Mammogram Mining Using Genetic Ant-Miner

Data Mining ◽  
2013 ◽  
pp. 775-792
Author(s):  
K. Thangavel ◽  
R. Roselin
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Image Analysis ◽  
Comparative Analysis ◽  
Image Data ◽  
Experimental Results ◽  
Biomedical Image Analysis ◽  
Image Mining ◽  
Image Domain ◽  
Biomedical Image

Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. It is an extension of data mining to image domain and an interdisciplinary endeavour. This chapter focuses on mammogram classification using genetic Ant-Miner. The key idea is to generate classifier for classifying mammograms as normal or abnormal using the proposed Genetic Ant-Miner algorithm. The Genetic Algorithm has been employed to optimize some of the ant parameters. A comparative analysis is performed in order to achieve the efficiency of the proposed algorithm. Further, the experimental results reveals that the improvement of the proposed Genetic Ant-Miner in the domain of Biomedical image Analysis.

Mammogram Mining Using Genetic Ant-Miner

2011 ◽  
pp. 232-250
Author(s):  
K. Thangavel ◽  
R. Roselin
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Image Analysis ◽  
Comparative Analysis ◽  
Image Data ◽  
Experimental Results ◽  
Biomedical Image Analysis ◽  
Image Mining ◽  
Image Domain ◽  
Biomedical Image

Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. It is an extension of data mining to image domain and an interdisciplinary endeavour. This chapter focuses on mammogram classification using genetic Ant-Miner. The key idea is to generate classifier for classifying mammograms as normal or abnormal using the proposed Genetic Ant-Miner algorithm. The Genetic Algorithm has been employed to optimize some of the ant parameters. A comparative analysis is performed in order to achieve the efficiency of the proposed algorithm. Further, the experimental results reveals that the improvement of the proposed Genetic Ant-Miner in the domain of Biomedical image Analysis.

scholarly journals Development of a biomedical image analysis framework, based on web services

2015 ◽  
Author(s):  
Θεοδόσιος Γούδας
Keyword(s):  
Image Analysis ◽  
Web Services ◽  
Ground Truth ◽  
Biomedical Image Analysis ◽  
Image Mining ◽  
Analysis Framework ◽  
Analysis Techniques ◽  
Biomedical Image ◽  
Log Likelihood

Η εξόρυξη γνώσης από βιοϊατρικές εικόνες είναι µια πολύπλοκη και χρονοβόρα διαδικασία, γιατί απαιτούνται πολλά βήµατα, όπως η ρύθµιση του χρώµατος, το φιλτράρισµα της εικόνας, η τµηµατοποίησή της, η εξαγωγή χαρακτηριστικών γνωρισµάτων, ο χαρακτηρισµός της, κ.λπ. Κάθε ένα από αυτά τα βήµατα απαιτεί κατάλληλη βαθµονόµηση, ώστε συνολικά να πετύχουν το βέλτιστο αποτέλεσµα. Αυτή η διδακτορική διατριβή, εστιάζει στην ανάπτυξη ενός πλαισίου, το οποίο περιέχει τις απαραίτητες τεχνικές εξόρυξης και ανάλυσης εικόνας, οργανωµένες σε οντότητες, για την επίλυση των σύνθετων βιοϊατρικών προβληµάτων ανάλυσης εικόνας. Το προτεινόµενο πλαίσιο επιτρέπει το σχεδιασµό διαγραµµάτων ροών εργασίας, για να επιλύσει αυτά τα προβλήµατα. Επιπλέον, παρέχει τη λειτουργία της αυτόµατης δηµιουργίας παράλληλων πολλαπλών εκδόσεων (multiple parallel instances) του διαγράµµατος ροής εργασίας που σχεδιάστηκε, πραγµατοποιώντας όλους τους πιθανούς συνδυασµούς των τελεστών που προστέθηκαν στο διάγραµµα, για να επιλεγεί αυτόµατα ο βέλτιστος συνδυασµός ροής εργασίας. Για την υλοποίηση αυτού του πλαισίου αξιοποιήθηκε η τεχνολογία υπηρεσιών δικτύου (web services technology), σε συνδυασµό µε τη µοντελοποίηση τεχνικών ανάλυσης και εξόρυξης εικόνας (image mining and analysis techniques), σε ανεξάρτητες οντότητες. Η επιλογή της βέλτιστης ροής εργασίας πραγµατοποιείται, είτε συγκρίνοντας τα αποτελέσµατα της τµηµατοποίησης µε την πραγµατικότητα (Ground Truth), είτε µε τη χρήση του µέτρου της απόστασης log-likelihood των οµαδοποιηµένων εντοπισµένων αντικειµένων (clustered salient objects). Για τη χρήση αυτού του πλαισίου απαιτούνται βασικές – και ίσως σε κάποιες περιπτώσεις προχωρηµένες – γνώσεις ανάλυσης εικόνας, αλλά δεν απαιτούνται προγραµµατιστικές γνώσεις. Αυτό το πλαίσιο µπορεί να ενσωµατωθεί στο πρόγραµµα διαχείρισης ροών εργασίας TAVERNA ή σε οποιαδήποτε άλλη παρόµοια πλατφόρµα. Επιπλέον, σε αυτήν τη διδακτορική διατριβή, παρουσιάζονται όλα τα βιοϊατρικά προβλήµατα ανάλυσης εικόνας που εξετάσθηκαν κατά τη διάρκεια της εκπόνησής της. Καθεµιά από τις προτεινόµενες προσεγγίσεις εξόρυξης εικόνας, χρησιµοποιεί το προτεινόµενο πλαίσιο. Όλες οι προσεγγίσεις εξήγαγαν ικανοποιητικά αποτελέσµατα, χρησιµοποιώντας τις δυνατότητες του προτεινόµενου πλαισίου. Επιπλέον, ορισµένα πρόσθετα σενάρια εξόρυξης εικόνας µοντελοποιήθηκαν στο προτεινόµενο πλαίσιο, αποδεικνύοντας την αποδοτική λειτουργία της εύρεσης της βέλτιστης ροής εργασίας.

scholarly journals A Feature Set for Cytometry on Digitized Microscopic Images

2003 ◽  
Vol 25 (1) ◽  
pp. 1-36 ◽  
Author(s):  
Karsten Rodenacker ◽  
Ewert Bengtsson
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Systematic Approach ◽  
Structural Features ◽  
Biomedical Image Analysis ◽  
Feature Sets ◽  
Biomedical Image ◽  
Rules Of Thumb ◽  
Quantitative Cytology ◽  
The Eu

Feature extraction is a crucial step in most cytometry studies. In this paper a systematic approach to feature extraction is presented. The feature sets that have been developed and used for quantitative cytology at the Laboratory for Biomedical Image Analysis of the GSF as well as at the Center for Image Analysis in Uppsala over the last 25 years are described and illustrated. The feature sets described are divided into morphometric, densitometric, textural and structural features. The latter group is used to describe the eu‐ and hetero‐chromatin in a way complementing the textural methods. The main goal of the paper is to bring attention to the need of a common and well defined description of features used in cyto‐ and histometrical studies. The application of the sets of features is shown in an overview of projects from different fields. Finally some rules of thumb for the design of studies in this field are proposed. Colour figures can be viewed onhttp://www.esacp.org/acp/2003/25‐1/rodenacker.htm.

Intelligent Computing in Medical Imaging

2021 ◽  
pp. 592-608
Author(s):  
Shouvik Chakraborty ◽  
Sankhadeep Chatterjee ◽  
Amira S. Ashour ◽  
Kalyani Mali ◽  
Nilanjan Dey
Keyword(s):  
Image Analysis ◽  
Medical Imaging ◽  
Biomedical Imaging ◽  
Clinical Analysis ◽  
Biological Species ◽  
Imaging Modalities ◽  
Biomedical Image Analysis ◽  
Accurate Analysis ◽  
Intelligent Computing ◽  
Biomedical Image

Biomedical imaging is considered main procedure to acquire valuable physical information about the human body and some other biological species. It produces specialized images of different parts of the biological species for clinical analysis. It assimilates various specialized domains including nuclear medicine, radiological imaging, Positron emission tomography (PET), and microscopy. From the early discovery of X-rays, progress in biomedical imaging continued resulting in highly sophisticated medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, Computed Tomography (CT), and lungs monitoring. These biomedical imaging techniques assist physicians for faster and accurate analysis and treatment. The present chapter discussed the impact of intelligent computing methods for biomedical image analysis and healthcare. Different Artificial Intelligence (AI) based automated biomedical image analysis are considered. Different approaches are discussed including the AI ability to resolve various medical imaging problems. It also introduced the popular AI procedures that employed to solve some special problems in medicine. Artificial Neural Network (ANN) and support vector machine (SVM) are active to classify different types of images from various imaging modalities. Different diagnostic analysis, such as mammogram analysis, MRI brain image analysis, CT images, PET images, and bone/retinal analysis using ANN, feed-forward back propagation ANN, probabilistic ANN, and extreme learning machine continuously. Various optimization techniques of ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and other bio-inspired procedures are also frequently conducted for feature extraction/selection and classification. The advantages and disadvantages of some AI approaches are discussed in the present chapter along with some suggested future research perspectives.

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