Urinary Sediment Overlapping Cells Image Segmentation Based on Combination Strategy

Shi, Zhongke; Jinlong, Q.; Guangjie, Qi

doi:10.1109/iscid.2008.21

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…First contrast of RGB image is increased using 'imadjust' command in MATLAB 13 using suitable contrast limit that shown in figure (2). Then image is converted to gray color (G).…”

Section: Methodsmentioning

confidence: 99%

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A Novel Approach to Detect Chronic Leukemia using Shape based Feature Extraction and Identification with Digital Image Processing

Vaghela¹,

Modi²,

Pandya³

et al. 2016

IJAIS

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In this paper, some shape based features like area, perimeter, roundness, standard deviation etc. are used to recognize different types of white blood cells like monocyte, lymphocytes, eosinophil, basophil, neutrophils etc. Using image processing techniques, result can be obtained within 3-4 minute. To perform shape base features operation, contrast of RGB image has to be increased for better detection of white cells. After recognition of each and every cell, classification is performed to detect either it is CML (Chronic Myelogenous Leukemia) or CLL (chronic Lymphocytic leukemia). This algorithm is performed on 30 images. Out of 30, it is successful on 28 images. So it gives accuracy of 93.33%.

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“…First contrast of RGB image is increased using 'imadjust' command in MATLAB 13 using suitable contrast limit that shown in figure (2). Then image is converted to gray color (G).…”

Section: Methodsmentioning

confidence: 99%

“…Here segmentation method is used to target foreground object [1,2,3,4]. Lin Yanyu et al suggest the method for edge detection.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Detect Chronic Leukemia using Shape based Feature Extraction and Identification with Digital Image Processing

Vaghela¹,

Modi²,

Pandya³

et al. 2016

IJAIS

View full text Add to dashboard Cite

show abstract

“…The existing available methods for the touching cells splitting can be categorized into binary erosion methods and mathematical morphological based methods [1] , watershed-based techniques [2,3,4,5] , model-based approaches [6,7] and concavity analysis [8,9] .In view of the problems in the existing algorithm above, Nilanjan [10] proposed a segmentation algorithm based on bottleneck rules. It apply bottleneck rule to find a pair of points for splitting, then find the split line by prior conditions.…”

Section: Introductionmentioning

confidence: 99%

An Improved Touching-Cell Splitting Algorithm Based on Bottleneck Detection

Yan

Zhao

et al. 2013

AMM

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In terms of the phenomenon that red cells and white cells incline to touch each other in urinary sediment images, an improved touching-cell splitting algorithm is proposed in the paper that is based on the bottleneck detection The proposed algorithm is featured by positioning the bottleneck points by the rule of bottleneck, also featured by determining to split a region or not by the criteria of segmentation conditions. Once a splitting is done, the algorithm turn to apply the bottleneck detection again to those regions that are already been segmented. Simulation and experiment show that the performance of the improved algorithm is accurate and stable in splitting ranging from two touched cells to multiple touched circumstances; the enhanced robustness and universality can be enough to prove the practicability of the algorithm a certain level.

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Improved Extraction of Objects from Urine Microscopy Images with Unsupervised Thresholding and Supervised U-net Techniques

Aziz¹,

Pande²,

Cheluvaraju³

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Urinary Sediment Overlapping Cells Image Segmentation Based on Combination Strategy

Cited by 4 publications

References 5 publications

A Novel Approach to Detect Chronic Leukemia using Shape based Feature Extraction and Identification with Digital Image Processing

A Novel Approach to Detect Chronic Leukemia using Shape based Feature Extraction and Identification with Digital Image Processing

An Improved Touching-Cell Splitting Algorithm Based on Bottleneck Detection

Improved Extraction of Objects from Urine Microscopy Images with Unsupervised Thresholding and Supervised U-net Techniques

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