Capture Largest Included Circles: An Approach for Counting Red Blood Cells

Complete Blood Count (CBC) is a standard medical test that can help diagnose various conditions and diseases. Manual counting of blood cells is highly tedious and time consuming. However, new methods for counting blood cells are customary employing both electronic and computer-assisted techniques. Image segmentation is a classical task in most image processing applications which can be used to count blood cells in a microscopic image. In this research work, we have employed a few existing segmentation techniques, and also proposed a new scheme to count total blood cells in a smear microscopic image. The proposed technique, called Capture Largest Included Circles (CLIC), is a parameterized segmentation algorithm that captures largest possible circles in an object boundary. The algorithm is perfectly suited for appliance in counting blood cells because of high circularity ratio of cells. Comparative study of segmentation by CLIC and a few other state-of-the-art segmentation algorithms such as Distance Regularized Level Set Evolution (DRLSE), Watershed segmentation and Pixcavator (a topology-based segmentation) is also part of this research work. Results have proven the superiority of CLIC over other schemes, especially in case of diseased red blood cells.