Detection of Cancer Cells Using Microscopic Images of Blood Sample

Identification of Blood disorders is practiced by visualization of the blood sample through a microscope by the naked eye of a human. In this project a computerized technique has been developed to help the doctor in identifying different types of Leukemia. Initially the RGB image is converted to L*a*b colour space and is segmented using K-Mean clustering. To this clustered image the features are extracted and is classified into different types of leukemia. The required code is developed using MATLAB. A graphical user interface has been developed for better understanding of the procedure. This technique is used to identify the diseases and diagnose them at an early stage. Images are used as inputs, as they are cheap and do not need any kind of expensive testing nor lab equipment’s. The project will be using features in the microscopic images and examine any kind of changes on color, texture, geometry and statistical analysis of the images. The changes that are found in these features will be used as our classifier input.