Anatomy and Physiology in Health and Illness

A novel method for performing anatomical magnetic resonance imaging-functional (positron emission tomography or single photon emission computed tomography) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image, while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering-based technique realized through a novel multi-scale system architecture. First, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Second, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared with the state-of-the-art fusion methods.

Anatomical-Functional Image Fusion by Information of Interest in Local Laplacian Filtering Domain

Finding an objective image quality metric that matches the subjective quality has always been a challenging task. We propose a new full reference image quality metric based on features extracted from Convolutional Neural Networks (CNNs). Using a pre-trained AlexNet model, we extract feature maps of the test and reference images at multiple layers, and compare their feature similarity at each layer. Such similarity scores are then pooled across layers to obtain an overall quality value. Experimental results on four state-of-the-art databases show that our metric is either on par or outperforms 10 other state-of-the-art metrics, demonstrating that CNN features at multiple levels are superior to handcrafted features used in most image quality metrics in capturing aspects that matter for discriminative perception. c © 2016 Society for Imaging Science

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Image Quality Assessment by Comparing CNN Features between Images.

An image quality matrix provides a significant principle for objectively observing an image based on an alteration between the original and distorted images. During the past two decades, a novel universal image quality assessment has been developed with the ability of adaptation with human visual perception for measuring the difference of a degraded image from the reference image, namely a structural similarity index. Structural similarity has since been widely used in various sectors, including medical image evaluation. Although numerous studies have reported the use of structural similarity as an evaluation strategy for computer-based medical images, reviews on the prospects of using structural similarity for medical imaging applications have been rare. This paper presents previous studies implementing structural similarity in analyzing medical images from various imaging modalities. In addition, this review describes structural similarity from the perspective of a family’s historical background, as well as progress made from the original to the recent structural similarity, and its strengths and drawbacks. Additionally, potential research directions in applying such similarities related to medical image analyses are described. This review will be beneficial in guiding researchers toward the discovery of potential medical image examination methods that can be improved through structural similarity index.

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Prospects of Structural Similarity Index for Medical Image Analysis

The compression of medical images is extremely important in medical applications. The compression algorithms allow an efficient way to represent image data, reducing the space required for storage and allow to minimize the demand in transmission through the communication channels. In the JPEG algorithm, the quantization matrix determines the quality of the reconstructed image and thus improved matrices were constructed. This paper makes a comparative study of the performance of the algorithm using JPEG quantization matrices optimized for compression of video frames of ultrasound image. A comparison was made between the traditional quantization matrix of the JPEG algorithm and a set of quantization matrices optimized for this algorithm. This comparison is made using compression measured in bits per [bpp] and criteria of objective fidelity (PSNR, MSE, SSIM and CC). There is coherence between the values of PSNR, MSE, SSIM and CC and the results show that the improved quantization matrices outperform the traditional matrix in terms of performance and can improve the image quality to values of PSNR slightly above 2dB.

Comparative study of the performance of the JPEG algorithm using optimized quantization matrices for ultrasound image compression

This paper presents the development of improved structural similarity index (SSIM) quality assessment parameter for compressed medical images. The SSIM has been proved to be better objective quality assessment metric which exploits the structural similarity in the viewing field. In this paper, the SSIM quality index has been modified by achieving optimal value of arbitrary constant K used in SSIM index expression. The analysis has been done for three different types of medical image; MRI scan, CT scan and ultrasound image. SSIM index along with PSNR, MSE and mean-opinion-score (MOS) are computed for SPIHT compressed medical images at varying compression rates by setting values of K in the range 0.02 to 2.0. The value of K giving maximum correlation coefficient (CC) between SSIM index and MOS is selected as optimal value. It is found that optimal values of K equal to 0.5, 0.05 and 0.1 for MRI, CT scan and ultrasound images respectively.

Development of improved SSIM quality index for compressed medical images

This paper is focused on analysing the effect of wavelet filters on Space frequency quantization (SFQ) coder performance. SFQ is a new class of image coding algorithms coupling standard scalar quantization of frequency coefficients with tree-structured quantization (related to spatial structures) has attracted wide attention because its good performance appears to confirm the promised efficiencies of hierarchical representation [1]. It has been shown that the statistics of wavelet coefficients in the medical images can be better approximated by the Generalized Pareto distribution (for positive DWT coefficients) and Generalized Student-t distribution (for negative DWT coefficients) [2]. To analyze the performance of SFQ coder with different wavelet filters PSNR (peak signal to noise ratio) is measured at various compression rates.

Chandrahas Kumar

Papers

Development of improved SSIM quality index for compressed medical images

Effect of wavelet filters on SFQ encoder performance for medical images