An algorithm for overlapping chromosome segmentation based on region selection

Liu, Xiangbin; Wang, Sisi; Lin, Jerry Chun‐Wei; Liu, Shuai

doi:10.1007/s00521-022-07317-y

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…Image processing refers to the use of computers to analyze images. Common image processing includes image denoising, image segmentation, and feature extraction (Liu et al, 2022d ). Image noise appears in the process of acquiring and transmitting EMs images (Gonzalez and Woods, 2002 ).…”

Section: Introductionmentioning

confidence: 99%

EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation

Yang

Zhao

et al. 2023

Front. Microbiol.

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Nowadays, the detection of environmental microorganism indicators is essential for us to assess the degree of pollution, but the traditional detection methods consume a lot of manpower and material resources. Therefore, it is necessary for us to make microbial data sets to be used in artificial intelligence. The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set that is applied in the field of multi-object detection of artificial intelligence. This method reduces the chemicals, manpower and equipment used in the process of detecting microorganisms. EMDS-7 including the original Environmental Microorganism (EM) images and the corresponding object labeling files in “.XML” format file. The EMDS-7 data set consists of 41 types of EMs, which has a total of 2,65 images and 13,216 labeled objects. The EMDS-7 database mainly focuses on the object detection. In order to prove the effectiveness of EMDS-7, we select the most commonly used deep learning methods (Faster-Region Convolutional Neural Network (Faster-RCNN), YOLOv3, YOLOv4, SSD, and RetinaNet) and evaluation indices for testing and evaluation. EMDS-7 is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571.

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Section: Introductionmentioning

confidence: 99%

EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation

Yang

Zhao

et al. 2023

Front. Microbiol.

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“…These approaches for data processing, such as collecting data, randomized image training, and semi-supervised learning, are divided into three primary categories; model-building techniques such as architectural design and result fusing, as well as process-optimization techniques such as heating learning and multi-task learning. Liu et al [25] used a heuristic approach to find a mathematical and geometric solution to this issue in order to enhance the segment of overlapping chromosomes. The issues that arise and its solutions are provided as graphically depicted interpretable image features starting with chromosomal images, which assists in a better comprehension of the process.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Wavelet-Based Attention Network for Semantic Segmentation of MRI Brain Tumor

Anusooya

Bharathiraja

Mahdal

et al. 2023

Sensors

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To determine the appropriate treatment plan for patients, radiologists must reliably detect brain tumors. Despite the fact that manual segmentation involves a great deal of knowledge and ability, it may sometimes be inaccurate. By evaluating the size, location, structure, and grade of the tumor, automatic tumor segmentation in MRI images aids in a more thorough analysis of pathological conditions. Due to the intensity differences in MRI images, gliomas may spread out, have low contrast, and are therefore difficult to detect. As a result, segmenting brain tumors is a challenging process. In the past, several methods for segmenting brain tumors in MRI scans were created. However, because of their susceptibility to noise and distortions, the usefulness of these approaches is limited. Self-Supervised Wavele- based Attention Network (SSW-AN), a new attention module with adjustable self-supervised activation functions and dynamic weights, is what we suggest as a way to collect global context information. In particular, this network’s input and labels are made up of four parameters produced by the two-dimensional (2D) Wavelet transform, which makes the training process simpler by neatly segmenting the data into low-frequency and high-frequency channels. To be more precise, we make use of the channel attention and spatial attention modules of the self-supervised attention block (SSAB). As a result, this method may more easily zero in on crucial underlying channels and spatial patterns. The suggested SSW-AN has been shown to outperform the current state-of-the-art algorithms in medical image segmentation tasks, with more accuracy, more promising dependability, and less unnecessary redundancy.

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“…The basis of mathematical morphology is the algebra of nonlinear operators for object shape, which is superior to the linear algebraic system based on convolution in restoring details and smoothing edges [10][11]. The purpose of morphological operation is to enhance the object structure and segment the object from the background.…”

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confidence: 99%

Evaluation Model of Telemedicine Service Quality Based on Machine Sensing Vision

Cao

Xie

et al. 2022

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: At present, the common telemedicine service quality evaluation methods can not obtain the key evaluation indicators, which leads to the low accuracy and low user satisfaction. OBJECTIVES: This paper constructs a telemedicine service quality evaluation model based on machine vision technology. METHODS: Machine vision technology is used to obtain telemedicine service information, preliminarily select service quality assessment indicators, complete the selection of indicators, build a telemedicine service quality assessment indicator system, adopt subjective and objective combination method to calculate the weight of service quality assessment indicators, and combine matter element analysis method to build a telemedicine service quality assessment model. RESULTS: The experimental results show that the Cronhach a is higher than 0.7, the Barthel index is higher than 90, and the satisfaction of many users is more than 90%. CONCLUSION: The proposed method solves the problems existing in the current method and lays a foundation for the development of telemedicine service technology.

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An algorithm for overlapping chromosome segmentation based on region selection

Cited by 13 publications

References 11 publications

EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation

EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation

Self-Supervised Wavelet-Based Attention Network for Semantic Segmentation of MRI Brain Tumor

Evaluation Model of Telemedicine Service Quality Based on Machine Sensing Vision

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