Proceedings of the 2011 International Conference on Communication, Computing &Amp; Security - ICCCS '11 2011
DOI: 10.1145/1947940.1947980
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Automated leukemia detection in blood microscopic images using statistical texture analysis

Abstract: Pathological image analysis plays a significant role in effective disease diagnostics. Quantitative microscopy has supplemented clinicians with accurate results for diagnosis of dreaded diseases such as leukemia, hepatitis, AIDS, psoriasis. In this paper we present a texture based approach for automated leukemia detection. Acute lymphocytic leukemia (ALL) is a malignant disease characterized by the accumulation of lymphoblast in the bone marrow. Texture features of the blood nucleus are investigated for diagno… Show more

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Cited by 28 publications
(15 citation statements)
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“…The contributions of this research include the following: In order to perform reliable diagnosis, the system considers both nucleus and cytoplasm in segmentation and feature extraction. This is different from related state-of-the-art applications in the literature which focused purely on nuclei for performing segmentation of WBCs and arriving at the resulting diagnosis 13 14 15 16 . The proposed SDM-based clustering takes both within- and between-cluster scatter variances into consideration.…”
mentioning
confidence: 77%
“…The contributions of this research include the following: In order to perform reliable diagnosis, the system considers both nucleus and cytoplasm in segmentation and feature extraction. This is different from related state-of-the-art applications in the literature which focused purely on nuclei for performing segmentation of WBCs and arriving at the resulting diagnosis 13 14 15 16 . The proposed SDM-based clustering takes both within- and between-cluster scatter variances into consideration.…”
mentioning
confidence: 77%
“…This algorithm is used to divide the image into K clusters by using n observations so that the objects in the same cluster are as close as possible and objects in the different cluster are different from other cluster's objects. This method has been used in the leukaemia blood image segmentation to extract the WBCs and lymphocytes from the image [ 35 , 36 ].…”
Section: Methodsmentioning
confidence: 99%
“…Partial analysis of blood microscopic images of leukemic patient have been addressed by multiple authors [7,11,14,25,26]; few examples of automated ALL detection systems that can extract and analyze leukocytes and discriminate lymphoblast (malignant leukocytes) from healthy leukocytes have been reported in the literature [24,27,28].…”
Section: Introductionmentioning
confidence: 99%
“…Mohapatra et al [28] improved an automatic ALL diagnosing system based on hematologist visual criteria. A shadow Cmeans-based segmentation algorithm is used to identify cytoplasm and nucleus of lymphocytes.…”
Section: Introductionmentioning
confidence: 99%