University of Engineering & Management, Kolkata, India.
Institute of Engineering & Management, Kolkata, India.
Appl Biochem Biotechnol. 2023 Apr;195(4):2196-2215. doi: 10.1007/s12010-022-04108-y. Epub 2022 Sep 21.
The current ongoing trend of dimension detection of medical images is one of the challenging areas which facilitates several improvements in accurate measuring of clinical imaging based on fractal dimension detection methodologies. For medical diagnosis of any infection, detection of dimension is one of the major challenges due to the fractal shape of the medical object. Significantly improved outcome indicates that the performance of fractal dimension detection techniques is better than that of other state-of-the-art methods to extract diagnostically significant information from clinical image. Among the fractal dimension detection methodologies, fractal geometry has developed an efficient tool in medical image investigation. In this paper, a novel methodology of fractal dimension detection of medical images is proposed based on the concept of box counting technique to evaluate the fractal dimension. The proposed method has been evaluated and compared to other state-of-the-art approaches, and the results of the proposed algorithm graphically justify the mathematical derivation of the box counting approach in terms of Hurst exponent.
当前医学图像的维数检测趋势是一个具有挑战性的领域，它通过分形维数检测方法极大地促进了临床成像的精确测量的改进。对于任何感染的医学诊断，由于医学对象的分形形状，检测维度是主要挑战之一。显著改善的结果表明，分形维数检测技术的性能优于其他最先进的方法，可从临床图像中提取具有诊断意义的信息。在分形维数检测方法中，分形几何为医学图像研究开发了一种有效的工具。在本文中，提出了一种基于盒子计数技术概念的医学图像分形维数检测的新方法，以评估分形维数。该方法已经进行了评估并与其他最先进的方法进行了比较，并且所提出的算法的结果从 Hurst 指数方面直观地证明了盒子计数方法的数学推导。
