Segmentation of Lung Region in Computed Tomography (CT) Images

Segmentation of lung region in lung CT scan images is an important pre-processing technique prior automatic detection and classification of emphysema disease. Well segmented lung allows correct selection of region of interest (ROI) and thereby improve the abnormality detection and classification of the lung. In this work, a lung segmentation algorithm for CT images is proposed and evaluated. The proposed method is uses Gaussian smoothing filter followed by thresholding to create binary mask for the lung region. Formally, the binary mask will only select the lung region and zero the all the regions surrounding the lung area. The binary mask can be set to either separate the left and right lung or to show both lungs simultaneously. The algorithm is tested on a database of lung CT scan images of emphysema patients. The database contains top, middle and bottom sections of the lung. Evaluation of the algorithm using 39 middle section lung CT scan images give 15.38% correct segmentation of the left & right lung. With 39 top and 37 bottom section lung images, the algorithm give yy43% and 82.05% correct segmentation of lung. These results show the good potential of the proposed algorithm for segmentation of the lung region in CT images

Classification of Emphysema Patterns in Computed Tomography Based On Gabor Filter

Emphysema is a type of chronic obstructive pulmonary disease (COPD) affecting millions of people worldwide. Patients with emphysema typically have breathing difficulty. Early detection using Computed Tomography (CT) scan image can save many of the emphysema patients life. Furthermore, it helps the medical practitioners in planning suitable treatments for patients. The CT scan of human lungs are commonly taken from 3 different directions; center, bottom and top. The images obtained from different slices are then used by radiologist to identify normal or abnormal tissues. Computer-aided diagnosis (CAD) has becomes part of routine clinical work for assisting radiologist in detection of abnormal tissue in many screening sites and hospitals. One of the main processing technique in CAD is texture classification and analysis. In this research, a Gabor-based emphysema classification algorithm is proposed. Gabor filter offer the advantage of multi-resolution and multi-orientation properties and is optimal for measuring local spatial frequencies. In essence, the Gabor transform is performed by applying Gaussian masks prior to the discrete wavelet transform. The extracted feature from the Gabor filter is in the form of local energy calculated at different scale and orientation. The proposed emphysema classification algorithm involves four aspects, image pre-processing, feature extraction, matching (classification), and decision making. In the classification stage, the k-NN classifier is used to classify the CT images to two different classes which are Normal Tissue (NT) and Abnormal Tissue; Centrilobular Emphysema (CLE) and Paraseptal Emphysema (PSE). The proposed algorithm is evaluated using k-fold cross validation technique and its performance is shown to produce low misclassification rate of 0.01%

Classification of Emphysema Patterns in Computed Tomography Based On Gabor Filter

Emphysema is a type of chronic obstructive pulmonary disease (COPD) affecting millions of people worldwide. Patients with emphysema typically have breathing difficulty. Early detection using Computed Tomography (CT) scan image can save many of the emphysema patients life. Furthermore, it helps the medical practitioners in planning suitable treatments for patients. The CT scan of human lungs are commonly taken from 3 different directions; center, bottom and top. The images obtained from different slices are then used by radiologist to identify normal or abnormal tissues. Computer-aided diagnosis (CAD) has becomes part of routine clinical work for assisting radiologist in detection of abnormal tissue in many screening sites and hospitals. One of the main processing technique in CAD is texture classification and analysis. In this research, a Gabor-based emphysema classification algorithm is proposed. Gabor filter offer the advantage of multi-resolution and multi-orientation properties and is optimal for measuring local spatial frequencies. In essence, the Gabor transform is performed by applying Gaussian masks prior to the discrete wavelet transform. The extracted feature from the Gabor filter is in the form of local energy calculated at different scale and orientation. The proposed emphysema classification algorithm involves four aspects, image pre-processing, feature extraction, matching (classification), and decision making. In the classification stage, the k-NN classifier is used to classify the CT images to two different classes which are Normal Tissue (NT) and Abnormal Tissue; Centrilobular Emphysema (CLE) and Paraseptal Emphysema (PSE). The proposed algorithm is evaluated using k-fold cross validation technique and its performance is shown to produce low misclassification rate of 0.01%

Extended Gabor approach applied to classification of emphysematous patterns in computed tomography

11 pags.; 4 figs.; 6 tabs.Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible lung condition typically related to emphysema. It hinders air from passing through airpaths and causes that alveolar sacs lose their elastic quality. Findings of COPD may be manifested in a variety of computed tomography (CT) studies. Nevertheless, visual assessment of CT images is time-consuming and depends on trained observers. Hence, a reliable computer-aided diagnosis system would be useful to reduce time and inter-evaluator variability. In this paper, we propose a new emphysema classification framework based on complex Gabor filters and local binary patterns. This approach simultaneously encodes global characteristics and local information to describe emphysema morphology in CT images. Kernel Fisher analysis was used to reduce dimensionality and to find the most discriminant nonlinear boundaries among classes. Finally, classification was performed using the k-nearest neighbor classifier. The results have shown the effectiveness of our approach for quantifying lesions due to emphysema and that the combination of descriptors yields to a better classification performance. © 2014 International Federation for Medical and Biological Engineering.This work has been partially sponsored by the grants UNAM PAPIIT IN113611, IG100814, and TEC2010-20307 from the Spanish Ministry of Economy.Peer Reviewe

Multifractal techniques for analysis and classification of emphysema images

This thesis proposes, develops and evaluates different multifractal methods for detection, segmentation and classification of medical images. This is achieved by studying the structures of the image and extracting the statistical self-similarity measures characterized by the Holder exponent, and using them to develop texture features for segmentation and classification. The theoretical framework for fulfilling these goals is based on the efficient computation of fractal dimension, which has been explored and extended in this work. This thesis investigates different ways of computing the fractal dimension of digital images and validates the accuracy of each method with fractal images with predefined fractal dimension. The box counting and the Higuchi methods are used for the estimation of fractal dimensions. A prototype system of the Higuchi fractal dimension of the computed tomography (CT) image is used to identify and detect some of the regions of the image with the presence of emphysema. The box counting method is also used for the development of the multifractal spectrum and applied to detect and identify the emphysema patterns. We propose a multifractal based approach for the classification of emphysema patterns by calculating the local singularity coefficients of an image using four multifractal intensity measures. One of the primary statistical measures of self-similarity used in the processing of tissue images is the Holder exponent (α-value) that represents the power law, which the intensity distribution satisfies in the local pixel neighbourhoods. The fractal dimension corresponding to each α-value gives a multifractal spectrum f(α) that was used as a feature descriptor for classification. A feature selection technique is introduced and implemented to extract some of the important features that could increase the discriminating capability of the descriptors and generate the maximum classification accuracy of the emphysema patterns. We propose to further improve the classification accuracy of emphysema CT patterns by combining the features extracted from the alpha-histograms and the multifractal descriptors to generate a new descriptor. The performances of the classifiers are measured by using the error matrix and the area under the receiver operating characteristic curve (AUC). The results at this stage demonstrated the proposed cascaded approach significantly improves the classification accuracy. Another multifractal based approach using a direct determination approach is investigated to demonstrate how multifractal characteristic parameters could be used for the identification of emphysema patterns in HRCT images. This further analysis reveals the multi-scale structures and characteristic properties of the emphysema images through the generalized dimensions. The results obtained confirm that this approach can also be effectively used for detecting and identifying emphysema patterns in CT images. Two new descriptors are proposed for accurate classification of emphysema patterns by hybrid concatenation of the local features extracted from the local binary patterns (LBP) and the global features obtained from the multifractal images. The proposed combined feature descriptors of the LBP and f(α) produced a very good performance with an overall classification accuracy of 98%. These performances outperform other state-of-the-art methods for emphysema pattern classification and demonstrate the discriminating power and robustness of the combined features for accurate classification of emphysema CT images. Overall, experimental results have shown that the multifractal could be effectively used for the classifications and detections of emphysema patterns in HRCT images