Improving the non-extensive medical image segmentation based on Tsallis entropy.
ABSTRACT Thresholding techniques for image segmentation is one of the most popular approaches in Computational Vision systems. Recently,
M. Albuquerque has proposed a thresholding method (Albuquerque et al. in Pattern Recognit Lett 25:1059–1065, 2004) based on the Tsallis entropy, which is a generalization of the traditional Shannon entropy through the introduction of an
entropic parameter q. However, the solution may be very dependent on the q value and the development of an automatic approach to compute a suitable value for q remains also an open problem. In this paper, we propose a generalization of the Tsallis theory in order to improve the non-extensive
segmentation method. Specifically, we work out over a suitable property of Tsallis theory, named the pseudo-additive property,
which states the formalism to compute the whole entropy from two probability distributions given an unique q value. Our idea is to use the original M. Albuquerque’s algorithm to compute an initial threshold and then update the q value using the ratio of the areas observed in the image histogram for the background and foreground. The proposed technique
is less sensitive to the q value and overcomes the M. Albuquerque and k-means algorithms, as we will demonstrate for both ultrasound breast cancer images and synthetic data.
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ABSTRACT: Diffusion weighted imaging (DWI) and dif-fusion tensor imaging (DTI) are noisy submodalities images in magnetic resonance imaging (MRI) and usu-ally have long acquisition time due to repetitions needed to improve the signal noise ratio (SNR). Here we pro-pose and evaluate anisotropic anomalous diffusion (AAD) filter on DTI and DWI to enhance SNR and reduce the need for repetitions. Anisotropic anomalous diffusion filter is an iterative parametric diffusion filter based on anomalous diffusion. Fractional anisotropy (FA) and mean diffusivity (MD) maps were acquired with different repetitions times and processed using AAD filter to investigate optimum q parameter. SNR, RMSE and Full Width at Half Maximum (FWHM) measures were evaluated as image quality metrics. The results show that filtering based on AAD approach can improve DWI and DTI image quality and preserving relevant aspects in images. We conclude that when the AAD is applied on DWI and DTI images maps we can use images with lower acquisition repetitions time and maintain the image quality comparable to higher acqui-sition images.XXIV Brazilian Congress on Biomedical Engineering, Uberlândia; 10/2014
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ABSTRACT: Thresholding methods based on entropy have been proposed and developed over the years. In this paper, an improved Tsallis entropy based thresholding method is proposed for segmenting the images which presenting local long-range correlation rather than global long-range correlation. The advantage of the proposed method is to distinguish the pixels' local long-range correlation by the nonextensive parameter q. And the experimental results of various infrared images as well as nondestructive test ones show the effectiveness of the proposed method.Signal Processing. 12/2012; 92(12):2931–2939.
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ABSTRACT: Noise is inherent to Diffusion-Weighted Magnetic Resonance Imaging (DWI) and noise reduction methods are necessary. Although process based on classical diffusion is one of the most used approaches for digital image, anomalous diffusion has the potential for image enhancement and it has not been tested for DWI noise reduction. This study evaluates Anomalous Diffusion (AD) filter as DWI enhancement method. The proposed method was applied to magnetic resonance diffusion weighted images (DW-MRI) with different noise levels. Results show better performance for anomalous diffusion when compared to classical diffusion approach. The proposed method has shown potential in DWI enhancement and can be an important process to improve quality in DWI for neuroimage-based diagnosis.Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:4022-4025.