Extraction and 3D Segmentation of Tumors-Based Unsupervised Clustering Techniques in Medical Images

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. We Received 290 medical images composed of 120 mammographic images, LJPEG format, scanned in gray-scale 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 LightSpeed 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. 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). 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.