Image segmentation and classification of white blood cells with the extreme learning machine and the fast relevance vector machine

Abstract

White blood cells (WBCs) or leukocytes are an important part of the body's defense against infectious organisms and foreign substances. WBC segmentation is a challenging issue because of the morphological diversity of WBCs and the complex and uncertain background of blood smear images. The standard ELM classification techniques are used for WBC segmentation. The generalization performance of the ELM classifier has not achieved the maximum nearest accuracy of image segmentation. This paper gives a novel technique for WBC detection based on the fast relevance vector machine (Fast-RVM). Firstly, astonishingly sparse relevance vectors (RVs) are obtained while fitting the histogram by RVM. Next, the relevant required threshold value is directly sifted from these limited RVs. Finally, the entire connective WBC regions are segmented from the original image. The proposed method successfully works for WBC detection, and effectively reduces the effects brought about by illumination and staining. To achieve the maximum accuracy of the RVM classifier, we design a search for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. Therefore, this proposed RVM method effectively works for WBC detection, and effectively reduces the computational time and preserves the images.