Automatic Segmentation on Cell Image Fusing Gray and Gradient Information

2007 ◽  
Author(s):  
Boqiang Liu ◽  
Cong Yin ◽  
Zhongguo Liu ◽  
Yanyan Zhang
Keyword(s):  
Automatic Segmentation ◽  
Cell Image ◽  
Gradient Information

scholarly journals A thresholding method for automatic cell image segmentation.

1979 ◽  
Vol 27 (1) ◽  
pp. 180-187 ◽  
Author(s):  
H Borst ◽  
W Abmayr ◽  
P Gais
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Second Step ◽  
Original Image ◽  
Digital Implementation ◽  
Thresholding Method ◽  
Cell Image

An algorithm for automatic segmentation of PAP-stained cell images and its digital implementation is described. First, the image is filtered in order to eliminate the granularily and small objects in the image which may upset the segmentation procedure. In a second step, information on gradient and compactness is extracted from the filtered image and stored in three histograms as functions of the extinction. From these histograms, two extinction thresholds are computed. These thresholds are suitable to separate the nucleus from the cytoplasm, and the cytoplasm from the background in the filtered image. Masks are determined in this way, and finally used to analyse the nucleus and the cytoplasm in the original image.

Automatic segmentation algorithm for breast cell image based on multi-scale CNN and CSS corner detection

2020 ◽  
Vol 24 (3) ◽  
pp. 195-203
Author(s):  
Haoyang Tang ◽  
Cong Song ◽  
Meng Qian
Keyword(s):  
Automatic Segmentation ◽  
Scale Space ◽  
Segmentation Algorithm ◽  
Corner Detection ◽  
Adherent Cells ◽  
Multi Scale ◽  
Cell Image ◽  
Curvature Scale Space ◽  
Segmentation Accuracy ◽  
Initial Segmentation

As the shapes of breast cell are diverse and there is adherent between cells, fast and accurate segmentation for breast cell remains a challenging task. In this paper, an automatic segmentation algorithm for breast cell image is proposed, which focuses on the segmentation of adherent cells. First of all, breast cell image enhancement is carried out by the staining regularization. Then, the cells and background are separated by Multi-scale Convolutional Neural Network (CNN) to obtain the initial segmentation results. Finally, the Curvature Scale Space (CSS) corner detection is used to segment adherent cells. Experimental results show that the proposed algorithm can achieve 93.01% accuracy, 93.93% sensitivity and 95.69% specificity. Compared with other segmentation algorithms of breast cell, the proposed algorithm can not only solve the difficulty of segmenting adherent cells, but also improve the segmentation accuracy of adherent cells.

Study on Method to Dissected Data of Wood Cell Image

2010 ◽  
Vol 139-141 ◽  
pp. 303-307 ◽  
Author(s):  
Shao Qun Zhang ◽  
Wei Xu ◽  
Zhao Xin Meng
Keyword(s):  
Automatic Segmentation ◽  
Anatomical Structure ◽  
Cell Image ◽  
Processing Theory ◽  
Manchurian Ash ◽  
Characteristics Of Image ◽  
Binarization Algorithm ◽  
Edge Tracing ◽  
Theory Base

Wood cell images are observed by the In-Situ SEM plays a very important role in wood structure. Because of the nature of cell image, automatic segmentation for wood cell image becomes one difficult question. According to the characteristics of image of anatomical structure of wood based on image processing theory, the theory and method of binarization algorithm for image of anatomical structure of wood is presented. The machine vision detecting of edge tracing of wood cell is processed to the binarized wood image. Use this method to dissected Yunnan ormosia and Manchurian ash image of anatomical structure of wood cell, this study provides the theory base for feature abstraction and pattern recognization for the further study on image of anatomical structure of wood

Prediction of lymph node metastase in NSCLC

2010 ◽  
Vol 01 (05) ◽  
pp. 219-226 ◽  
Author(s):  
F. Beyer ◽  
B. Buerke ◽  
J. Gerss ◽  
K. Scheffe ◽  
M. Puesken ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Fdg Pet ◽  
Automatic Segmentation ◽  
Correlation Coefficients ◽  
Mediastinal Lymph Nodes ◽  
Intra Class Correlation ◽  
Axial Diameter ◽  
Pet Ct ◽  
Fdg Pet Ct

SummaryPurpose: To distinguish between benign and malignant mediastinal lymph nodes in patients with NSCLC by comparing 2D and semiautomated 3D measurements in FDG-PET-CT.Patients, material, methods: FDG-PET-CT was performed in 46 patients prior to therapy. 299 mediastinal lymph-nodes were evaluated independently by two radiologists, both manually and by semi-automatic segmentation software. Longest-axial-diameter (LAD), shortest-axial-diameter (SAD), maximal-3D-diameter, elongation and volume were obtained. FDG-PET-CT and clinical/FDG-PET-CT follow up examinations and/or histology served as the reference standard. Statistical analysis encompassed intra-class-correlation-coefficients and receiver-operator-characteristics-curves (ROC). Results: The standard of reference revealed involvement in 87 (29%) of 299 lymph nodes. Manually and semi-automatically measured 2D parameters (LAD and SAD) showed a good correlation with mean

Comparative Analysis and Automatic Segmentation of PCG Signal Using Wavelets Transform

2015 ◽  
Vol 3 (3) ◽  
pp. 24-29
Author(s):  
Lekram Premlal Bahekar ◽  
◽  
Deepali Shende ◽  
Simran Kaur Digwa ◽  
◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Automatic Segmentation ◽  
Wavelets Transform

Carpal Bone Segmentation Using Fully Convolutional Neural Network

2019 ◽  
Vol 15 (10) ◽  
pp. 983-989
Author(s):  
Liang Kim Meng ◽  
Azira Khalil ◽  
Muhamad Hanif Ahmad Nizar ◽  
Maryam Kamarun Nisham ◽  
Belinda Pingguan-Murphy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Bone Age ◽  
Image Feature ◽  
Qualitative Assessment ◽  
Carpal Bone ◽  
Radiographic Images ◽  
Accurate Quantitative Analysis

Background: Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delineation of the carpal bone and accurate quantitative analysis. Methods: In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8. Results and Conclusion: The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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