A non-iterative clustering based soft segmentation approach for a class of fuzzy images

Wang, Zhenzhou; Yang, Yongming

doi:10.1016/j.asoc.2017.05.025

Cited by 11 publications

(2 citation statements)

References 25 publications

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“…To solve the challenging problem of cell segmenta-tion, several approaches have been utilized in the literature. Clustering-based approaches such as expectation maximization (EM) [1], [2], K-means method [3], [4], the fuzzy Cmeans method [5], type-2 fuzzy logic [6], thresholding-based approach [7], edge detection based method [8], shape-based matching method [9], machine learning [10], or energy minimization [11], Gram-Schmidt orthogonalization [12], combining several image processing techniques such as thresholding, k-means clustering, and modified watershed algorithm [13], morphological operators [14], etc. to mention a few.…”

Section: Introductionmentioning

confidence: 99%

Unified Approach for White Blood Cell Segmentation, Feature Extraction, and Counting using Max-Tree Data Structure

Ferdosi¹

2020

IJACSA

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Accurate identification and counting of White Blood Cells (WBCs) from microscopy blood cell images are vital for several blood-related disease diagnoses such as leukemia. The inevitability of automated cell image analysis in medical diagnosis results in a plethora of research for the last few decades. Microscopic blood cell image analysis involves three major steps: cell segmentation, classification, and counting. Several techniques have been employed separately to solve these three problems. In this paper, a simple unified model is proposed for White Blood Cell segmentation, feature extraction for classification, and counting with connected mathematical morphological operators implemented using the max-tree data structure. Max-tree creates a hierarchical representation of connected components of all possible gray levels present in an image in such a way that the root holds the connected components comprise of pixels with the lowest intensity value and the connected components comprise of pixels with the highest intensity value are in the leaves. Any associated attributes such as the size or shape of each connected component can be efficiently calculated on the fly and stored in this data structure. Utilizing this knowledge-rich data structure, we obtain a better segmentation of the cells that preserves the morphology of the cells and consequently obtain better accuracy in cell counting.

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Section: Introductionmentioning

confidence: 99%

Unified Approach for White Blood Cell Segmentation, Feature Extraction, and Counting using Max-Tree Data Structure

Ferdosi¹

2020

IJACSA

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“…Image segmentation is a one of fundamental techniques in compute vision [1]. Many image segmentation algorithms have been proposed in the past few years [2], [3]. Superpixels method is a kind of image segmentation technique, which are usually used as a pre-processing step for computer vision applications.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Superpixels for Polarimetric SAR Images Classification

Guo

Jiao

Wang

et al. 2018

IEEE Trans. Fuzzy Syst.

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Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application. In the last few years, superpixels are widely used in polarimetric synthetic aperture radar image classification. However, no superpixel algorithm is especially designed for image classification. It is believed that both mixed superpixels and pure superpixels exist in an image. Nevertheless, mixed superpixels have negative effects on classification accuracy. Thus, it is necessary to generate superpixels containing as few mixed superpixels as possible for image classification. In this paper, first, a novel superpixels concept, named fuzzy-superpixels, is proposed for reducing the generation of mixed superpixels. In fuzzy-superpixels, not all pixels are assigned to a corresponding superpixel. We would rather ignore the pixels than assign them to improper superpixels. Second, a new algorithm is proposed to generate fuzzy-superpixels for PolSAR image classification. Three PolSAR images are used to verify the effect of the proposed fuzzy superpixels algorithm. Experimental results demonstrate the superiority of the proposed fuzzy-superpixels algorithm over several state-of-the-art superpixels algorithms.

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Introduction

Singh

2023

Biomedical Image Analysis

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A non-iterative clustering based soft segmentation approach for a class of fuzzy images

Cited by 11 publications

References 25 publications

Unified Approach for White Blood Cell Segmentation, Feature Extraction, and Counting using Max-Tree Data Structure

Unified Approach for White Blood Cell Segmentation, Feature Extraction, and Counting using Max-Tree Data Structure

Fuzzy Superpixels for Polarimetric SAR Images Classification

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