A novel framework for white blood cell segmentation based on stepwise rules and morphological features

Gim, Jawon; Park, Junoh; Lee, Ji-Hyeon; Ko, Byoung Chul; Nam, Jae-Yeal

doi:10.1117/12.876435

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jabar et al [105] compared the performance of three diferent types of clustering, classical K-means (CKM), FCM, and adaptive K-means (AKM) clustering for the segmentation of acute leukemia blood cells. They concluded that the AKM-clustering technique uses the values of Another active contour model, color gradient vector low (GVF) snake, is used for segmenting cells in the blood smear image [112]. The GVF snake is advantageous over other gradient-based ACMs because of their insensitivity to contour initialization and large capture region.…”

Section: Segmentationmentioning

confidence: 99%

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

et al. 2022

View full text Add to dashboard Cite

Leukemia, the cancer of blood-forming tissues, becomes fatal if not detected in the early stages. It is detected through a blood smear test that involves the morphological analysis of the stained blood slide. The manual microscopic examination of slides is tedious, time-consuming, error-prone, and subject to inter-observer and intra-observer bias. Several computerized methods to automate this task have been developed to alleviate these issues during the past few years. However, no exclusive comprehensive review of these methods has been presented to date. Such a review shall be highly beneficial for novice readers interested in pursuing research in this domain. This paper fills the void by presenting a comprehensive review of 149 papers detailing the methods used to analyze blood smear images and detect leukemia. The primary focus of the review is on presenting the underlying techniques used, their reported performance, along with their merits and demerits. It also enumerates the research issues that have been satisfactorily solved and open challenges still existing in the domain.

show abstract

Section: Segmentationmentioning

confidence: 99%

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The boundary edges and noise edges are then removed using a boundary removal scheme, while a GVF snake is forced to deform to the cytoplasm boundary edges. Therefore, the main contribution of this study is an improved segmentation accuracy of our previous study (Gim et al, 2010), regardless of the different types of WBCs, and reduced computational time based on cropped sub-images and the application of different segmentation schemes according to the cell part.…”

Section: Introductionmentioning

confidence: 97%

Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake

Gim

Nam

2011

Micron

Self Cite

110

View full text Add to dashboard Cite

Review on Image Segmentation Techniques Incorporated with Machine Learning in the Scrutinization of Leukemic Microscopic Stained Blood Smear Images

Umamaheswari

Geetha²

2019

Lecture Notes in Computational Vision and Biomechanics

View full text Add to dashboard Cite

A novel framework for white blood cell segmentation based on stepwise rules and morphological features

Cited by 5 publications

References 0 publications

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake

Review on Image Segmentation Techniques Incorporated with Machine Learning in the Scrutinization of Leukemic Microscopic Stained Blood Smear Images

Contact Info

Product

Resources

About