Recent Advances in Automated Chromosome Image Analysis

2009 ◽  
pp. 307-319
Author(s):  
Petros S. Karvelis ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Image Analysis ◽  
Multispectral Imaging ◽  
Digital Images ◽  
Imaging Techniques ◽  
Automatic Segmentation ◽  
Chromosome Analysis ◽  
Color Information ◽  
Human Cytogenetics ◽  
Improved Methods

Automated chromosome analysis is now becoming routine in most human cytogenetics laboratories. It involves both processing and analysis of digital images and has been developed because of the demandby cytogeneticists. Over the years, many techniques have been introduced for the automatic segmentation and classification of chromosome images, of which only a few are included in the available commercial systems. Today, advances in chromosome imaging techniques, especially in multispectral imaging, lead the way for the development of new and improved methods for the location, segmentation and classification of chromosome images by exploiting the color information. In this chapter the authors describe methods which have been already developed for automated chromosome analysis.

scholarly journals Automated Segmentation and Object Classification of CT Images: Application toIn VivoMolecular Imaging of Avian Embryos

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Alexander Heidrich ◽  
Jana Schmidt ◽  
Johannes Zimmermann ◽  
Hans Peter Saluz
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Bone Growth ◽  
Imaging Techniques ◽  
Long Bone ◽  
Automated Segmentation ◽  
Chick Embryos ◽  
In Ovo

Background. Although chick embryogenesis has been studied extensively, there has been growing interest in the investigation of skeletogenesis. In addition to improved poultry health and minimized economic loss, a greater understanding of skeletal abnormalities can also have implications for human medicine. Truein vivostudies require noninvasive imaging techniques such as high-resolution microCT. However, the manual analysis of acquired images is both time consuming and subjective.Methods. We have developed a system for automated image segmentation that entails object-based image analysis followed by the classification of the extracted image objects. For image segmentation, a rule set was developed using Definiens image analysis software. The classification engine was implemented using the WEKA machine learning tool.Results. Our system reduces analysis time and observer bias while maintaining high accuracy. Applying the system to the quantification of long bone growth has allowed us to present the first truein ovodata for bone length growth recorded in the same chick embryos.Conclusions. The procedures developed represent an innovative approach for the automated segmentation, classification, quantification, and visualization of microCT images. MicroCT offers the possibility of performing longitudinal studies and thereby provides unique insights into the morpho- and embryogenesis of live chick embryos.

scholarly journals AUTOMATIC SEGMENTATION AND CLASSIFICATION OF CELLS FROM BRONCHO ALVEOLAR LAVAGE

2011 ◽  
Vol 26 (3) ◽  
pp. 111 ◽  
Author(s):  
Olivier Lezoray ◽  
Michel Lecluse
Keyword(s):  
Image Analysis ◽  
Automatic Segmentation ◽  
Image Analysis Technique ◽  
Analysis Technique ◽  
Analysis Scheme ◽  
Ranking Score ◽  
Blue Stain ◽  
Cellular Components ◽  
2D Histogram

Broncho alveolar lavage is the most commonly used diagnostic tool for confirming alveolar hemorrhage. Golde has introduced a ranking score, based on the hemosiderin content of macrophages which enables ranking cells from 0 to 4 based on the degree of Prussian blue stain. We propose a complete image analysis scheme to automatically perform both the extraction of the cellular objects and the ranking of each cell according to the Golde score. The image analysis techniques used mainly involve clustering and mathematical morphology. A 2D histogram is clustered to extract the main cellular components, a color watershed is used to determine and refine the regions. Finally, the cellular components of interest are firstly classified according to their hue and secondly according to their staining repartition. The proposed image analysis technique is very fast and produces reliable and accurate results.

scholarly journals Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Gamal ElMasry ◽  
Nasser Mandour ◽  
Marie-Hélène Wagner ◽  
Didier Demilly ◽  
Jerome Verdier ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vigna Unguiculata ◽  
Multispectral Imaging ◽  
Imaging Techniques

scholarly journals Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activation

2003 ◽  
Vol 57A (1) ◽  
pp. 22-33 ◽  
Author(s):  
Joakim Lindblad ◽  
Carolina Wählby ◽  
Ewert Bengtsson ◽  
Alla Zaltsman
Keyword(s):  
Image Analysis ◽  
Automatic Segmentation

Colour image analysis for the classification of turkey breast meat according to cooking yield

2003 ◽  
Vol 23 (1) ◽  
pp. 124-127
Author(s):  
Isabel Sebastáan ◽  
V Santé ◽  
G Le Pottier ◽  
Pascale Marty-Mahé ◽  
P Loisel ◽  
...  
Keyword(s):  
Image Analysis ◽  
Breast Meat ◽  
Colour Image ◽  
Cooking Yield

scholarly journals Classification of total carotene and quality of chili pepper (Capsicum frutescens) based on image analysis

2021 ◽  
Vol 733 (1) ◽  
pp. 012005
Author(s):  
Y Hendrawan ◽  
R Utami ◽  
D Y Nurseta ◽  
Daisy ◽  
S Nuryani ◽  
...  
Keyword(s):  
Image Analysis ◽  
Chili Pepper ◽  
Capsicum Frutescens

scholarly journals Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Image Analysis ◽  
Short Term Memory ◽  
Disease Diagnosis ◽  
Short Term ◽  
Term Memory ◽  
Time Frequency ◽  
Time Space ◽  
Histopathological Images ◽  
Long Short Term Memory

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

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