White Blood Cell Segmentation Research Articles

A method based on multispectral imaging technique for White Blood Cell segmentation

Aiming at the problem of image analysis of White Blood Cells in bone marrow microscopic images, multispectral imaging techniques with spectral calibration method was proposed to acquire device-independent images, which is almost impossible in conventional color imaging method. For image segmentation, Support Vector Machine (SVM) was applied directly to the spectrum of each pixel, and using sequential minimal optimization (SMO) algorithm for feature selection to reduce the time of training SVM classifier. Mass of experiments showed that the method is robust, effective and insensitive to smear staining and illumination condition.

A novel white blood cell segmentation scheme based on feature space clustering

This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods.

Snakes on the watershed

We present a new approach for object boundary extraction, called the watersnake. It is a two-step snake algorithm whose energy functional is minimized by the dynamic programming method. It is more robust to local minima because it finds the solution by searching the entire energy space. To reduce the complexity of the minimization process, the watershed transformation and a coarse-to-fine strategy are used. The new technique is compared to standard methods for accuracy in synthetic data and is applied to segmentation of white blood cells in bone marrow images.