Studies On Recognition Of Cell Images And Initial Application Based On Invariant Moments

The cell recognition and automatic classification and counting of leucocytes is one of the most important research topics in clinical medicine, and how to get an effective image descriptor is the crucial problem for realizing the automatic identification.Two original image databases, the database for 7 kinds of leucocytes and the database for peritoneal macrophages of mice, were established in advance in this study. The first one was made up 134 leucocytes with different microscopic morphological characteristics, and the second one was comprised 128 peritoneal macrophages swallowed different number of yeast fungus. The training sample set was established by using 500 different versions of cell images which were artificially treated by gradation, rotation and adding background noise. And then Pseudo-Jacobi(p=4,q=3)-Fourier Moments (PJFM's) was used to describe different microscopic characteristics of cells in the training sample set, and the Invariant Moments Database for these cells were also established.In order to prove the digitally descriptive, anti-distortion and anti-noise properties of the Moments, the microscopic characteristics of leucocytes were theoretically normalized to multi-distortion of scale, intensity, rotation and shift when N=M=10, and the microscopic characteristic images of cells were also reconstructed in our study with or without background noise. Then, 90 leucocytes in experimental sample set, comprised cells of original images and artificially treated versions and newly prepared smears, were initially identified by using the minimum-mean-distance rule. For further confirm the properties of digitalization and application of the this Moment in the medical field, the invariant moments database for microscopic characteristic images of peritoneal macrophage were set up, and semi-automatic classification and counting for 300 macrophages swallowed different number of yeast fungus from 25 pieces of typical visual field was carried out by using the minimum-mean-distance rule.The experimental results showed that the invariant moment values for different kinds of leucocytes were significant difference and the invariant moment values for different versions of same leucocytes were nearly identical after their microscopic images were normalized by PJFM's. The reconstructed images of leucocytes with background noise indicated that the main information of original images was recovered by finite number of invariant moment values, and the average recognition rate was reached 98.3%. The accurate average classified counting rate of peritoneal macrophages was 97%. Therefore, PJFM's possesses strong properties of digitalization, anti-multidistortion and anti-noise for microscopic characteristics of different cells, and can be used for feature selection and extraction of microstructure characteristics in microscopic identification, and classification and counting of cells, and also will make a good contribution to improve the efficiency of clinical testing.