Leukemia, a subdivision of cancer, develops in human blood and the bone marrow. The reason behind is the expeditious and sudden formation and accumulation of WBCs in blood. Identification & diagnosis of these types of abnormalities by humans is difficult and may lead to misidentification. Therefore an automatic system for the identification and classification would be of great help. This paper aims at proposing a technique for correct and quick classification of leukemia images and categorizing them into their respective types. For this, different features are extracted from the input images and then based on these features a data set for the input images is created. This data set is then utilized as input data to a neural network for training purposes. This neural network is designed and created to categorize the images according to their corresponding leukemia type.
During the unfolding measures that are taken for the purpose of leukemia detection, segmentation of blood cells is a vital step.In this paper two approaches of such segmentation technique is proposed.While one uses K-means clustering, other uses color image based segmentation method.Both the processes segment the image into two regions, blasts & backgrounds.These blasts are our area of interest.The performance measure is based on the comparison of the two proposed techniques tends to find the more suitable approach for correct leukemia image segmentation.The results show that the segmentation based on K-means clustering gives better results preserving important information and removing background noise.
Image segmentation commonly known as partitioning of an image is one of the intrinsic parts of any image processing technique. In this image processing step, the digital image of choice is segregated into sets of pixels on the basis of some predefined and preselected measures or standards. There have been presented many algorithms for segmenting a digital image. This paper presents a general review of algorithms that have been presented for the purpose of image segmentation.
