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Paper Information

Journal:   IRANIAN JOURNAL OF MEDICAL PHYSICS   SUMMER 2013 , Volume 10 , Number 3; Page(s) 95 To 107.
 
Paper: 

EXTRACTION AND 3D SEGMENTATION OF TUMORS-BASED UNSUPERVISED CLUSTERING TECHNIQUES IN MEDICAL IMAGES

 
 
Author(s):  HADADNIA JAVAD*, REZAEE KHOSRO
 
* CENTER FOR NEW RESEARCH OF MEDICAL TECHNOLOGIES SABZEVAR UNIVERSITY OF MEDICAL SCIENCES, SABZEVAR, IRAN
 
Abstract: 

Introduction
The diagnosis and separation of cancerous tumors in medical images require accuracy, experience, and time, and it has always posed itself as a major challenge to the radiologists and physicians.
Materials and Methods
We Received 290 medical images composed of 120 mammographic images, LJPEG format, scanned in grayscale with 50 microns size, 110 MRI images including of T1-Wighted, T2-Wighted, and Proton Density (PD) images with 1-mm slice thickness, 3% noise and 20% intensity non-uniformity (INU) as well as 60 lung cancer images acquired using the 3D CT scanner, GE Medical System Light Speed QX/i helical, yielding 16-bit slices taken from various medical databases. By applying the Discrete Wavelet Transform (DWT) on the input images and constructing the approximate coefficients of scaling components, the different parts of image were classified. In next step using k-means algorithm, the appropriate threshold was selected and finally the suspicious cancerous mass was separated by implementation image processing techniques.
Results
By implementing the proposed algorithm, acceptable levels of accuracy 92.06%, sensitivity 89.42%, and specificity 93.54% were resulted for separating the target area from the rest of image. The Kappa coefficient was approximately 0.82 which illustrate suitable reliability for system performance. The correlation coefficient of physician’s early detection with our system was highly significant (p
<0.05).
Conclusion
The precise positioning of the cancerous tumor enables the radiologists to determine the progress level of the disease. The low Positive Predictive Value (PPV) and high Negative Predictive Value (NPV) of the system is a warranty of the system and both clinical specialist and patients can trust the software and output.

 
Keyword(s): DISCRETE WAVELET TRANSFORM, K-MEANS CLUSTERING, IMAGE PROCESSING, LUNG CANCER, MAMMOGRAMS, MR IMAGES
 
References: 
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