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

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.

scholarly journals Comparison of feature based segmentation of full polarimetric SAR satellite sea ice images with manually drawn ice charts

2013 ◽  
Vol 7 (6) ◽  
pp. 1693-1705 ◽  
Author(s):  
M. -A. N. Moen ◽  
A. P. Doulgeris ◽  
S. N. Anfinsen ◽  
A. H. H. Renner ◽  
N. Hughes ◽  
...  
Keyword(s):  
Sea Ice ◽  
Visual Inspection ◽  
Automatic Segmentation ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Polarimetric Synthetic Aperture Radar ◽  
Feature Based ◽  
Number Of Classes ◽  
Future Work

Abstract. In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by available in situ measurements. The segmentation result was then compared to ice charts drawn by ice service analysts. The comparison revealed big discrepancies between the charts of the analysts, and between the manual and the automatic segmentations. In the succeeding analysis, the automatic segmentation chart was labeled into ice types by sea ice experts, and the SAR features used in the segmentation were interpreted in terms of physical sea ice properties. Utilizing polarimetric information in sea ice charting will increase the efficiency and exactness of the maps. The number of classes used in the segmentation has shown to be of significant importance. Thus, studies of automatic and robust estimation of the number of ice classes in SAR sea ice scenes will be highly relevant for future work.

scholarly journals Comparison of automatic segmentation of full polarimetric SAR sea ice images with manually drawn ice charts

2013 ◽  
Vol 7 (3) ◽  
pp. 2595-2634 ◽  
Author(s):  
M.-A. N. Moen ◽  
A. P. Doulgeris ◽  
S. N. Anfinsen ◽  
A. H. H. Renner ◽  
N. Hughes ◽  
...  
Keyword(s):  
Sea Ice ◽  
Visual Inspection ◽  
Automatic Segmentation ◽  
Synthetic Aperture ◽  
Segmentation Result ◽  
Sar Image ◽  
Polarimetric Synthetic Aperture Radar ◽  
Number Of Classes ◽  
Future Work

Abstract. In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by available in-situ measurements. The segmentation result was then compared to ice charts drawn by ice service analysts. The comparison revealed big discrepancies between the charts of the analysts, and between the manual and the automatic segmentations. In the succeeding analysis, the automatic segmentation chart was labeled into ice types by sea ice experts, and the SAR features used in the segmentation were interpreted in terms of physical sea ice properties. Studies of automatic and robust estimation of the number of ice classes in SAR sea ice scenes will be highly relevant for future work.

scholarly journals Multiplex RNA single molecule FISH of inducible mRNAs in single yeast cells

2019 ◽  
Author(s):  
Guoliang Li ◽  
Gregor Neuert
Keyword(s):  
Single Cell ◽  
Single Molecule ◽  
Transcription Initiation ◽  
Yeast Cells ◽  
Cell Image ◽  
Rna Export ◽  
Approaches To Study ◽  
Kinetic Expression ◽  
Accurate Quantification

AbstractTranscript levels powerfully influence cell behavior and phenotype and are carefully regulated at several steps. Recently developed single cell approaches such as RNA single molecule fluorescence in-situ hybridization (smFISH) have produced advances in our understanding of how these steps work within the cell. In comparison to single-cell sequencing, smFISH provides more accurate quantification of RNA levels. Additionally, transcript subcellular localization is directly visualized, enabling the analysis of transcription (initiation and elongation), RNA export and degradation. As part of our efforts to investigate how this type of analysis can generate improved models of gene expression, we used smFISH to quantify the kinetic expression of STL1 and CTT1 mRNAs in single Saccharomyces cerevisiae cells upon 0.2 and 0.4M NaCl osmotic stress. In this Data Descriptor, we outline our procedure along with our data in the form of raw images and processed mRNA counts. We discuss how these data can be used to develop single cell modelling approaches, to study fundamental processes in transcription regulation and develop single cell image processing approaches.

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.

scholarly journals Selected items of wheat variation - from palaeobotany to molecular biology

2014 ◽  
Vol 68 (2) ◽  
pp. 129-141 ◽  
Author(s):  
Romuald Kosina
Keyword(s):  
In Situ Hybridization ◽  
Molecular Biology ◽  
Ploidy Level ◽  
Genomic Dna ◽  
Spatial Arrangement ◽  
Anatomical Structure ◽  
Hybrid Necrosis ◽  
Tetraploid Wheats

The usefulness of data on ecotypes of wheat as well as of information about distribution of genes of hybrid necrosis for an interpretation of some questionable detections of fossil materials is emphasized. Variability of contemporary wheats is illustrated by means of morphology of lodicules, anatomical structure of caryopsis, morphology of embryo and features of epidermis of inflorescence bracts. These structures exhibit often a trend dependent on ploidy level. Discrimination of similar grains of fossil <em>Triticum compactum</em> and <em>T. sphaerococcum</em> is possible when traits of embryo are used. Wheat genomes are changed by numerous translocations and are spatially separated. This status may be detected by means of in situ hybridization of the genomic DNA. With such a spatial arrangement of the genomes the dominance of a caryopsis trait complex in hybrids between tetraploid wheats may be correlated. It may also create a part of new variation in wheat.

scholarly journals Klasifikasi Statistikal Tekstur Sel Pap Smear Dengan Decesion Tree

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Toni Arifin ◽  
Dwiza Riana ◽  
Gita Indah Hapsari
Keyword(s):  
Decision Tree ◽  
Carcinoma In Situ ◽  
Pap Smear ◽  
Image Data ◽  
Image Texture ◽  
Severe Dysplasia ◽  
Gray Level ◽  
Cell Image ◽  
Occurrence Matrix

ABSTRAK Penelitian ini menyajikan analisis tekstur dan klasifikasi citra sel pap smear. Pada analisis tekstur difokuskan pada citra nukleus sel Pap smear, metode yang digunakan adalah metode Gray Level Co-occurrence Matrix (GLCM) dengan menggunakan lima parameter yaitu korelasi, energi, homogenitas dan entropi ditambah dengan menghitung nilai Brightness pada citra yang diproses. Citra yang digunakan dalam penelitian ini menggunakan data citra Harlev, yang terdiri dari 280 citra yang sudah dikategorikan ke dalam 7 kelas yaitu 3 kelas sel normal yang meliputi Normal Superficial, Normal Intermediate, and Normal Columnar dan 4 kelas lainnya adalah kategori kelas citra sel abnormal yang meliputi Mild (Light) Dyplasia, Moderate Dysplasia, Severe Dysplasia dan Carcinoma In Situ. Berdasarkan hasil pengolahan citra yang menghasilkan nilai matriks dari setiap parameter yang dihitung, citra sel Pap smear akan diklasifikasikan menurut jenisnya normal atau abnormal dan berdasarkan kelasnya dengan menggunakan decision tree yang diolah dengan algoritma clasifier J48 pada aplikasi weka. Untuk akurasi yang dihasilkan dari klasifikasi sel normal dan abnormal adalah 73% dan untuk akurasi klasifikasi tujuh kelas adalah 34,3%. Kata Kunci : Klasifikasi, Statistikal Tekstur, Sel Pap Smear, Decision Tree. ABSTRACT This research presents the texture analysis and classification of cells pap smear image. Texture analysis focused on the cell nucleus Pap smear image, the research method used the Gray Level Co-occurrence Matrix (GLCM) method, by using five parameter that include contrast, correlation, energy, homogeneity, entropy and brightness. The image used in this research using image data Harlev. The images from 280 subjects are categorized into seven classes. Three classes of which are normal cell image class categories that include Normal Superficial, Normal Intermediate, and Normal Columnar, and the other four classes are categories of abnormal cell image class that include Mild (Light) Dyplasia, Moderate Dysplasia, Severe Dysplasia and Carcinoma In Situ. Based on the results of image processing that produces a matrix of values of each parameter were calculated, Pap smear cell image will be classified according to the type of normal or abnormal and based on the class using the decision tree treated with algorithm clasifier J48 in weka applications. To the resulting accuracy of the classification normal and abnormal cells is 73% and for seven class classification accuracy is 34,3%. Keywords : Classification, Statistical Texture, Cell Pap Smear, Decision Tree

Rendez vous with Saturn's rings

1984 ◽  
Vol 75 ◽  
pp. 743-759 ◽  
Author(s):  
Kerry T. Nock
Keyword(s):  
Solar Energy ◽  
Electric Propulsion ◽  
Power Source ◽  
Propulsion System ◽  
Low Thrust ◽  
Ring Plane ◽  
The Earth ◽  
Total Impulse ◽  
Saturn's Rings

ABSTRACTA mission to rendezvous with the rings of Saturn is studied with regard to science rationale and instrumentation and engineering feasibility and design. Future detailedin situexploration of the rings of Saturn will require spacecraft systems with enormous propulsive capability. NASA is currently studying the critical technologies for just such a system, called Nuclear Electric Propulsion (NEP). Electric propulsion is the only technology which can effectively provide the required total impulse for this demanding mission. Furthermore, the power source must be nuclear because the solar energy reaching Saturn is only 1% of that at the Earth. An important aspect of this mission is the ability of the low thrust propulsion system to continuously boost the spacecraft above the ring plane as it spirals in toward Saturn, thus enabling scientific measurements of ring particles from only a few kilometers.

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