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X-Ray Scanner Supplementary Module: Evaluation Of Disease Progression By Pulse Coupled Neural Network
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AUTHOR(S)
Maminiaina Alphonse Rafidison, Andry Auguste Randriamitantsoa, Paul Auguste Randriamitantsoa
KEYWORDS
Index Terms: Disease evaluation, image segmentation, Pulse Coupled Neural Network, x-ray scanner supplementary module.
ABSTRACT
Abstract: This paper presents a supplementary module of x-ray scanner to analyze the evolution of disease for a patient. The previous image medical examination which is stored on database will be compared with the current image scanner output. The new function is inserted between image reconstruction and visualization module. The algorithm is based on image segmentation which is handled by a particular neural network called PCNN or Pulse Coupled Neural Network. We compare the PCNN output of both images by calculating the percentage of cured/deterioration of the target and differentiate the concerned region with color marking. It will help the doctor to take an immediate decision for his patient instead of spending time for manual comparison.
REFERENCES
[1] S. Claeys, "x-ray scanner - physical basis of x-ray”, CERF 2001.
[2] I. Julian, D. Didier, "Computed Tomography x-ray", General Electric Medical Systems, Europe, January 2002.
[3] T. Lindblad, J. M. Kinser, "Image processing Using Pulse-Coupled Neural Networks", Second, Revised Edition, Springer, 2005.
[4] T. Hoang, N. Nguyen, T. Bui, "A Real-time Image Feature Extraction Using Pulse-Coupled Neural Network", International Journal of Emerging Trends & Technology in Computer Science (IJETICS) Vol. 1, Issue 3, pp. 117-185, September - October 2012.
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