Segmentation of cell images based on improved graph MST and skeleton distance mapping

Weixing, 王卫星 Wang; Li-ping, 田利平 Tian; Yue, 王悦 Wang

doi:10.3788/ope.20132109.2464

Cited by 4 publications

(2 citation statements)

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“…Currently, traditional methods such as active contour models, watershed algorithms and graph cut algorithms [2][3][4][5], which are usually based on simple features and thresholding strategies, cannot deal with pathological images with drastic variations of nuclei density, blurred boundaries, overlapping targets, mitosis nucleus and sources from different organs. In recent years, deep learning methods have been widely applied to medical image segmentation due to the ability to learn representations of medical images with multiple levels of abstraction [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of nucleus based on dynamic convolution and deep features of stain distribution

Zhou,

Feng,

Chen

2023

IET Image Processing

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Analysis of the pathology image is important to diagnose cancer of lung, breast and stomach. Segmenting the nucleus is a key step for quantitative analysis, and has significance to the pathology researches and computer aided diagnosis systems. The inconsistency of colour, fuzzy boundary of nucleus and overlapping of cells are the universally acknowledged challenges. To solve these problems, the difference between the inside and outside of nucleus is enhanced by obtaining the distribution of the haematoxylin based on Lambert–Beer's law and the optical characteristics of stains. An inferior encoder, which is supervised by the inferior decoder, is proposed to extract the deep features of the distribution of stains. And these features are fed into the primary encoder to improve the accuracy of segmentation. To relieve the problem that some nuclei are segmented as background because the deep feature is inapparent, dynamic convolution is introduced into the encoders. The experiments show that the proposed model can segment the nucleus in the pathological images more precisely than the compared models. The Dice similarity coefficient (DSC) and panoptic quality (PQ) are 0.810 and 0.512, respectively.

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Section: Introductionmentioning

confidence: 99%

Segmentation of nucleus based on dynamic convolution and deep features of stain distribution

Zhou,

Feng,

Chen

2023

IET Image Processing

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“…Zhang et al [11] proposed an image segmentation method based on watershed and graph theory. Wang et al [12] adopted a new image segmentation algorithm based on graph theory and mathematical morphology. Fabijanska et al [13] used an improved algorithm based on minimum spanning tree, which can increase the speed of image segmentation by reducing the number of vertices in the graph.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Fuzzy and Touching Cells Based on Modified Minimum Spanning Tree and Concave Point Detection

Wang¹,

Lin²

2016

Recent Advances in Image and Video Coding

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In order to segment fuzzy and touching cell images accurately, an improved algorithm is proposed based on minimum spanning tree (MST) and concave point detection. First, the cell images are smoothed and enhanced by a Gaussian filter. Then, the improved minimum spanning tree algorithm is used to segment the cell images. The MST algorithm is modified from three aspects, namely, weight function of edges, difference function of internal and inter region, and threshold function and parameter k. Furthermore, the problem of cell touching is solved by means of concave point detection. According to the rugged topography of touching cells, the concave points are found from the concave regions in the touching cell images, which are used to find the separation points quickly and accurately. Experimental results indicate that the new algorithm is ideal and effective.

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Red Blood Cell Detection Using Improved Mask R-CNN

Pan,

Su,

Chen

et al. 2024

Lecture Notes in Computer Science

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Segmentation of cell images based on improved graph MST and skeleton distance mapping

Cited by 4 publications

References 0 publications

Segmentation of nucleus based on dynamic convolution and deep features of stain distribution

Segmentation of nucleus based on dynamic convolution and deep features of stain distribution

Segmentation of Fuzzy and Touching Cells Based on Modified Minimum Spanning Tree and Concave Point Detection

Red Blood Cell Detection Using Improved Mask R-CNN

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