Colour image enhancement techniques for acute Leukaemia blood cell morphological features

Salihah, A.N. Aimi; Mashor, Mohd Yusoff; Harun, Nor Hazlyna; Hassan, Rosline

doi:10.1109/icsmc.2010.5641867

Cited by 20 publications

(15 citation statements)

References 7 publications

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“…Due to the complex nature of blood cells and overlapping between these cells, segmenting them remains a challenging task [7]. Many algorithms for segmentation have been developed for color images that produce more information of the scene than grayscale images do [8].…”

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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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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“…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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“…Due to the complex nature of blood cells and overlapping between these cells, segmenting them remains a challenging task [9]. Many algorithms for segmentation have been developed for color images that produce more information of the scene than grayscale images do [10]. For leukemia segmentation process, transformations of original RGB images to different color spaces such as (HSI, HSV, YUV, XYZ, Lab…etc.)…”

Section: Introductionmentioning

confidence: 99%

An Efficient Implementation of Acute Lymphoblastic Leukemia Images segmentation on the FPGA

ElDahshan

Youssef

Masameer

et al. 2015

AIVP

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In the medical field, image segmentation process is considered the most essential step in image analysis. In this work, 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 hue channel (H) of HSV color space as a method in segmentation of WBC from its complicated background. This work presents an efficient framework for segmentation of ALL images on a reconfigurable logic platform using Simulink, MATLAB and Xilinx System Generator (XSG). This segmentation framework is implemented on a FPGA using basic Xilinx Blockset to minimize hardware resources and lower execution time to be suitable enough for medical applications. It is designed using XSG as DSP design tool that enables the use of Simulink models, implemented in VHDL and synthesized for three different Xilinx FPGA boards.

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Colour image enhancement techniques for acute Leukaemia blood cell morphological features

Cited by 20 publications

References 7 publications

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

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

An Efficient Implementation of Acute Lymphoblastic Leukemia Images segmentation on the FPGA

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