS based enhanced ABC algorithm to reduce the PAPR and bit error rate (BER). The orthogonal initialization method and suboptimal method in ABC algorithm greatly reduces the PAPR and BER in the OFDM system. MATLAB software is used to run this simulation.

Hybrid STBC-PTS with enhanced artificial bee colony algorithm for PAPR reduction in MIMO-OFDM system

This paper implements a novel approach for image steganography based on Hidden Markov Tree (HMT) Contourlet transform. In this paper, the biomedical image considers as a cover image and it is mapped to a specific frequency domain by applying HMT Contourlet transform. Then canny edge detection method implemented to detect the smooth edges to hide the secret data. The secret data is encrypted by using Paillier cryptosystem in a new location of the cover image. Particle Swarm Optimization (PSO) algorithm developed for the selection of the best place to locate the number of particles in a new location. The proposed method prevents the medical image from the various attacks such as rotate, crop, histogram, salt & pepper, blur and resize provides the robustness, thereby reduces to 8.19%, 10.88%, 24.03%, 15.27%, 13.21% and 14.35%. The performance measures of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) are calculated to show the better performance compared with the existing techniques.

N. Amutha Prabha

Papers

Analysis of denoising filters on MRI brain images

A comprehensive review: Segmentation of MRI images-brain tumor

Novel medical image encryption using DWT block-based scrambling and edge maps

Hybrid STBC-PTS with enhanced artificial bee colony algorithm for PAPR reduction in MIMO-OFDM system

Effective payload and improved security using HMT Contourlet transform in medical image steganography