Nucleus segmentation technique for acute Leukemia

Halim, Nurul Hazwani Abd; Mashor, Mohd Yusoff; Nasir, Aimi Salihah Abdul; Mokhtar, N.; Hassan, Rosline

doi:10.1109/cspa.2011.5759871

Cited by 34 publications

(21 citation statements)

References 5 publications

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“…Segmentation of an image refers to the separation of regions with similar characteristics. In relation to the blood cell images, the complexity of cellular nature and overlapping between the cells, segmentation remains a difficult task [1].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Acute Lymphoblastic Leukaemia based on HSI Colour Space

Vyavahare¹,

Thool²

2012

Fourth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom2012)

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Image segmentation is the most important step and critical task in image processing. The term leukaemia refers to a group of cancer blood cells which occur when the cells stop working and functioning properly and affect blood forming (Stem) cells in the body. In leukaemia, white blood cells become abnormal, divide and grow in an uncontrolled way. Early detection of the disease is necessary; it aims at aiding the prognosis of the disease through image analysis. It primarily involves noise identification and removal, enhancement and segmentation of blood orbone marrow smear images and analysis of processed images. After effective noise removal, depending upon clarity of image, suitable enhancement technique is employed. Then the image is converted into appropriate colour space and segmentation algorithm is applied to segregate cells from background. This allows the identification of abnormal WBCs. Then feature extraction and analysis of the WBCs was done.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection of Acute Lymphoblastic Leukaemia based on HSI Colour Space

Vyavahare¹,

Thool²

2012

Fourth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom2012)

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“…According to [9], Lab color space is used for segmentation process. Also, algorithm Based on HSI color space is proposed in [10]. Based on HSV color space, segmentation technique [11] for ALL images is proposed.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis Using RGB and HSV Color Spaces

ElDahshan

Youssef

Masameer

et al. 2015

JBEMi

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Image segmentation process is considered the most essential step in image analysis especially in the medical field. In this paper, the color segmentation for acute lymphoblastic leukemia images (ALL) is applied to segment each leukemia image into two clearly defined regions: blasts and background. The ALL segmentation process is based on two different color spaces: RGB color space and HSV color space. The comparison performance between the segmentation methods based on RGB and HSV color spaces are investigated to find the best method to segment the acute lymphoblastic leukemia images. The experimental results show that the segmentation of ALL images based on HSV color space yield better accuracy than RGB color space when compared with the manual segmentation image made by medical experts. Using HSV color space, the shape of blasts in ALL blood samples is closely preserved with segmentation accuracy over 99.00%. However, segmentation based HSV color space was chosen as it produced the highest ALL segmentation rate.

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“…The algorithm consists of preprocessing; processing and post processing these main steps were agreed upon among many researchers in the field [24-25, and 28]. The developed approach is based on the Panel selection, segmentation using K-means clustering, and refinement process succeeds to detect abnormal white blood cells (Blast) [29][30][31]. Pre-processing is a critical step.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Automatic Segmentation of Acute Leukemia Cells

Kandil¹,

Hassan²

2016

IJCA

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The recognition of the acute Leukemia blast cells in colored microscopic images is a challenging task. Segmentation is the essential step for image analysis and image processing. In this paper, an algorithm is presented that consists of panel selection followed by segmentation using K-means clustering then a refinement process. This algorithm was applied on public dataset designed for testing segmentation techniques. The results were compared with two different segmentation techniques developed by other researchers on the same data set. Our algorithm results in a sensitivity of 97.4 % and specificity of 98.1%. The developed algorithm was tested to another dataset of samples extracted from patients in local hospitals. The algorithm results in sensitivity of 100%, Specificity of 99.747% and accuracy of 99.7617%. The results were approved by expert pathologists. General TermsPattern Recognition, Image processing and segmentation.

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Nucleus segmentation technique for acute Leukemia

Cited by 34 publications

References 5 publications

Automatic Detection of Acute Lymphoblastic Leukaemia based on HSI Colour Space

Automatic Detection of Acute Lymphoblastic Leukaemia based on HSI Colour Space

Comparison of Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis Using RGB and HSV Color Spaces

Automatic Segmentation of Acute Leukemia Cells

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