Microscopic image segmentation using hybrid technique for dengue prediction

Abstract

An application of hybrid soft computing technique for early detection and treatment of a most common mosquito-borne viral disease Dengue, is discussed thoroughly in this chapter. The global pictures of dengue endemics are shown clearly. The structure of dengue virus and the infection procedure of the virus are also discussed. A detailed analysis of dengue illness, diagnosis methods, and treatments has been done to conclude that platelet counting is needful for early diagnosis of Dengue illness and for monitoring the health status of the patients. The main challenge in developing an automated platelet counting system for efficient, easy, and fast detection of dengue infection as well as treatment, is in the segmentation of platelets from microscopic images of a blood smear. This chapter shows how the challenges can be overcome. Color-based segmentation and k-means clustering cannot provide desired outputs in all possible situations. A hybrid soft computing technique efficiently segments platelet and overcomes the shortcomings of the other two segmentation techniques. This technique is the combination of fuzzy c-means technique and adaptive network-based fuzzy interference system (ANFIS). We have applied three different segmentation techniques namely color-based segmentation, k-means, and the hybrid soft computing technique on poor intensity images. However, only the hybrid soft computing technique detects the platelets correctly.