An improved approach for medical image fusion using sparse representation and Siamese convolutional neural network

Multimodal image fusion is a contemporary branch of medical imaging that aims to increase the accuracy of clinical diagnosis of the disease stage development. The fusion of different image modalities can be a viable medical imaging approach. It combines the best features to produce a composite image with higher quality than its predecessors and can significantly improve medical diagnosis. Recently, sparse representation (SR) and Siamese Convolutional Neural Network (SCNN) methods have been introduced independently for image fusion. However, some of the results from these approaches have recorded defects, such as edge blur, less visibility, and blocking artifacts. To remedy these deficiencies, in this paper, a smart blending approach based on a combination of SR and SCNN is introduced for image fusion, which comprises three steps as follows. Firstly, entire source images are fed into the classical orthogonal matching pursuit (OMP), where the SR-fused image is obtained using the max-rule that aims to improve pixel localization. Secondly, a novel scheme of SCNN-based K-SVD dictionary learning is re-employed for each source image. The method has shown good non-linearity behavior, contributing to increasing the fused output's sparsity characteristics and demonstrating better extraction and transfer of image details to the output fused image. Lastly, the fusion rule step employs a linear combination between steps 1 and 2 to obtain the final fused image. The results depict that the proposed method is advantageous, compared to other previous methods, notably by suppressing the artifacts produced by the traditional SR and SCNN model

Improved implementation of digital watermarking techniques

With the advancement of high speed computer networks, multimedia storage and transmission technology, anenormous amount of information is being communicated in digital form. There is a constant threat to copyright, ownership and integrity of digital data. Therefore information security has become an emerging area of research. Security of images is an area of major concern as digital images are available all over World Wide Web. Numerous watermarking techniques have been developed to protect images from illegal manipulations. In this project, spatial domain and transform domain have been proposed. Histogram shifting falls under the spatial domain method and spread spectrum falls under transform domain method. For the proposed Histogram Shifting, modification of the histogram and shifting is applied for embedding the stage, add to that our contribution in this method incorporated the threshold concept to improve the visual quality of the host image. In the proposed Spread Spectrum, a chaos-based spread spectrum watermarking algorithm is developed in the DCT domain for still image. The most significant feature of chaos is its sensitivity to initial conditions. This characteristic of chaos has been used successfully for secure watermarking applications. Local properties of the image and the features of the human visual system are considered in order to optimize the watermark strength in addition to the incorporated luminance masking effect of the HVS in the masking image, since the human eye is less sensitive to change in regions with high brightness as well as in very dark regions in comparison to mid-grey regions, where the distortion is most noticeable. The contribution only uses luminance masking for enhancing the visual quality of images in the embedding stage instead of using a combination of different masks and that leads to less complexity. The performance of the proposed watermarking schemes have been evaluated by using the watermarked images of size 512×512, and the watermark (payload) is of the same size as the host image in the spread spectrum with a different amount of variables "random bit streams" used in the histogram sniffing. The simulations are performed in MATLAB 7 software environment. The PSNR metric and SSIM with GMSD are applied to measure the degradation of the images. A comparison is made between the results of the proposed algorithms with each other and also compared with the best method namely Bit Plane Mapping and demonstrates that the proposed methods are superior in performance and especially the proposed Spread Spectrum

Review on anatomical medical images classification methods: Diagnostic value

Deep learning (DL) based convolutional neural network (CNN) has grown rapidly and become a selected choice for medical imaging fields. The paper reviews into three categories; 1) exploring the supervised machine learning methods, 2) reviews the limitations in deep learning, 3) and finally reviews the majors deep learning techniques within a specific summarized on lesion classification-based DL in term of (application, method, type of lesion diseases classification, cons)

A new scheme of medical image fusion using deep convolutional neural network and local energy pixel domain

In this paper, a new multimodal medical image fusion method based on deep convolutional neural networks (CNN) and local spatial domain modification is proposed. First, the source image is fed to Siamese CNN to obtain the weight map and then processed by the Weighted Sum of Eight neighbourhood-based Modified Laplacian (WSEML) to obtain a new image-based WSEML. Next, CT and MRI input images are fed to Weighted Local Energy (WLE). Finally, the activity level measurement based on local energy is dedicated to combining each of the new WLE images and new WSEML images to retrieve useful information at the reconstruction stage. Simulation results demonstrate that the proposed method extracted more useful information with higher visibility from source images, and at the same time reduce fused image artefacts

A novel pathological stroke classification system using NSST and WLEPCA

Stroke is a type of cerebrovascular disease, and it is one of the leading cause of death, with over six million deaths recorded annually. In this paper, a novel scheme for an accurate multi-class stroke disease classification named Pathological Stroke Classification System (PSCS) is introduced to classify stroke disease into six classes. Features are extracted using Nonsubsampled Shearlet Transform (NSST), which decomposes the fused image into the low-frequency band and k-bands of high frequency. The low-frequency band is further analyzed using a new scheme of feature reduction and selection using weighted local energy based principal component analysis (WLEPCA). Different subsets of principal vectors are applied to three decision models, k-Nearest Neighbors (KNN), random forest (RF), and Support Vector Machine (SVM). The RF-based classifier performed better than SVM and k-NN and achieved an accuracy of 96.10%. The proposed PSCS showed a promising result in stroke classification can be considered as a reliable and robust diagnostic tool for medical practitioners