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Computer-based objective quantitative assessment of pulmonary parenchyma via X-ray CT Renuka Uppaluri1, Geoffrey McLennan2, Milan Sonka3, Eric A. Hoffman1,4 1Department 2Department 3Department

of Radiology University of Iowa, Iowa City, IA 52242

of Internal Medicine University of Iowa, Iowa City, IA 52242

of Electrical and Computer Engineering University of Iowa, Iowa City, IA 52242

4Department

of Biomedical Engineering University of Iowa, Iowa City, IA 52242 This is Page 1 of 13

Computer-based Objective Quantitative Assessment of Pulmonary Parenchyma via X-ray CT: Renuka Uppaluri — Published November 24, 1999 E-mail the author Tell a Colleague about this Article Join JRAD Mailing List | Search our archives ©2000 Journal of Radiology, LLC

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Abstract: This paper is a review of our recent studies using a texture-based tissue characterization method called the Adaptive Multiple Feature Method. This computerized method is automated and performs tissue classification based upon the training acquired on a set of representative examples. The AMFM has been applied to several different discrimination tasks including normal subjects, subjects with interstitial lung disease, smokers, asbestos-exposed subjects, and subjects with cystic fibrosis. The AMFM has also been applied to data acquired using different scanners and scanning protocols. The AMFM has shown to be successful and better than other existing techniques in discriminating the tissues under consideration. We demonstrate that the AMFM is considerably more sensitive and specific in characterizing the lung, especially in the presence of mixed pathology, as compared to more commonly used methods. Evidence is presented suggesting that the AMFM is highly sensitive to some of the earliest disease processes. Tissue characterization, texture, smoking, pulmonary, interstitial lung disease This is Page 2 of 13


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INTRODUCTION In recent years, it has become critical that an objective quantitative lung assessment tool be developed which can be used to measure outcomes for new drug therapies and surgical interventions. The Adaptive Multiple Feature Method (AMFM) is such a computer-based technique for the characterization of the pulmonary parenchyma from X-ray CT. While previously reported methods using mean lung density information or the lowest fifth percentile of the density histogram of the image have been shown to be reasonably successful for characterizing emphysema, these methods have not been successful in identifying other parenchymal diseases. The AMFM has proven to be applicable to a wide variety of tissue characterization problems relating to the lung. This paper provides an overview of our recent successes with the AMFM towards providing a user-independent quantitative assessment of lung. Specifically, we will discuss the evolution of the AMFM as a method and its applications to emphysema, interstitial lung diseases smoking-related pathologies, asbestosis, and cystic fibrosis. This is Page 3 of 13


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METHODS The AMFM is a pattern recognition method which basically consists of three steps: feature extraction, optimal feature selection, and classification. Prior to feature extraction, pre-processing of the images was also performed and appropriate regions of interest were selected. When the AMFM was first designed, 17 texture features were extracted. These were the grey level distribution features (mean, variance, skewness, kurtosis, grey level entropy), run length features (short run emphasis, long run emphasis, grey level non-uniformity, run length non-uniformity, run percentage), co-occurrence matrix measures (angular second moment, inertia, entropy, contrast, correlation, inverse difference moment), and a fractal measure (geometric fractal dimension). In later studies, five other novel fractal measures were added. These were stochastic fractal dimension (SFD) measures based on the fractional Brownian motion model; SFD mean, SFD variance, SFD skewness, SFD kurtosis, SFD entropy. Still later, based on the partial success of the lowest fifth percentile of the density histogram as a measure of emphysema, five percentile-based measures were added. These were the lowest fifth percentile of the histogram, highest fifth percentile of the histogram, mean - lowest fifth percentile, highest fifth percentile - mean, ratio of (highest fifth percentile - mean) over (mean - lowest fifth). The optimal feature selection was performed using the ``divergence" measure along with correlation analysis. As a classifier, a minimum distance classifier was initially used for ease of implementation. This was later replaced by a non-linear Bayesian classifier which performs classifications based on a minimum loss criterion. In all discrimination tasks, the data available was split arbitrarily into two independent sets - a training and a test set. The training data was used to find an optimal subset of features that best discriminated the samples in the training set. This optimal subset of features was then extracted on the test set and classification was performed using the learning acquired in the training stage. The success of the method in performing the discrimination task was assessed by computing the accuracy (percent correctly classified samples of all subject groups under consideration) or the classification rate (percent of correctly classified sample of each subject group under consideration). Sensitivity and specificity were computed in some studies. This is Page 4 of 13


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RESULTS Application to emphysema Our first report of the partially designed AMFM and its applications can be found in Uppaluri et al. This served as a pilot study to assess the potential of such a method for tissue characterization. The salient features of this work are listed below: ● Data Data included 10 normal and 10 emphysema subjects. They were scanned either prone or supine with an Imatron C-150 XL electron beam CT (EBCT) scanner (South San Francisco, CA). The collimation ranged from 3 mm to 10 mm. Three slices of each subject were used for the analysis. ● Method There were 17 features used which were derived from the grey level distribution, run length, co-occurrence matrix measures, and the geometric fractal dimension. A minimum distance classifier was used following optimal feature selection. ● Findings The first experiment was the global analysis study where the goal was to differentiate the the whole CT slices of normal and emphysematous subjects. It was found that these two subject groups could be discriminated with 93.0% accuracy. In the second experiment, the sensitivity of the method was tested by applying it for differentiating subtle differences in textures. Specifically, the task was to discriminate between the anterior one-third vs. the posterior one-third of a normal slice. Such differences were known to occur because of gravity. The accuracy in differentiating these groups was 86.6%. In the third experiment, the lung on the CT slice was divided in to 6 regions, anterior to posterior, and regional analysis was performed. The AMFM was applied to differentiate normal and emphysema samples derived from the same region (one of the six) on the slice. This was performed with an accuracy of 82.9 5.7% over all the 6 regions. It was concluded that computerized texture analysis was indeed applicable for tissue characterization. However, this study had limitations. The slice thickness of the scans varied from subject to subject. Also, in the regional analysis experiment, any sample with at least 20.0% emphysema (as assessed visually by an experienced observer) was used. This probably led to the inclusion of emphysema samples having both ``normal" and ``emphysema" regions in them. The lack of representative pure emphysema samples in the training set probably led to ambiguous training and hence the accuracies of correct recognition were lower. In a following study, these limitations were overcome by data scanned using a standardized protocol. The AMFM was compared against previously reported methods for identifying emphysema, namely, the mean lung density (MLD) method and the lowest fifth percentile of the histogram (HIST) method. ● Data Data included 9 normal and 10 emphysema subjects scanned with an Imatron C-150 XL EBCT scanner. The normal subjects were scanned prone and the emphysema subjects were scanned supine. Standardized protocol of same slice thickness (3 mm), similar field of view, and same inspiration level (full inspiration) was used. Four slices from each subject were used for the analysis. ● Method As before, the grey level distribution, run length, co-occurrence matrix measures, and the geometric fractal dimension were used as features. A Bayesian classifier was used in place of the minimum distance classifier after the optimal feature selection. ● Findings The same three experiments as in the previous study were performed. In the global analysis, the AMFM differentiated the normal from the emphysematous scans with 100.0% accuracy as compared to accuracies of 94.7% and 97.4% by the MLD and HIST methods. In the anterior-posterior differentiation, the accuracy of the AMFM was 89.8% compared to accuracies of 74.6% and 64.4% by the MLD and HIST methods. In the regional normal vs. emphysema discrimination, over all the 6 regions, the accuracy of the AMFM was 97.9%. The accuracies using the MLD and HIST methods were 89.9% and 99.1%, respectively. The results obtained in this study were substantially better than the results of our previous study. This demonstrated the importance of standardized scanning protocol. Further, the Bayesian classifier, being a non-linear classifier was a better choice for a classifier. In the regional analysis, only those emphysema regions with definite disease (as assessed visually by an experienced observer) were used for training and testing. This is Page 5 of 13


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RESULTS Application to interstitial lung disease In recent years, it has been found that emphysema occurs along with interstitial lung disorders such as idiopathic pulmonary fibrosis (IPF). Therefore, we expanded our study to characterize IPF in addition to emphysema. MLD was the only method previously used to describe IPF. The AMFM was compared against the MLD and HIST methods in simultaneously differentiating normal subjects from subjects with emphysema and IPF. ● Data 20 normal, 10 emphysema, and 19 IPF subjects were included in the study. The normal and IPF subjects were scanned prone and the emphysema subjects were scanned supine. Data with 3 mm slice thickness was acquired at full inspiration with an Imatron C-150 XL EBCT scanner. Four slices from each subject were used for the analysis. ● Methods The same 17 texture features as in the previous two studies were used. The optimal feature selection followed the application of the Bayesian classifier. ● Findings A global analysis study including whole lungs from CT slices of all the subject groups was performed. The classification rates of AMFM in identifying emphysema, IPF, and normal were 100.0%, 97.2%, and 100.0% respectively. The classification rates of the MLD method for the same three subject groups were 100.0%, 77.8%, and 65.0%. With the HIST method, the classification rates for the same three subject groups were 80.0%, 61.1%, and 97.5%. From this study, we concluded that the AMFM was applicable and successful in the detection of other parenchymal lung diseases in addition to emphysema. Further, the AMFM out-performed the MLD and HIST methods substantially in simultaneously discriminating normal from emphysema and IPF. We progressed towards regional quantitative assessment of the pulmonary parenchyma. This was accomplished by characterizing the parenchymal patterns through which the diseases manifest themselves, rather than the diseases themselves. Sarcoidosis, another interstitial lung disorder, was added to the study. Representative samples of 6 patterns from normal, emphysema, IPF, and sarcoidosis were identified on CT by experienced observers. These were honeycombing, ground glass, broncho-vascular, nodular, emphysema, and normal. Through a training and testing phase, it was ascertained that these six patterns could successfully be discriminated by the AMFM. Following pixel windows was performed. The this, analysis of CT slices using overlapping classification (one of the six patterns) assigned for each

pixel window was assigned to a

pixel block at its center. The composition of the CT slice in terms of the distribution of patterns was available using this method. This computer analysis was validated against 5 independent experienced observers. ● Data 12 CT slices derived from normal, emphysema, IPF, and sarcoidosis subjects were included in the study. The normal, IPF, and sarcoidosis subjects were scanned prone and the emphysema subjects were scanned supine. Data with 3 mm slice thickness was acquired at full inspiration with an Imatron C-150 XL EBCT scanner. ● Method In addition to the 17 features used so far, 5 novel in-house developed stochastic fractal dimension features were extracted from the data. In total, 22 texture features were used. The Bayesian classifier was used for the classification following optimal feature selection. ● Findings The observers were asked to label the same regions of the lung for which the computer provided a classification. They performed the labeling in 3 repeat settings, 2-4 weeks apart. The first and second settings were double-blinded (blinded to primary diagnosis of the subject and to the computer output). The third setting was single-blinded (primary diagnosis disclosed but blinded to the computer output). The average inter-observer agreement in the first and second settings was 43.8 7.9% and 45.0 13.8%. The average computer-observer agreement for the same settings was 36.6

5.6% and 39.0

6.0%, respectively. The

intra-observer agreement ranged from 46.2%-77.1%. In the single-blinded study, the average computer-observer agreement significantly increased (p