Acute Myelogenous Leukemia Detection in Blood Microscopic Images using Different Wavelet Family Techniques

Abstract

Image segmentation is considered the most critical step in image processing and helps to analyze, infer and make decisions especially in the medical field. Analyzing digital microscope images for earlier Acute Myelogenous leukemia (AML) diagnosis and treatment require sophisticated software and hardware systems. The need for automation of leukemia detection arises since current methods involve manual examination of the blood smear as the first step toward diagnosis. In this paper presents selected mathematical methods used for image segmentation and application of wavelet transform for the following segments classification by multiresolution decomposition of segments blood cell images. The Haar wavelets transform and Daubechies wavelet transform approaches has been adopted here and used for feature extraction allowing its use for image denoising and resolution enhancement as well. Feature classification is then achieved by self-organizing neural networks .A proposed method has been verified for simulated structures and then gives the better segmentation accuracy and Precision for analysis of microscopic images