Towards automated diagnosis of celiac disease by computer-assisted classification of duodenal imagery

2008 ◽  
Author(s):  
A. Vecsei ◽  
T. Fuhrmann ◽  
A. Uhl
Keyword(s):  
Celiac Disease ◽  
Computer Assisted ◽  
Automated Diagnosis

scholarly journals On Computer-assisted Classification of Coupled Integrable Equations

2002 ◽  
Vol 33 (5) ◽  
pp. 647-660 ◽  
Author(s):  
Mikhail V. Foursov ◽  
Marc Moreno Maza
Keyword(s):  
Computer Assisted ◽  
Integrable Equations

scholarly journals Maintainability Evaluation of Object-Oriented Software System Using Clustering Techniques

2013 ◽  
Vol 5 (2) ◽  
pp. 136-143 ◽  
Author(s):  
Astha Mehra ◽  
Sanjay Kumar Dubey
Keyword(s):  
Clustering Algorithms ◽  
Daily Basis ◽  
Computer Assisted ◽  
Program Execution ◽  
Huge Data ◽  
Large Databases ◽  
Input Dataset ◽  
Data Objects

In today’s world data is produced every day at a phenomenal rate and we are required to store this ever growing data on almost daily basis. Even though our ability to store this huge data has grown but the problem lies when users expect sophisticated information from this data. This can be achieved by uncovering the hidden information from the raw data, which is the purpose of data mining.  Data mining or knowledge discovery is the computer-assisted process of digging through and analyzing enormous set of data and then extracting the meaning out of it. The raw and unlabeled data present in large databases can be classified initially in an unsupervised manner by making use of cluster analysis. Clustering analysis is the process of finding the groups of objects such that the objects in a group will be similar to one another and dissimilar from the objects in other groups. These groups are known as clusters.  In other words, clustering is the process of organizing the data objects in groups whose members have some similarity among them. Some of the applications of clustering are in marketing -finding group of customers with similar behavior, biology- classification of plants and animals given their features, data analysis, and earthquake study -observe earthquake epicenter to identify dangerous zones, WWW -document classification, etc. The results or outcome and efficiency of clustering process is generally identified though various clustering algorithms. The aim of this research paper is to compare two important clustering algorithms namely centroid based K-means and X-means. The performance of the algorithms is evaluated in different program execution on the same input dataset. The performance of these algorithms is analyzed and compared on the basis of quality of clustering outputs, number of iterations and cut-off factors.

scholarly journals Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3337
Author(s):  
Francesco Bianconi ◽  
Jakob N. Kather ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Color Constancy ◽  
Local Binary Patterns ◽  
Histological Evaluation ◽  
Computer Assisted ◽  
Automated Classification ◽  
Negative Effects ◽  
Processing Methods ◽  
Color Deconvolution ◽  
Quantitative Basis

Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature—for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers—specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns.

scholarly journals Digital Image Analysis of Picrosirius Red Staining: A Robust Method for Multi-Organ Fibrosis Quantification and Characterization

Biomolecules ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1585
Author(s):  
Guillaume E. Courtoy ◽  
Isabelle Leclercq ◽  
Antoine Froidure ◽  
Guglielmo Schiano ◽  
Johann Morelle ◽  
...  
Keyword(s):  
Digital Method ◽  
Computer Assisted ◽  
Kidney Fibrosis ◽  
Collagen Accumulation ◽  
Organ Specific ◽  
Picrosirius Red ◽  
Lung And Kidney ◽  
Organ Fibrosis ◽  
Accurate Quantification

Current understanding of fibrosis remains incomplete despite the increasing burden of related diseases. Preclinical models are used to dissect the pathogenesis and dynamics of fibrosis, and to evaluate anti-fibrotic therapies. These studies require objective and accurate measurements of fibrosis. Existing histological quantification methods are operator-dependent, organ-specific, and/or need advanced equipment. Therefore, we developed a robust, minimally operator-dependent, and tissue-transposable digital method for fibrosis quantification. The proposed method involves a novel algorithm for more specific and more sensitive detection of collagen fibers stained by picrosirius red (PSR), a computer-assisted segmentation of histological structures, and a new automated morphological classification of fibers according to their compactness. The new algorithm proved more accurate than classical filtering using principal color component (red-green-blue; RGB) for PSR detection. We applied this new method on established mouse models of liver, lung, and kidney fibrosis and demonstrated its validity by evidencing topological collagen accumulation in relevant histological compartments. Our data also showed an overall accumulation of compact fibers concomitant with worsening fibrosis and evidenced topological changes in fiber compactness proper to each model. In conclusion, we describe here a robust digital method for fibrosis analysis allowing accurate quantification, pattern recognition, and multi-organ comparisons useful to understand fibrosis dynamics.

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