Abstract: Image segmentation plays an important role in
medical imaging applications. Therefore, accurate methods are
needed for the successful segmentation of medical images for
diagnosis and detection of various diseases. In this paper, we have
used maximum entropy to achieve image segmentation. Maximum
entropy has been calculated using Shannon, Renyi and Tsallis
entropies. This work has novelty based on the detection of skin lesion
caused by the bite of a parasite called Sand Fly causing the disease is
called Cutaneous Leishmaniasis.
Abstract: In this paper, we propose a novel approach for image
segmentation via fuzzification of Rènyi Entropy of Generalized
Distributions (REGD). The fuzzy REGD is used to precisely measure
the structural information of image and to locate the optimal
threshold desired by segmentation. The proposed approach draws
upon the postulation that the optimal threshold concurs with
maximum information content of the distribution. The contributions
in the paper are as follow: Initially, the fuzzy REGD as a measure of
the spatial structure of image is introduced. Then, we propose an
efficient entropic segmentation approach using fuzzy REGD.
However the proposed approach belongs to entropic segmentation
approaches (i.e. these approaches are commonly applied to grayscale
images), it is adapted to be viable for segmenting color images.
Lastly, diverse experiments on real images that show the superior
performance of the proposed method are carried out.
Abstract: Artifact rejection plays a key role in many signal processing applications. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Our aim is to automatically remove the artifacts, in particular from the Electroencephalographic (EEG) recordings. A technique for the automatic artifact rejection, based on the Independent Component Analysis (ICA) for the artifact extraction and on some high order statistics such as kurtosis and Shannon-s entropy, was proposed some years ago in literature. In this paper we try to enhance this technique proposing a new method based on the Renyi-s entropy. The performance of our method was tested and compared to the performance of the method in literature and the former proved to outperform the latter.
Abstract: The goal of this work is to improve the efficiency and the reliability of the automatic artifact rejection, in particular from the Electroencephalographic (EEG) recordings. Artifact rejection is a key topic in signal processing. The artifacts are unwelcome signals that may occur during the signal acquisition and that may alter the analysis of the signals themselves. A technique for the automatic artifact rejection, based on the Independent Component Analysis (ICA) for the artifact extraction and on some high order statistics such as kurtosis and Shannon-s entropy, was proposed some years ago in literature. In this paper we enhance this technique introducing the Renyi-s entropy. The performance of our method was tested exploiting the Independent Component scalp maps and it was compared to the performance of the method in literature and it showed to outperform it.
Abstract: In present communication, we have developed the
suitable constraints for the given the mean codeword length and the
measures of entropy. This development has proved that Renyi-s
entropy gives the minimum value of the log of the harmonic mean
and the log of power mean. We have also developed an important
relation between best 1:1 code and the uniquely decipherable code by
using different measures of entropy.