BACKGROUND:As the complexity of human anatomic structure,the abnormity of tissue shape and the difference among individuals,the structure of multifractal is adapted.OBJECTIVE:To investigate medical image segmentation based on multifractal.METHODS:Image segmentation was performed by algorithm based on capacity measurement and probability measure.The experimental images were segmented using traditional region growing,max capacity measurement,sum capacity measurement,and probability measure.Following adding noise,the images were identically segmented and compared.RESULTS AND CONCLUSION:In the two algorithms based on multifractal,the key of the algorithm based on capacity measurement is that appropriate measure μα is defined,and the key of the algorithm based on probability measure is that appropriate normalized probability Pi is defined.The different measures (probability) and thresholds bring greater effect.The method based on probability measure is sensitive to noises,but after filtration noise,segmentation effect is greater for the images whose pixels vary comparatively great and very complicated.The results show that it is feasible that appropriate measure (probability) and threshold is chosen based on medical image segmentation.Especially greater advantage exists for the distinction of texture and edge in the complicated image processing,which can reserve details while precisely dividing.It has very significant practical significance.At the same time,multifractal can also be characteristics of images,which provide powerful data for feature extraction.

Symbolic dynamics may be a new research direction for electroencephalogram (EEG) signal analysis. Symbolic entropy can reflect the degree of complexity of nonlinear signal simply and reliably. In this paper, we propose a new method to symbolize the EEG signal, namely difference symbolization, by which we can analyze the characteristics of dynamics on the tangent space of observation data. Furthermore, we compare and analyze the value of symbolic entropy by choosing difference symbolization parameter. The comparative analyses on different physiological state of EEG data sets show that this method can clearly distinguish the EEGs' complexity between normal and epilepsy, between eyes open and eyes closed, and so on. And it is of significance to the establishment of objective criteria for evaluation and fine quantitative analysis of EEG.
Algorithms ; Electroencephalography ; methods ; Entropy ; Humans ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted