Biomedical Image Processing / Medical Image Processing
Fateme Nazem; Alireza Ahmadian; Mohammad Javad Abolhasani; Nasim Dadashi; Masoume Gity; Mohammad Bagher Shiran
Volume 5, Issue 4 , June 2011, , Pages 351-358
Abstract
Abstract: Image guided liver surgery based on intra-operative ultrasound images has received much attention in recent years. Using an efficient point-based registration method to improve both the accuracy and computational time for registration of pre-deformation CT liver images to post-deformation Ultrasound ...
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Abstract: Image guided liver surgery based on intra-operative ultrasound images has received much attention in recent years. Using an efficient point-based registration method to improve both the accuracy and computational time for registration of pre-deformation CT liver images to post-deformation Ultrasound images is of great concern during surgical procedure. Although, Iterative Closest Point (ICP) algorithm is widely used in surface-based registration, its performance is strongly dependent on existence of noise and initial alignment. The registration technique based on the Unscented Kalman Filter (UKF) proposed recently can be a solution to overcome to noise and outliers on an incremental registration basis but it suffers from computational complexity. To overcome the limitations of ICP and UKF algorithms we proposed an incremental two-stage registration algorithm based on the combination of ICP and UKF algorithm to update the registration process based on arrival of intra-operative images. The two-stage algorithm is examined on phantom data sets. The results of phantom study confirm that the two-stage algorithm outperforms the accuracy of ICP and UKF by 23% and 13%, respectively and reduces the running time of UKF by 60%.
Biomedical Image Processing / Medical Image Processing
Azar Tolouee; Hamid Abrishami Moghaddam; Masoume Giti
Volume 2, Issue 3 , June 2008, , Pages 179-189
Abstract
Automatic classification of lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) is an important stage in the construction of a computer-aided diagnosis system. In this study, classification of Jung tissue patterns was conducted ...
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Automatic classification of lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) is an important stage in the construction of a computer-aided diagnosis system. In this study, classification of Jung tissue patterns was conducted using a new machine learning approach. The proposed system comprises three stages. In the first stage, the parenchyma region in HRCT lung images is separated using a set of thresholding, filtering and morphological operators. In the second stage, two sets of overcomplete wavelet filters, namely discrete wavelet frames and rotated wavelet frames are utilized to extract the features from the defined regions of interest (ROJs) within parenchyma. Then, in the third stage, the fuzzy k-nearest neighbor algorithm is employed to perform the pattern classification. Our experiments in lung pattern classification were rendered on four different lung tissue patterns (ground glass, honey combing, reticular, and normal) selected from a database of 340 images from 17 subjects. After applying the technique to classify these patterns in small ROis, we extended the classification scheme to the whole lung in order to produce the quantitative scores of abnormalities in lung parenchyma of the patients. The performance of the proposed method was compared with two state-of-the-art computer based methods for lung tissue characterization. It was also validated against the experienced observers. The average kappa statistic of agreement between two radiologists and the computer was found to be 0.6543 where as the average kappa statistic for the interobserver agreement was 0.6848. This computer system can approach the performance of the expert observers in the diagnosing regions of interest and can help to produce objective measures of abnormal patterns in lung HRCT images.
Biomedical Image Processing / Medical Image Processing
Nader Riahi Alam; Reza Aghaeizade Zoroofi; Masoume Giti; Arian Deldari; Alireza Ahmadian
Volume 1, Issue 3 , June 2007, , Pages 157-165
Abstract
In this study, the need of a CAD system and its capabilities has been investigated and then a sample program for a mammographic CAD system proper to Iranian tropical patients was designed. In the first step, the analog mammographic images were digitized by 56 and 112 mm spatial resolution and then were ...
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In this study, the need of a CAD system and its capabilities has been investigated and then a sample program for a mammographic CAD system proper to Iranian tropical patients was designed. In the first step, the analog mammographic images were digitized by 56 and 112 mm spatial resolution and then were processed by the designed sample program. Analysis and technical details for designing and implementing the program included for following steps: The capability of the program image displayer consisting of viewing four mammographic images from four breast views (RCC, RMLO, LCC, LMLO) in one window, determining breast region by background removing and other conventional preprocessing application tools; Software processing tools including theresholding, histogram, ROI determination; Patient information fields such as clinical information, conventional reporting section as used in radiological department in Iran; Computer-aided diagnostic section including proper diagnostic processing algorithm to automatic detection of breast abnormality. For instance the application of wavelet and fuzzy logic for detecting malignant clusters of microcalcification. The introduced mammographic CAD system can provide the collection, organizing and the availability of the patient local information. Therefore by using the prepared database the evaluation of the sensitivity and specifity of the detecting algorithm for comparison of different research methods would be possible.
Biomedical Image Processing / Medical Image Processing
Ladan Amini; Hamid Soltanian Zadeh; Caro Lucas; Masoume Giti
Volume -2, Issue 1 , July 2005, , Pages 17-34
Abstract
Based on a discrete dynamic contour model, a method for segmentation of brain structures like thalamus and red nucleus from magnetic resonance images (MRI) is developed. A new method for solving common problems in extracting the discontinuous boundary of a structure from a low contrast image is presented. ...
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Based on a discrete dynamic contour model, a method for segmentation of brain structures like thalamus and red nucleus from magnetic resonance images (MRI) is developed. A new method for solving common problems in extracting the discontinuous boundary of a structure from a low contrast image is presented. External and internal forces deform the dynamic contour model. Internal forces are obtained from local geometry of the contour, which consist of vertices and edges, connecting adjacent vertices. The image data and desired image features such as image energy are utilized to obtain external forces. The problem of low contrast image data and unclear edges in the image energy is overcome by the proposed algorithm that uses several methods like thresholding, unsupervised clustering methods such as fuzzy C-means (FCM), edge-finding filters like Prewitt, and morphological operations. We also present a method for generating an initial contour for the model from the image data automatically. Evaluation and validation of the methods are conducted by comparing radiologist and automatic segmentation results. The average of the similarity between segmentation results is 0.8 for the left and right thalami indicating excellent performance of the new method. Additional noise and intensity inhomogeneity changed the evaluation results slightly illustrating the robustness of the proposed method to the image noise and intensity inhomogeneity.
Biomedical Image Processing / Medical Image Processing
Hamid Abrishami Moghaddam; Alireza Sheikh Hasani; Abbas Mostafa; Masoume Giti; Parviz Abdolmaleki
Volume -1, Issue 2 , June 2005, , Pages 117-128
Abstract
This paper presents a CAD system for detection and diagnosis of microcalcification clusters in mammograms. The proposed algorithm is composed of three main stages. In the first stage, the image pixels are examined for corresponding to individual microcalcification objects. For this purpose, the wavelet ...
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This paper presents a CAD system for detection and diagnosis of microcalcification clusters in mammograms. The proposed algorithm is composed of three main stages. In the first stage, the image pixels are examined for corresponding to individual microcalcification objects. For this purpose, the wavelet transform of the image is computed. Then two wavelet coefficients as well as two statistical features are used with a neural network for a primary classification of the image pixels. In the second stage, some noisy pixels extracted by the first step are eliminated. Then 18 features defined for each microcalcification are used with a nonlinear classifier for accurate detection of microcalcifications. For training of this classifier we used 16 regions from a database containing 379 microcalcifications. Finally, in the third stage five features defined for each microcalcification cluster with a neural network are used to recognize malignant microcalcification clusters. For training of this network, 22 clusters including 8 malignant and 14 benign cases were used. The performance of the algorithm was evaluated using a separate image set composed of 22 clusters including 10 malignant and 12 benign cases. Using these tests images and the threshold value of 0.45, the sensitivity of the algorithm was 100% and its specificity was 91.6%.
