Development of improved SSIM quality index for compressed medical images

Kumar, Basant; Kumar, Shishu Bind; Kumar, C. N. Ravi

doi:10.1109/iciip.2013.6707593

Cited by 28 publications

(10 citation statements)

References 8 publications

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“…These dependencies are beneficial in terms of object structure present in the visual site. 19 Similarly, a structural similarity index measures the quality of an image. It is defined as a parameter that measures the visual similarity between two frames, reference and distorted frames.…”

Section: Structural Similarity Indexmentioning

confidence: 99%

Multistage Forward Path Regenerative Genetic Algorithm for Brain Magnetic Resonant Imaging Registration

et al. 2022

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In image registration, the search space used to compute the optimal transformation between the images depends on the group of pixels in the vicinity. Favorable results can be achieved by significantly increasing the number of neighboring pixels in the search space; however, this strategy increases the computational load, thus making it challenging to realize the most desirable solution in a reasonable amount of time. To address the mentioned problem, the genetic algorithm is used to find the optimum solution and the solution lies in finding the best chromosomes. In rigid image registration problem, chromosomes contain a set of three parameters, x-translation, y-translation, and rotation. The genetic algorithm iteratively improves chromosomes from generation to generation and selects the best one having the best fittest value. Chromosomes with high fitness value are the ones with an optimal solution where the template image best aligns reference image. Fitness function in the genetic algorithm for image registration problem uses similarity measure index measure to find the amount of similarity between two images. The best fittest value is the one with a high similarity measure that shows the best-aligned template and reference image. Here we used the structural similarity index measure in fitness function that helps in evaluating the best chromosome, even for the compressed images with low quality, intensity nonuniformity (INU), and noise degradation. Building on the genetic algorithm, we propose a novel approach called multistage forward path regenerative genetic algorithm (MFRGA), abbreviated as MFRGA, with reducing search space at each stage. Compared with the single stage of genetic algorithm, our approach proved to be more reliable and accurate in terms of finding true rigid image transformation for alignment. At each increasing stage of MFRGA, results are computed with decreasing search space and increasing precision levels. Moreover, to prove the robustness of our algorithm, we utilized compressed images of brain magnetic resonant imaging that vary in compression qualities ranging from 10 to 100. Furthermore, we added noise levels of 1%, 3%, 5%, 7%, and 9% with an INU of 20% and 40%, respectively, provided by the online BrainWeb simulator. We achieved the monomodal rigid image registration that proves to be successful using MFRGA, even when the noise is critical, the compression quality is the least, and the intensity is nonuniform.

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Section: Structural Similarity Indexmentioning

confidence: 99%

Multistage Forward Path Regenerative Genetic Algorithm for Brain Magnetic Resonant Imaging Registration

et al. 2022

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“…In medical images, Kocsis et al [25] proposed a set of quantitative measurements related to medical image quality parameters, Perez-Diaz in his work [26] presents a study using some known quality metrics to evaluate the quality in medical images, and Chow and Paramesran [27] publish a review and a classification of medical image quality assessment. An improved SSIM quality for compressed medical images was developed by Kumar et al [28]. Gaudeau et al [29] used some compressed image quality assessment in interactive upper limb radiology images and Liu et al [30] studied a perceptual quality assessment used in medical images.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

Bouida¹,

Beladgham²,

Bassou³

et al. 2020

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The importance of image compression is now essential during transmission or storage processes in various data applications, especially in medical and biometric systems. To perform the effectiveness of the compression process on images and evaluate degradation caused by this process, image quality assessment becomes an important tool in image services. We note that the objective criteria in image quality depend especially on the image type and image texture composition. The actual tendency is to find metrics making better qualification on errors in compressed images and correlate with the human visual system. This paper presents an investigation to examine and evaluate image compression degradation by the use of a new tendency concept of image quality assessment based on texture and edge analysis. To perform and practice this evaluation, we compress the medical and biometric images using second-generation wavelet compression algorithms and study the degradation of texture information in these images.

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“…No modelo proposto, três métricas de qualidade visual servem como entrada do sistema, cuja saídaé a qualidade percebida da imagem que se quer avaliar. São elas: Gradient map weighting-gradient diretion similarity (GMW-GDSIM), Edge preservation measure (EPM) e o Mean structural similarity index (MSSIM) [16], [17], [25]. Na Figura 5, o fluxograma do desenho do modeloé apresentado.…”

Section: B Medida De Qualidade Propostaunclassified

Avaliação de Qualidade Visual Baseada em Rede Bayesiana Aplicada a Imagens Médicas

Gonçalves¹,

Júnior²,

Zampolo³

et al. 2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Development of improved SSIM quality index for compressed medical images

Cited by 28 publications

References 8 publications

Multistage Forward Path Regenerative Genetic Algorithm for Brain Magnetic Resonant Imaging Registration

Multistage Forward Path Regenerative Genetic Algorithm for Brain Magnetic Resonant Imaging Registration

Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

Avaliação de Qualidade Visual Baseada em Rede Bayesiana Aplicada a Imagens Médicas

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