Faculty Publications
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Item A comparative performance evaluation of independent component analysis in medical image denoising(2011) Arakeri, M.P.; Guddeti, G.R.M.Medical images are often corrupted by noise arising in image acquisition process. Accurate diagnosis of the disease requires that medical images be sharp, clear and free of noise. Thus, image denoising is one of the fundamental tasks required by medical image analysis. There exist several denoising techniques for medical images like Median, Wavelet, Wiener, Average and Independent component analysis (ICA) filters. The independent component analysis is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. In this paper, ICA has been used to separate out noise from the image to provide important diagnostic information to the physician and its usefulness is demonstrated by comparing its performance with other noise filtering methods. The performance of the ICA and other denoising techniques is evaluated using the metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Absolute Error (MAE) and Mean Structural Similarity Index (MSSIM). The ICA based noise filtering technique gives 25.8245 dB of PSNR, 0.7312 of MAE and 0.9120 of SSIM. The experimental results and the performance comparisons show that ICA proves to be the effective method in eliminating noise from the medical image. © 2011 IEEE.Item Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images(Springer London, 2015) Arakeri, M.P.; Guddeti, G.R.M.The manual analysis of brain tumor on magnetic resonance (MR) images is time-consuming and subjective. Thus, to avoid human errors in brain tumor diagnosis, this paper presents an automatic and accurate computer-aided diagnosis (CAD) system based on ensemble classifier for the characterization of brain tumors on MR images as benign or malignant. Brain tumor tissue was automatically extracted from MR images by the proposed segmentation technique. A tumor is represented by extracting its texture, shape, and boundary features. The most significant features are selected by using information gain-based feature ranking and independent component analysis techniques. Next, these features are used to train the ensemble classifier consisting of support vector machine, artificial neural network, and k-nearest neighbor classifiers to characterize the tumor. Experiments were carried out on a dataset consisting of T1-weighted post-contrast and T2-weighted MR images of 550 patients. The developed CAD system was tested using the leave-one-out method. The experimental results showed that the proposed segmentation technique achieves good agreement with the gold standard and the ensemble classifier is highly effective in the diagnosis of brain tumor with an accuracy of 99.09 % (sensitivity 100 % and specificity 98.21 %). Thus, the proposed system can assist radiologists in an accurate diagnosis of brain tumors. © 2013, Springer-Verlag London.