Conference Papers
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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 Recent advances and future potential of computer aided diagnosis of liver cancer on computed tomography images(2011) Arakeri, M.P.; Guddeti, G.Liver cancer has been known as one of the deadliest diseases. It has become a major health issue in the world over the past 30 years and its occurrence has increased in the recent years. Early detection is necessary to diagnose and cure liver cancer. Advances in medical imaging and image processing techniques have greatly enhanced interpretation of medical images. Computer aided diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver cancer and hence reduce death rate. The concept of computer aided diagnosis is to provide a computer output as a second opinion in analysis of liver cancer. It assists radiologist's image interpretation by improving accuracy and consistency of radiological diagnosis and also by reducing image analysis time. The main objective of this paper is to provide an overview of recent advances in the development of CAD systems for analysis of liver cancer. Medical imaging system based on computer tomography will be focused as it is particularly suitable for detecting liver tumors. The paper begins with introduction to liver tumors and medical imaging techniques. Then the key CAD techniques developed recently for liver tumor detection, classification, case-based reasoning based on image retrieval and 3D reconstruction are presented. This article also explores the future key directions and highlights the research challenges that need to be addressed in the development of CAD system which can help the radiologist in improving the diagnostic accuracy. © Springer-Verlag 2011.Item Efficient fuzzy clustering based approach to brain tumor segmentation on MR images(2011) Arakeri, M.P.; Guddeti, G.Image segmentation is one of the most vital and significant step in medical applications. The conventional fuzzy c-means (FCM) clustering is the most widely used unsupervised clustering method for brain tumor segmentation on magnetic resonance (MR) images. However, the major limitation of the conventional FCM is its huge computational time and it is sensitive to initial cluster centers. In this paper, we present a novel efficient FCM algorithm to eliminate the drawback of conventional FCM. The proposed algorithm is formulated by incorporating distribution of the gray level information in the image and a new objective function which ensures better stability and compactness of clusters. Experiments are conducted on brain MR images to investigate the effectiveness of the proposed method in segmenting brain tumor. The conventional FCM and the proposed method are compared to explore the efficiency and accuracy of the proposed method. © 2011 Springer-Verlag.Item An approach for color edge detection with automatic threshold detection(2012) Arpitha, M.D.; Arakeri, M.P.; Guddeti, G.Edge is an important feature for image segmentation and object detection. Edge detection reduces the amount of data needed to process by removing unnecessary features. Edge detection in color images is more challenging than edge detection in gray-level images. This paper proposes a method for edge detection of color images with automatic threshold detection. The proposed algorithm extracts the edge information of color images in RGB color space with fixed threshold value. The algorithm works on three channels individually and the output is fused to produce one edge map. The algorithm uses the Kuwahara filter to smoothen the image, sobel operator is used for detecting the edge. A new automatic threshold detection method based on histogram data is used for estimating the threshold value. The method is applied for large number of images and the result shows that the algorithm produces effective results when compared to some of the existing edge detection methods. © 2012 Springer-Verlag.Item A parallel segmentation of brain tumor from magnetic resonance images(2012) Dessai, V.S.; Arakeri, M.P.; Guddeti, G.Medical image segmentation is nowadays at the core of medical image analysis and supports computer-aided diagnosis, surgical planning, intra-operative guidance or postoperative assessment. Large amounts of research efforts have been made in developing effective brain MR (magnetic resonance) image tumor segmentation methods in the past years. However algorithms proposed so far are time consuming because it involves lot of mathematical computations. Also serial segmentation of multiple MRI slices (usually required for 3D visualization) takes exponential time. This results in need for improvement in performance as far as the time complexity is concerned. This paper proposes a methodology that incorporates the K-means clustering and morphological operation for parallel segmentation of multiple MRI slices corresponding to single patient. Segmentation of multiple MRI slices for tumor extraction plays major role in 3D (Three Dimensional) visualization and serves as an input for the same. The proposed framework follows SIMD (Single Instruction Multiple Data) model and since the segmentation of individual slice is independent of each other and can be performed in parallel and multithreading definitely speeds up the entire process. Also the framework does not involve any kind of inter-process communication thus the time is saved here as well. © 2012 IEEE.Item Medical image retrieval system for diagnosis of brain tumor based on classification and content similarity(2012) Arakeri, M.P.; Guddeti, G.Accurate diagnosis is important for successful treatment of brain tumor. Content based medical image retrieval (CBMIR) can assist the radiologist in diagnosis of brain tumor by retrieving similar images from medical image database. Magnetic resonance imaging (MRI) is the most commonly used modality for imaging brain tumors. During image acquisition there can be misalignment of magnetic resonance (MR) image slices due to movement of patient and also the low level features extracted from MR image may not correspond with the high level semantics of brain tumor. These problems create a semantic gap and limit the application of automated image analysis tools on MR images. In order to address these problems, this paper proposes a two-level hierarchical CBMIR system which first classifies the query image of brain tumor as benign or malign and then searches for the most similar images within the identified class. Separate set of rotation invariant shape and texture features are used to discriminate between brain tumors at each level. Experiments have been conducted on medical image database consisting of 820 brain MR images. The proposed approach gives promising retrieval results by improving precision, recall and retrieval time. © 2012 IEEE.