An Efficient Scheme for Lung Nodule Detection

Abstract

Lung cancer has been one of the major threats to human life for decades in both developed and under developed countries with the smallest rate of survival after diagnosis. The survival rate can be increased by early nodule detection. Computer Aided Detection (CAD) can be an important tool for early lung nodule detection and preventing the deaths caused by the lung cancer. In this dissertation, we have proposed a novel technique for lung nodule detection using a hybrid feature set. The proposed method starts with pre-processing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multi scale dot enhancement filtering prior to nodule detection and feature extraction. Finally, classification of lung nodules is achieved using Support Vector Machine (SVM) classifier. The feature set consists of intensity, shape (2D and 3D) and texture features, which have been selected to optimize the sensitivity and reduce false positives. In addition to SVM, some other supervised classifiers like K-Nearest-Neighbour (KNN), Decision Tree and Linear Discriminant Analysis (LDA) have also been used for performance comparison. The extracted features have also been compared class-vise to determine the most relevant features for lung nodule detection. The proposed system has been evaluated using 850 scans from Lung Image Database Consortium (LIDC) dataset and k-fold cross validation scheme. The main research work done in this dissertation is summarized in the following section. 1. The proposed method starts with the segmentation of lung volume from pre-processed input CT images. Lung segmentation has a critical importance as it is pre-requisite to the nodule detection. Any in-accurate lung volume segmentation can lead to the low accuracy of whole system. In this dissertation, we propose a fully automated segmentation method for lung volume from CT scan images which consists of series of steps. Initially, the CT image is segmented by using optimal thresholding and the lung volume is obtained using connected component labeling method and other irrelevant information is removed at this stage. The resultant image at this stage contains holes which is filled with the hole filling algorithm e.g. morphological operations. Finally, the lung contour is smoothed by rolling ball algorithm to include any juxta pleural nodules. 2. After lung segmentation, image enhancement is done to detect the low-density nodules. Image enhancement plays an important role in detection of these nodules by enhancing them and reducing false positives by weakening the other structures in lung region. In this thesis, a multi scale dot enhancement filter is used to detect these low-density nodules which may remain undetected in the absence of any enhancement algorithm and can affect the accuracy of the system. In the first step, a Gaussian smoothing on all the corresponding 2D slices is performed to reduce the noise and sensitivity effect. After Gaussian smoothing, Hessian matrix and its eigen values |𝜆2|<|𝜆1| are calculated for every pixel to determine the local shape of the structure. The suspected pulmonary nodule region exhibits the form of a circular or oval object whereas vascular tissue structures presents a line-like elongated structure. Therefore, this property can be used to distinguish different shape structures present in lung region. This process is repeated for different scales and finally we integrate the filter’s output values to obtain the maximum value for the best enhanced effect and generate the resultant image. After image enhancement, lung nodule candidates are detected using optimal thresholding. Then a rule-based analysis has been made based on some initial measurements like area, diameter and volume whether to keep or discard the detected nodule candidate. The advantage of rule-based analysis is that it eliminates the objects which are too small or too big to be considered as a nodule candidate and thus reduces the workload for the next stage. 3. A hybrid feature set is obtained after rigorous experimentation which increases the classification accuracy and reduces the false positive per scan considerably. The proposed feature set plays a crucial role in the overall performance of the CAD system. We selected a large pool of features initially and then trimmed down the set on the basis of accuracy and false positive per scan and ultimately obtained the proposed hybrid feature set. 4. The classification of pulmonary nodules is done using SVM algorithm. In the classification phase, the suspected pulmonary nodules are divided into true pulmonary nodules and false pulmonary nodules. SVM as a high-dimensional multi-feature hyperplane differentiation algorithm performs considerably well in a situation where it must decide only between the two classes i.e., nodule or non-nodule and the features of the suspected pulmonary nodules refer mainly to the two classes and the Gaussian Radial Basis Function (RBF) kernel function can increase its linear separability which makes the detection and classification of pulmonary nodules more accurate. 5. We have done an extensive evaluation of our proposed system on Lung Image Database Consortium (LIDC). LIDC is a publicly available database accessible from The Cancer Imaging Archive (TCIA). We have considered the 850 scans (LIDC-IDRI-0001 to LIDC-IDRI 0844) of this dataset, which contains nodules of size 3-30 mm fully annotated by four expert radiologists in two consecutive sessions. K-fold cross-validation scheme is used for model selection and validation whereas the k value varies for 5, 7 and 10. An exhaustive grid search has been used to tune the hyperparameters of SVM classifier. Some other classifiers have also been used for classification of lung nodule candidates. An attempt has also been made to determine the most relevant feature class for lung nodule detection system. The achieved sensitivities at detection and classification stages are 94.20% and 98.15%, respectively, with only 2.19 FP/scan. The results of our proposed method show the superiority of our scheme as compared to other systems with increased sensitivity and reduced FP/scan. The main contribution of this dissertation is the presentation of a relatively simple nodule detection scheme that has a very good performance in an extensive experimental analysis. In addition, the proposed feature set has helped in reducing the false positives significantly and has increased the sensitivity of the proposed system. Moreover, a comparison has been made to determine the most relevant feature class in extracted feature set. The overall sensitivity has been improved compared to the previous methods and FP/scan have been reduced significantly.