Title :
Fuzzy Based Blood Image Segmentation for Automated Leukemia Detection
Author :
Mohapatra, Subrajeet ; Samanta, Sushanta Shekhar ; Patra, Dipti ; Satpathi, Sanghamitra
Author_Institution :
IPCV Lab., Nat. Inst. of Technol., Rourkela, India
Abstract :
Acute lymphoblastic leukemia (ALL) are a group of hematological neoplasia of childhood which is characterized by a large number of lymphoid blasts in the blood stream. ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3-7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of stained blood smear or bone marrow aspirate is the only way to effective diagnosis of leukemia. Techniques such as fluorescence in situ hybridization (FISH), immunophenotyping, cytogenetic analysis and cytochemistry are also employed for specific leukemia detection. The need for automation of leukemia detection arises since the above specific tests are time consuming and costly. Morphological analysis of blood slides are influenced by factors such as hematologists experience and tiredness, resulting in non standardized reports. A low cost and efficient solution is to use image analysis for quantitative examination of stained blood microscopic images for leukemia detection. A fuzzy based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia. In the present paper two novel shape features i.e., Hausdorff Dimension and contour signature is implemented for classifying a lymphocytic cell nucleus. Support Vector Machine (SVM) is employed for classification. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.
Keywords :
blood; cancer; cellular biophysics; feature extraction; fuzzy systems; image classification; image segmentation; image texture; medical image processing; shape recognition; support vector machines; Hausdorff dimension; SVM; WBC; automated leukemia detection; blood cells; blood slides; contour signature; feature extraction; fuzzy based blood image segmentation; hematologist; image classification; image color analysis; image shape; image texture; lymphoblastic leukemia; lymphocytic cell nucleus; lymphoid blasts; morphological analysis; stained blood microscopic images; support vector machine; Blood; Feature extraction; Image color analysis; Image segmentation; Pixel; Shape; Support vector machines;
Conference_Titel :
Devices and Communications (ICDeCom), 2011 International Conference on
Conference_Location :
Mesra
Print_ISBN :
978-1-4244-9189-6
DOI :
10.1109/ICDECOM.2011.5738491