Morphology Preserving Segmentation Method for Occluded Cell Nuclei from Medical Microscopy Image

2021 ◽  
pp. 2250019
Author(s):  
Rafflesia Khan ◽  
Rameswar Debnath
Keyword(s):  
Prior Knowledge ◽  
Single Cells ◽  
Region Of Interest ◽  
Automatic Measurement ◽  
Cell Counting ◽  
Manual Annotation ◽  
Cell Nuclei ◽  
Image Region ◽  
Microscopy Image ◽  
Optimal Approach

Nowadays, image segmentation techniques are being used in many medical applications such as tissue culture monitoring, cell counting, automatic measurement of organs, etc., for assisting doctors. However, high-level segmentation results cannot be obtained without manual annotation or prior knowledge for high variability, noise and other imaging artifacts in medical images. Furthermore, unstable and continuously changing characteristics of all human cells, tissues and organs manipulate training-based segmentation methods. Detecting appropriate contour of a region of interest and single cells from overlapping condition are extremely challenging. In this paper, we aim for a model that can detect biological structure (e.g. cell nuclei and lung contour) with their proper morphology even in overlapping or occluded condition without manual annotation or prior knowledge. We have introduced a new optimal approach for automatic medical image region segmentation. The method first clearly focuses the boundaries of all object regions in a microscopy image. Then it detects the areas by following their contours. Our model is capable of detecting and segmenting object regions from medial image using less computation effort. Our experimental results prove that our model provides better detection on several datasets of different types of medical data and ensures more than 98% segmentation rate in the case of densely connected regions.

scholarly journals A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

2021 ◽  
Vol 11 (9) ◽  
pp. 4091
Author(s):  
Débora N. Diniz ◽  
Mariana T. Rezende ◽  
Andrea G. C. Bianchi ◽  
Claudia M. Carneiro ◽  
Daniela M. Ushizima ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Pap Smear ◽  
Hierarchical Classification ◽  
Cell Nuclei ◽  
Test Analysis ◽  
Pap Smear Test ◽  
Microscopy Image ◽  
Feature Based ◽  
Cervical Cells ◽  
Number Of Classes

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.

scholarly journals Microscopy cell nuclei segmentation with enhanced U-Net

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Feixiao Long
Keyword(s):  
Data Science ◽  
State Of The Art ◽  
Cell Nuclei ◽  
Nuclei Segmentation ◽  
Microscopy Image ◽  
Segmentation Algorithms ◽  
Bright Field Microscopy ◽  
The Cost ◽  
Advanced Computing ◽  
Microscopy Image Analysis

Abstract Background Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing. Results An enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch is proposed to potentially work with low-resources computing. Through strictly controlled experiments, the average IOU and precision of U-Net+ predictions are confirmed to outperform other prevalent competing methods with 1.0% to 3.0% gain on the first stage test set of 2018 Kaggle Data Science Bowl cell nuclei segmentation contest with shorter inference time. Conclusions Our results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing.

scholarly journals Development of a Microfluidic GHz Impedance Cytometer

2013 ◽  
Vol 80 (12) ◽  
Author(s):  
Niels Haandbæk ◽  
Sebastian C. Bürgel ◽  
Flavio Heer ◽  
Andreas Hierlemann
Keyword(s):  
Dielectric Properties ◽  
Chinese Hamster Ovary ◽  
Cho Cells ◽  
Single Cells ◽  
Chinese Hamster ◽  
Frequency Range ◽  
Cell Nuclei ◽  
Dielectric Characterization ◽  
The Difference

AbstractThis article presents a novel microfluidic impedance cytometer enabling dielectric characterization of single cells at frequencies up to 500 MHz. The dielectric properties of cells at lower frequencies contain information about their size and membrane capacitance. The increased frequency range of the presented cytometer potentially allows for characterization of intracellular components, such as vacuoles or the cell nuclei. We demonstrate the overall capabilities of the cytometer through discrimination of polystyrene beads from Chinese hamster ovary (CHO) cells. The discrimination is based on the difference in dielectric properties at frequencies up to 500 MHz.

Intelligent Computer Aided Detection of Tumor in MRI Brain Images Using Cascaded Correlation Neural Network Classifier

2014 ◽  
Vol 626 ◽  
pp. 65-71
Author(s):  
V. Amsaveni ◽  
N. Albert Singh ◽  
J. Dheeba
Keyword(s):  
Neural Network ◽  
Region Of Interest ◽  
Texture Features ◽  
Neural Network Classifier ◽  
Image Region ◽  
Brain Images ◽  
Gabor Features ◽  
Computer Aided ◽  
Pixel Location ◽  
Nonlinear Classifier

In this paper, a Computer aided classification approach using Cascaded Correlation Neural Network for detection of brain tumor from MRI is proposed. Cascaded Correlation Neural Network is a nonlinear classifier which is formulated as a supervised learning problem and the classifier was applied to determine at each pixel location in the MRI if the tumor is present or not. Gabor texture features are taken from the image Region of interest (ROI). The extracted Gabor features from MRI is given as input to the proposed classifier. The method was applied to real time images from the collected from diagnostic centers. Based on the analysis the performance of the proposed cascaded correlation neural network classifier is superior when compared with other classification approaches.

Non-rigid multi-frame registration of cell nuclei in live cell fluorescence microscopy image data

2015 ◽  
Vol 19 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Marco Tektonidis ◽  
Il-Han Kim ◽  
Yi-Chun M. Chen ◽  
Roland Eils ◽  
David L. Spector ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Image Data ◽  
Live Cell ◽  
Cell Nuclei ◽  
Microscopy Image

Quick Registration Method Based on Image Region Correlation

2014 ◽  
Vol 608-609 ◽  
pp. 555-558
Author(s):  
Na Zhu
Keyword(s):  
Monitoring System ◽  
High Speed ◽  
Vision System ◽  
Automatic Measurement ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Image Region ◽  
Registration Method ◽  
Implementation Effect ◽  
Accurate Matching

Binocular vision system can be widely used in CNC machine tools chatter monitoring, due to its simple system and automatic measurement function. Traditional registration method cannot balance the contradiction between precision and speed of registration; restrict its application in high speed monitoring system. So based on traditional feature point registration method, it proposes a new method to obtain more accurate matching feature points by using complexity distribution feature of image region to determine the distribution of feature region and the bidirectional similarity and triangle similar method, which realize quick registration. From the simulation and implementation effect perspective, this method is feasible for the image registration in high-speed monitoring system.

scholarly journals Eye-tracking and learning experience: gaze trajectories to better understand the behavior of memorial visitors

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Salma Mesmoudi ◽  
Stanislas Hommet ◽  
Denis Peschanski
Keyword(s):  
Focus Group ◽  
Eye Tracking ◽  
Learning Experience ◽  
Region Of Interest ◽  
Viewing Time ◽  
Image Region ◽  
Mass Violence ◽  
The Gaze ◽  
Before And After ◽  
First Results

Eye-tracking technology is increasingly introduced in museums to assess their role in learning and knowledge transfer. However, their use provide limited quantitative and/or qualitative measures such as viewing time and/or gaze trajectory on an isolated object or image (Region of Interest "ROI"). The aim of this work is to evaluate the potential of the mobile eye-tracking to quantify the students’ experience and behaviors through their visit of the "Genocide and mass violence" area of the Caen memorial. In this study, we collected eye-tracking data from 17 students during their visit to the memorial. In addition, all visitors filled out a questionnaire before the visit, and a focus group was conducted before and after the visit. The first results of this study allowed us to analyze the viewing time spent by each visitor in front of 19-selected ROIs, and some of their specific sub-parts. The other important result was the reconstruction of the gaze trajectory through these ROIs. Our global trajectory approach allowed to complete the information obtained from an isolated ROI, and to identify some behaviors such as avoidance. Clustering analysis revealed some typical trajectories performed by specific sub-groups. The eye-tracking results were consolidated by the participants' answers during the focus group.  

Superpixel-Based HDR Image Region of Interest Extraction Method

2020 ◽  
Vol 47 (9) ◽  
pp. 793-803
Author(s):  
Simon Suh ◽  
Seung-Ryeol Ohk ◽  
Young-Jin Kim
Keyword(s):  
Extraction Method ◽  
Region Of Interest ◽  
Image Region ◽  
Hdr Image
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