Recently,many rapid developments in digital medical imaging have made further contributions to health care systems.The segmentation of regions of interest in medical images plays a vital role in assisting doctors with...Recently,many rapid developments in digital medical imaging have made further contributions to health care systems.The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses.Many factors like image contrast and quality affect the result of image segmentation.Due to that,image contrast remains a challenging problem for image segmentation.This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans.The proposed work consists of two stages:enhancement by fractional Rényi entropy,and MRI Kidney deep segmentation.The proposed enhancement model exploits the pixel’s probability representations for image enhancement.Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels,yielding an overall better details of the kidney MRI scans.In the second stage,the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans.The experimental results showed an average of 95.60%dice similarity index coefficient,which indicates best overlap between the segmented bodies with the ground truth.It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.展开更多
The problem of embedding the Tsallis, Rényi and generalized Rényi entropies in the framework of category theory and their axiomatic foundation is studied. To this end, we construct a special category MES rel...The problem of embedding the Tsallis, Rényi and generalized Rényi entropies in the framework of category theory and their axiomatic foundation is studied. To this end, we construct a special category MES related to measured spaces. We prove that both of the Rényi and Tsallis entropies can be imbedded in the formalism of category theory by proving that the same basic partition functional that appears in their definitions, as well as in the associated Lebesgue space norms, has good algebraic compatibility properties. We prove that this functional is both additive and multiplicative with respect to the direct product and the disjoint sum (the coproduct) in the category MES, so it is a natural candidate for the measure of information or uncertainty. We prove that the category MES can be extended to monoidal category, both with respect to the direct product as well as to the coproduct. The basic axioms of the original Rényi entropy theory are generalized and reformulated in the framework of category MES and we prove that these axioms foresee the existence of an universal exponent having the same values for all the objects of the category MES. In addition, this universal exponent is the parameter, which appears in the definition of the Tsallis and Rényi entropies. It is proved that in a similar manner, the partition functional that appears in the definition of the Generalized Rényi entropy is a multiplicative functional with respect to direct product and additive with respect to the disjoint sum, but its symmetry group is reduced compared to the case of classical Rényi entropy.展开更多
Though manifold learning has been success-fully applied in wide areas, such as data visu-alization, dimension reduction and speech rec-ognition;few researches have been done with the combination of the information the...Though manifold learning has been success-fully applied in wide areas, such as data visu-alization, dimension reduction and speech rec-ognition;few researches have been done with the combination of the information theory and the geometrical learning. In this paper, we carry out a bold exploration in this field, raise a new approach on face recognition, the intrinsic α-Rényi entropy of the face image attained from manifold learning is used as the characteristic measure during recognition. The new algorithm is tested on ORL face database, and the ex-periments obtain the satisfying results.展开更多
Coherence is a fundamental ingredient for quantum physics and a key resource for quantum information theory.Baumgratz,Cramer and Plenio established a rigorous framework(BCP framework)for quantifying coherence[Baumgrat...Coherence is a fundamental ingredient for quantum physics and a key resource for quantum information theory.Baumgratz,Cramer and Plenio established a rigorous framework(BCP framework)for quantifying coherence[Baumgratz T,Cramer M and Plenio M B Phys.Rev.Lett.113140401(2014)].In the present paper,under the BCP framework we provide two classes of coherence measures based on the sandwiched Rényi relative entropy.We also prove that we cannot get a new coherence measure f(C(·))by a function f acting on a given coherence measure C.展开更多
针对医学图像配准对鲁棒性强、准确性高和速度快的要求,本文提出一种基于Rényi熵的互补尺度空间关键点配准算法。该算法首先从图像上提取Harris-Laplace(HL)和Laplacian of Gaussian(LoG)两种互补的尺度空间关键点,然后将关键点对...针对医学图像配准对鲁棒性强、准确性高和速度快的要求,本文提出一种基于Rényi熵的互补尺度空间关键点配准算法。该算法首先从图像上提取Harris-Laplace(HL)和Laplacian of Gaussian(LoG)两种互补的尺度空间关键点,然后将关键点对应的灰度信息融入到联合Rényi熵中,最后使用最小生成树来估计联合Rényi熵。新算法结合了互补关键点的鲁棒性,和最小生成树估计Rényi熵的高效性。实验结果表明在图像含有噪声、灰度不均匀和初始误配范围较大的情况下,该算法在达到良好配准精度的同时,具有较强的鲁棒性和较快的速度。展开更多
基金funded by the deanship of scientific research at princess Nourah bint Abdulrahman University through the fast-track research-funding program.
文摘Recently,many rapid developments in digital medical imaging have made further contributions to health care systems.The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses.Many factors like image contrast and quality affect the result of image segmentation.Due to that,image contrast remains a challenging problem for image segmentation.This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans.The proposed work consists of two stages:enhancement by fractional Rényi entropy,and MRI Kidney deep segmentation.The proposed enhancement model exploits the pixel’s probability representations for image enhancement.Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels,yielding an overall better details of the kidney MRI scans.In the second stage,the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans.The experimental results showed an average of 95.60%dice similarity index coefficient,which indicates best overlap between the segmented bodies with the ground truth.It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.
文摘The problem of embedding the Tsallis, Rényi and generalized Rényi entropies in the framework of category theory and their axiomatic foundation is studied. To this end, we construct a special category MES related to measured spaces. We prove that both of the Rényi and Tsallis entropies can be imbedded in the formalism of category theory by proving that the same basic partition functional that appears in their definitions, as well as in the associated Lebesgue space norms, has good algebraic compatibility properties. We prove that this functional is both additive and multiplicative with respect to the direct product and the disjoint sum (the coproduct) in the category MES, so it is a natural candidate for the measure of information or uncertainty. We prove that the category MES can be extended to monoidal category, both with respect to the direct product as well as to the coproduct. The basic axioms of the original Rényi entropy theory are generalized and reformulated in the framework of category MES and we prove that these axioms foresee the existence of an universal exponent having the same values for all the objects of the category MES. In addition, this universal exponent is the parameter, which appears in the definition of the Tsallis and Rényi entropies. It is proved that in a similar manner, the partition functional that appears in the definition of the Generalized Rényi entropy is a multiplicative functional with respect to direct product and additive with respect to the disjoint sum, but its symmetry group is reduced compared to the case of classical Rényi entropy.
文摘Though manifold learning has been success-fully applied in wide areas, such as data visu-alization, dimension reduction and speech rec-ognition;few researches have been done with the combination of the information theory and the geometrical learning. In this paper, we carry out a bold exploration in this field, raise a new approach on face recognition, the intrinsic α-Rényi entropy of the face image attained from manifold learning is used as the characteristic measure during recognition. The new algorithm is tested on ORL face database, and the ex-periments obtain the satisfying results.
基金Project supported by the China Scholarship Council(Grant No.201806305050)
文摘Coherence is a fundamental ingredient for quantum physics and a key resource for quantum information theory.Baumgratz,Cramer and Plenio established a rigorous framework(BCP framework)for quantifying coherence[Baumgratz T,Cramer M and Plenio M B Phys.Rev.Lett.113140401(2014)].In the present paper,under the BCP framework we provide two classes of coherence measures based on the sandwiched Rényi relative entropy.We also prove that we cannot get a new coherence measure f(C(·))by a function f acting on a given coherence measure C.
基金supported by the National Natural Science Foundation of China(21503076)Aid Program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province,China(Xiang Jiao Tong[2012]318)~~
文摘针对医学图像配准对鲁棒性强、准确性高和速度快的要求,本文提出一种基于Rényi熵的互补尺度空间关键点配准算法。该算法首先从图像上提取Harris-Laplace(HL)和Laplacian of Gaussian(LoG)两种互补的尺度空间关键点,然后将关键点对应的灰度信息融入到联合Rényi熵中,最后使用最小生成树来估计联合Rényi熵。新算法结合了互补关键点的鲁棒性,和最小生成树估计Rényi熵的高效性。实验结果表明在图像含有噪声、灰度不均匀和初始误配范围较大的情况下,该算法在达到良好配准精度的同时,具有较强的鲁棒性和较快的速度。
