Blood Cell Detection and Counting Using Convolutional Sparse Dictionary Learning

Abstract

Blood count identification has been analyzed for all types of blood which are useful in clinical purpose. During early 1950, automatic complete blood count equipment was developed. In order to treat disease like anaemia, leukaemia and various other diseases, counting of blood cells from a blood cell image is very important. Manual identification, location and overall count of red blood cells are time consuming and are very complex and difficult process. Hence automatic analysis will be very useful where segmentation is the important and crucial process which needs to be taken care. A convolutional sparse dictionary learning and coding approach for detecting and counting instances of a repeated object in a holographic lens-free image has been proposed.