Computer Aided Diagnosis Of Lung Diseases In Computed Tomography Images

The computer-aided diagnosis of lung disease in CT images has become one of theworld's research hotspots, which can be used as "secondary opinion" of radiologists toreduce the reading time and improve the correction of diagnosis. Computerizedsegmentation, feature quantification provided the precision analysis, which can improvereproducibility and the consistency of the diagnosis results.In this study, we developed different computer-aided diagnosis methods for airwaypathology and lung nodules in CT images with lung cancer and chronic obstructivepulmonary disease (COPD) based on their respective features.At first, we discuss the method of computer-aided diagnosis of COPD with thebronchial wall thickening in CT images. The key problem to be solved is how to detect themore suitable bronchial sections for quantification. In our study, based on ref, wepresent an automatic detection of bronchial sections based on roundness in multidirections. To validate this approach, we tested on 9 whole CT scans (including 7 normalCT scans and 2 COPD CT scans), the results show that, this approach can detect 2007approximate perpendicular bronchial sections per scan, with 0.67 false bronchial sectionsper section and 0.12 excessive attachment bronchial sections per section. The radios ofdetected bronchial sections under 3^rd stage of the bronchial tree in COPD scans are higherthan normal ones, which is coincident with the diagnosis made by radiologists. Thequantification of bronchial morphological parameters is very difficult because of the verysmall area and thin wall of bronchia, the partial volume effect influences the accuracy ofthe segmentation, which is the key problem to be solved in this study. We adopted themethod used max-min of CT value to segment the boundary of the bronchial wall, andquantify the parameters, such as the bronchial wall, the bronchial lumen area, and the ratioof the bronchial lumen area to the total bronchial section area. The results show that, forthe bronchial sections of the same stage in the tree. The bronchial wall areas of COPD arelarger than normal ones in the CT of patients with the same age, which is also coincidentwith the diagnosis made by radiologists.In another work of our researches, we study the method of computer-aided diagnosisof lung cancer with lung nodules in CT images. The main two contents include automaticdetection and feature quantification of lung nodules. Automatic detection of the lungnodules is the most popular issue of the research about the CAD of lung diseases. The curial problem to be solved is how to improve the sensitivity and reduce the false positivefaction of nodules at the same time. During the processing, the GGO nodules are moredifficult to be detection. In our work, we developed a multi-scale selective filter based onref to initially detect the candidate nodules, and then, we extract 20 features, such aseffective diameter, compactness et al, from the candidate nodules. We optimized thefeature set by feature selection, and used Fisher linear classifier to identify the true andfalse positive nodules. We used leave-one-scan-out cross validation on LIDC (LungImaging Database Consortium, LIDC) dataset to evaluate this detection, and draw theROC of all nodules, multi-scale nodules, multi-pattern nodules, multi-shape nodules andmulti-attached-degree nodules. The results show that when TPF is 87% of the detection ofall nodules, the FPF is 0.01FPs/Section, and when TPF is 96%, the FPF is0.03FPs/Section.The results also show that the GGO nodules can be detect well by thismethod. The feature quantification of nodules is very important to its benign andmalignant discrimination. One of its difficulties is an accurate segmentation. We present anodule segmentation approach used dynamic-programming and multi-direction fusiontechniques. The segmentation result on 23 nodules provided by LIDC indicate that theaverage overlap, Seg_TP and Seg_FP are 66%, 75%, 15% respectively. Based on thesegmentation results, we quantify the nodule features contribute to the diagnosis, such asspiculation signs, pleural indentation, and lobulation sign et al. Specific quantificationalgorithm need to be study for each sign because of different performances, which isanother difficulty of this study. We present a novel quantification method of spiculationsbased on boundary-normal gradient direction and make an evaluation by ROC analysis.The results show that the quantification of spiculations by this method is with highconsistency with the real spiculation level. Moreover, in our experiments, we compare thequantification of other features, such as pleural indentation, lobulation sign et al, with thequalitative classification provided by radiologists. The results indicate that mostquantification of the features can obtain consistency higher than 87%.