Blood cancer cell classification based on geometric mean transform and dissimilarity metrics

Blood cancer is an umbrella term for cancers that affect the blood,bone marrow and lymphatic system. there are three main groups of blood cancer: leukemia,lymphoma and myeloma. some types are more common than others. in this paper,a new image transform based on geometric mean properties of integral values in both horizontal and vertical image directions is proposed for leukemia cancer cell classification. available classification methods using the classical feature extraction methods which are sensitive to rotation and deformation of the blood cells. the new transform is based on geometric mean projection,which -unlike other image transforms,such as radon transform- is not considered all signals in an image with the same signal acquisition rate. instead,it is general and thus applicable to all capturing signal functions to achieve sufficient invariant features. the geometric mean projection transforms (gmpt) guarantees that the detector only extracts the highly informative information from the object to achieve an invariant feature vector for an accurate classification process. this method has been used as cancer cell identification using microscopic imagery analysis in this study. dissimilarity metric calculation and shape analysis by using image transform has been used to extract the feature vectors of the imagery. then,the accumulated feature vectors have been classified to different classes by using artificial neural network (ann). the proposed technique has been evaluated in the standard images sourced from usim,malaysia. the evaluation results indicate the robustness of the technique in different types of images available in the dataset. © 2017 universiti putra malaysia press.