ABSTRACT
As the most popular simplified model of the optical imaging system, the acquisition of the Gaussian point spread function (PSF) parameter is one of the hotspots and key points on which people do research in the field of image restoration. Based on the idea by which there exists deterministic mathematical relationship between Gaussian OTF feature points as well as its parameter and the frequency representation of the image in an existed literature, we proposed an automatic, accurate, stable, and improved approach. This method is able to give prominence to the related calculation feature by a Gaussian convolution and degeneration operation and finally realize the automatic esti mation of PSF parameter of a microscopic image. Experiments have proved that a good restoration result can be achieved utilizing the estimated PSF by the present method, which is of considerable application and reference value in restoration of other sorts with Gaussian approximate PSF model or 3D microscopic image restoration.
Subject(s)
Humans , Algorithms , Image Interpretation, Computer-Assisted , Imaging, Three-Dimensional , Microscopy , Models, Theoretical , Normal DistributionABSTRACT
To solve the problem of traditional K-means clustering in which initial clustering centers are selected randomly, we proposed a new K-means segmentation algorithm based on robustly selecting 'peaks' standing for White Matter, Gray Matter and Cerebrospinal Fluid in multi-peaks gray histogram of MRI brain image. The new algorithm takes gray value of selected histogram 'peaks' as the initial K-means clustering center and can segment the MRI brain image into three parts of tissue more effectively, accurately, steadily and successfully. Massive experiments have proved that the proposed algorithm can overcome many shortcomings caused by traditional K-means clustering method such as low efficiency, veracity, robustness and time consuming. The histogram 'peak' selecting idea of the proposed segmentootion method is of more universal availability.
Subject(s)
Humans , Algorithms , Brain , Cluster Analysis , Magnetic Resonance Imaging , NeuroimagingABSTRACT
With an un-calibrated camera a new method is proposed in this paper to measure dynamically the bending angle of knee-joint based on cross ratio invariability. Firstly, by video image processing technique, we detected 25 circular objectives distributed in the aided measurement tool and determined their geometric centers in the video frame respectively. Then we further computed the extending direction of thigh and shank by detecting position of two rectangular targets tied on them. Finally, using of the principle of cross ratio invariability in Projection Geometry, we could realize real-time dynamic measurement of the bending angle of knee-joint based on the method. Experiments have proved that the measurement precision of the introduced approach could satisfy basic requirements of clinical applications.