A METAHEURISTIC APPROACH FOR TETROLET BASED MEDICAL IMAGE COMPRESSION

Over recent times, medical imaging plays a significant role in clinical practices. Storing and transferring the huge volume of images becomes complicated without an efficient image compression technique. This paper proposes a compression algorithm that uses a Haar based wavelet transform called Tetrolet transform, which reduces the noise on the input images and decomposes with a 4 x 4 blocks of equal squares called tetrominoes. It opts for a decomposing using optimal scheme for achieving the input image into a sparse representation which gives a much-detailed performance for texture and edge information better than wavelet transform. Set Partitioning in Hierarchical Trees (SPIHT) is used for encoding the significant coefficients to achieve efficient image compression. It has been investigated with various metaheuristic algorithms. Experimental results prove that the proposed method outperforms the other transform-based compression in terms of PSNR, CR, and Complexity. Also, the proposed method shows an improved result with another state of work.

A Metaheuristic Approach for Tetrolet-Based Medical Image Compression

Over recent times, medical imaging plays a significant role in clinical practices. Storing and transferring the huge volume of images becomes complicated without an efficient image compression technique. This paper proposes a compression algorithm that uses a Haar based wavelet transform called Tetrolet transform, which reduces the noise on the input images and decomposes with a 4 x 4 blocks of equal squares called tetrominoes. It opts for a decomposing using optimal scheme for achieving the input image into a sparse representation which gives a much-detailed performance for texture and edge information better than wavelet transform. Set Partitioning in Hierarchical Trees (SPIHT) is used for encoding the significant coefficients to achieve efficient image compression. It has been investigated with various metaheuristic algorithms. Experimental results prove that the proposed method outperforms the other transform-based compression in terms of PSNR, CR, and Complexity. Also, the proposed method shows an improved result with another state of work.

ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance

Iris Recognition at-a Distance (IAAD) is a major challenge for researchers due to the defects associated with the visual imaging and poor image quality in dynamic environments, which imposed bad impacts on the accuracy of recognition. Thus, in order to enable the effective IAAD, this paper proposes a new method, named, Chronological Monarch Butterfly Optimization (Chronological MBO)-enabled Neural Network (NN). The recognition of iris using NN is trained with the proposed Chronological MBO, which is developed through the combination of Chronological theory in Monarch Butterfly Optimization (MBO). The recognition becomes effective with the automatic segmentation and the normalization of iris image on the basis of Hough Transform (HT) and Daugman’s rubber sheet model followed with the process of feature extraction with the developed ScatT-LOOP descriptor, which is the integration of scattering transform (ST), Local Optimal Oriented Pattern (LOOP) descriptor, and Tetrolet transform (TT). The developed ScatT-LOOP descriptor extracts the texture as well as the orientation details of image for effective recognition. The analysis is evaluated with the CASIA Iris dataset with respect to the evaluation metrics, accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR). The proposed method has the accuracy, FRR, and FAR of 0.97, 0.005, and 0.005, respectively. The experimental results proved that the proposed method is effective than the existing methods of iris recognition.

Chicken-Moth Search Optimization-based Deep Convolutional Neural Network for Image Steganography

Image steganography is considered as one of the promising and popular techniques utilized to maintain the confidentiality of the secret message that is embedded in an image. Even though there are various techniques available in the previous works, an approach providing better results is still the challenge. Therefore, an effective pixel prediction based on image stegonography is developed, which employs error dependent Deep Convolutional Neural Network (DCNN) classifier for pixel identification. Here, the best pixels are identified from the medical image based on DCNN classifier using pixel features, like texture, wavelet energy, Gabor, scattering features, and so on. The DCNN is optimally trained using Chicken-Moth search optimization (CMSO). The CMSO is designed by integrating Chicken Swarm Optimization (CSO) and Moth Search Optimization (MSO) algorithm based on limited error. Subsequently, the Tetrolet transform is fed to the predicted pixel for the embedding process. At last, the inverse tetrolet transform is used for extracting the secret message from an embedded image. The experimentation is carried out using BRATS dataset, and the performance of image stegonography based on CMSO-DCNN+tetrolet is evaluated based on correlation coefficient, Structural Similarity Index, and Peak Signal to Noise Ratio, which attained 0.85, 46.981dB, and 0.6388, for the image with noise.