Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/5164
Title: Improving the classification accuracy of computer aided diagnosis through multimodality breast imaging
Authors: Παγώνης, Νικόλαος
Κάβουρας, Διονύσης Α.
Σιδηρόπουλος, Κωνσταντίνος
Σακελλαρόπουλος, Γεώργιος Χ.
Νικηφορίδης, Γεώργιος Χ.
Item type: Conference publication
Keywords: Breast Cancer;Καρκίνος μαστού;Image Analysis;Pattern Recognition;Multimodality;US X-RAY;Ανάλυση εικόνας;Πολυτροπικότητα;Πρότυπα αναγνώρισης
Subjects: Medicine
Medical physics
Ιατρική
Ιατρική φυσική
Issue Date: 30-Jan-2015
2010
Date of availability: 30-Jan-2015
Publisher: Νερατζής, Ηλίας
Σιανούδης, Ιωάννης
Βαλαής, Ιωάννης Γ.
Φούντος, Γεώργιος Π.
Abstract: The purpose of the present study is to evaluate the effect of using multiple modalities on the accuracy achieved by a computer-aided diagnosis system, designed for the detection of breast cancer. Towards this aim, 41 cases of breast cancer were selected, 18 of which were diagnosed as malignant and 23 as benign by an experienced physician. Each case included images acquired by means of two imaging modalities: x-ray and ultrasound. Manual segmentation was performed on every image in order to delineate and extract the regions of interest (ROIs) containing the breast tumors. Then 104 textural features were extracted; 52 from the x-ray images and 52 from the US images. A classification system was designed using the extracted features for every case. Firstly, features extracted from x-ray images alone were used to evaluate the system. The same task was performed for features extracted from US images alone. Lastly the combination of the two feature sets, mentioned afore, was used to evaluate the system. The proposed system that employed the Probabilistic Neural Network (PNN) classifier scored 78.05% in classification accuracy using only features from x-ray. While classification accuracy increased at 82.95% using only features from US, a significant increase in the system’s accuracy (95.12%) was achieved by using combined features from both x-ray and US. In order to minimize total training time, the proposed system adopted the Client-Server model to distribute processing tasks in a group of computers interconnected via a local area network. Depending on the number of clients employed, there was about a 4-fold reduction in training time employing 7 clients.
Description: Special issue: Scientific papers presented on the 3nd International Conference on Experiments/Process/System Modeling/Simulation & Optimization in Athens, 8-11 July, 2009. Mini symposium on Medical Imaging, organized by G. Panayiotakis, I. Kandarakis, G. Fountos and I. Valais
Language: English
Citation: Pagonis, N., Cavouras, D.A., Sidiropoulos, K., Sakellaropoulos, G.C. and Nikiforidis, G.C. (2010). Improving the classification accuracy of computer aided diagnosis through multimodality breast imaging. "e-Journal of Science & Technology". [Online] 5(2): 33-39. Available from: http://e-jst.teiath.gr/
Journal: e-Περιοδικό Επιστήμης & Τεχνολογίας
e-Journal of Science & Technology
Type of Journal: With a review process (peer review)
Access scheme: Publicly accessible
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/5164
Appears in Collections:Τόμος 05, τεύχος 2 (2010)

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