Abstract
Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. The proposed model estimates the probability of pixels that represent edges based on the entropy of the neighboring pixels, which results in local fractional entropy. When there is a small change in the intensity values (indicating the presence of edge in the image), the local fractional entropy gives fine image details. Similarly, when no change in intensity values is present (indicating smooth texture), the LFE does not provide fine details, based on the fact that there is no edge information. Tests were conducted on a large dataset of different, poor-quality kidney images to show that the proposed model is useful and effective. A comparative study with the classical methods, coupled with the latest enhancement methods, shows that the proposed model outperforms the existing methods.
Highlights
As one’s living style changes, various unexpected health issues arise in the same proportion.To find solutions to such diseases, new devices and systems have been developed
In this paper, we propose a generalized and new model for enhancing poor quality kidney images based on Local Fractional Entropy (LFE)
Motivated by the methods [3,10,11,12,13] that indicate that the Fractional calculus has an ability to enhance low contrast information, we explore the same methods in new way for addressing the issue of poor quality kidney images
Summary
As one’s living style changes, various unexpected health issues arise in the same proportion. Few methods addressed the issues of low contrast and poor quality of images by proposing fractional-based models. There is a scope for developing a generalized model for enhancing poor quality kidney images affected by several adverse factors such as MRI systems, diseases, and noise. In this paper, we propose a generalized and new model for enhancing poor quality kidney images based on Local Fractional Entropy (LFE). Motivated by the methods [3,10,11,12,13] that indicate that the Fractional calculus has an ability to enhance low contrast information, we explore the same methods in new way for addressing the issue of poor quality kidney images. The remainder of this paper is prepared as follows: Section 2 describes Local Fractional Entropy for kidney images, Section 3 discusses the experimental results for validating the proposed model, and, Section 4 presents the conclusion and future work
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