A METHOD OF LEUKOCYTE SEGMENTATION BASED ON S COMPONENT AND B COMPONENT IMAGES

A leukocyte segmentation method based on S component and B component images is proposed.Threshold segmentation operation is applied to get two binary images in S component and B component images.The samples used in this study are peripheral blood smears.It is easy tofind from the two binary images that gray values are the same at every corresponding pixels in theleukocyte cytoplasm region,but opposite in the other regions.The feature shows that "IMAGEAND"operation can be employed on the two binary images to segment the cytoplasm region ofleukocyte.By doing"IMAGE XOR"operation between cytoplasn region and nucleus region,theleukocyte segment ation can be retrieved effectively.The segmentation accuracy is evaluated by comparing the segmentation result of the proposed method with the manual segmentation by ahematologist.Experiment results show that the proposed method is of a higher segmentationaccuracy and it also performs well when leukocytes overlap_with erythrocytes.The averagesegmentation accuracy of the proposed method reaches 97.7%for segmenting five types ofleukocyte.Good segmentation results provide an important foundation for leukocytes aut omaticrecognition.

Encryption and Decryption of Color Images through Random Disruption of Rows and Columns

In modern society,information is becoming increasingly interconnected through networks,and the rapid development of information technology has caused people to pay more attention to the encryption and the protection of information.Image encryption technology is a key technology for ensuring the security performance of images.We extracted single channel RGB component images from a color image using MATLAB programs,encrypted and decrypted the color images by randomly disrupting rows,columns and regions of the image.Combined with histograms and the visual judgments of encryption images,it is shown that the information of the original image cannot be obtained from the encryption image easily.The results show that the color-image encryptions with the algorithm we used have good effect and fast operation speed.Thus this algorithm has certain practical value.

Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition

An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis （PCA） and multiple discriminant analysis （MDA） is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection （ORICS） plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.