Abstract-The purpose of the described system is to aid radiologists on their daily routine in the task of analyzing HRCT lung images and to contribute to a more accurate and fast diagnosis. We developed a framework -Study Lung Toolwith the objective of gather information from radiologists, in a systematic way. Using Study Lung Tool framework, the radiologist analyzes HRCT scans, outlines regions of typical pattern and characterizes the patterns. A database of typical patterns associated with common pulmonary diseases was created. The information gathered can be a valuable teaching tool to every one that intends to understand HRCT lung parenchyma. The proposed system discriminates between normal and abnormal patterns of lung parenchyma based on statistical texture analysis extracted from HRCT lung scans. An overall accuracy of 89,2%, a sensitivity of 92,7% and a specificity of 83,6% were achieved

José Silvestre Silva

João Barroso

Verónica Vasconcelos

CiteSeerX

O. Dössel and W.C. Schlegel (Eds.): WC 2009, IFMBE Proceedings 25/II, pp. 909–912, 2009. 
www.springerlink.com 
Study Lung Tool: A Way to Understand HRTC Lung Parenchyma  
Verónica Vasconcelos1,3, José Silvestre Silva 2,3, and João Barroso4 
1 Instituto Superior de Engenharia, Instituto Politécnico de Coimbra, Portugal 
2 Departamento de Física, Faculdade de Ciências e Tecnologia, Universidade de Coimbra, Portugal 
3 Centro de Instrumentação, Faculdade de Ciências e Tecnologia, Universidade de Coimbra, Portugal 
4 Universidade de Trás-os-Montes e Alto Douro, Portugal 
Abstract—The purpose of the described system is to aid 
radiologists on their daily routine in the task of analyzing 
HRCT lung images and to contribute to a more accurate and 
fast diagnosis. We developed a framework -Study Lung Tool- 
with the objective of gather information from radiologists, in a 
systematic way. Using Study Lung Tool framework, the 
radiologist analyzes HRCT scans, outlines regions of typical 
pattern and characterizes the patterns. A database of typical 
patterns associated with common pulmonary diseases was 
created. The information gathered can be a valuable teaching 
tool to every one that intends to understand HRCT lung 
parenchyma. The proposed system discriminates between 
normal and abnormal patterns of lung parenchyma based on 
statistical texture analysis extracted from HRCT lung scans. 
An overall accuracy of 89,2%, a sensitivity of 92,7% and a 
specificity of 83,6% were achieved. 
Keywords—High-resolution computed tomography, pulmo-
nary parenchyma, computer-aided diagnosis, texture analysis. 
I. INTRODUCTION  
In clinical practice, Computed Tomography (CT) is an 
important imaging modality for the diagnosis, treatment, 
and follow-up of lung diseases. Nowadays, the image 
quality achieved by High Resolution Computed 
Tomography (HRCT) scanners provides high morphologic 
detail of lung parenchyma and has already become the 
accepted imaging golden standard for lung study [1]. 
However, the HRCT analysis and interpretation of lung 
parenchyma patterns is a complex task. Several lung 
changes are difficult to understand because patterns are 
often mixed, can be present in different diseases and also 
may change during different stages of the disease. Many 
computer-aided diagnosis (CAD) systems have been 
developed during the last several years [2 - 7] showing the 
importance of this issue.  
Due to intra-observer and inter-observer differences in 
analysis and interpretation of HRCT scans and the difficulty 
of establish a reliable reference standard we were motivated 
to develop a framework - Study Lung Tool - that collects 
information of normal and abnormal lung parenchyma from 
HRCT exams acquired at Coimbra University Hospital [8].  
In this paper, we present an automated system capable of 
detect abnormal lung tissue in HRCT scans. Based on a 
database of patterns the system was trained and tested using 
pattern recognition approach. Each region of interest (ROI) 
is characterized through a set of 97 statistical texture 
descriptors. The performance of the system was evaluated 
by overall accuracy, sensitivity and specificity. 
II. DATA AQUISITION 
Usually the diagnosis of lung disease on a thoracic CT is 
based on three elements [9]: recognition and classification 
of abnormal pattern in a category, determination of location 
and distribution of the abnormalities in the lung, careful 
analysis of the patient data that are available at the time the 
CT scan is done. 
In order to gather information from radiologists we 
developed an independent, intuitive and user-friendly 
framework - Study Lung Tool. This software has multiple 
applications:  
• Allows the visualization of complete CT scans, in an 
automatic mode or image by image mode. Details can 
be observed by magnifying a region or whole CT slice 
through zoom in/out option; 
• After the characterization of diseases/patterns in the 
exam, by radiologist, the software can be used as a 
learning tool. Users of this framework can understand 
lung anatomy and pathology in HRTC images, 
understand the CT changes in patients with lung 
disease, recognize the appearance and distribution of 
lung patterns in different stages of the disease and to 
use these patterns to assist in the diagnosis; 
• Radiologist can review complete HRCT scans, select 
and put a label on the outlined ROI, typifying tissue 
patterns. The Figure 1 illustrates some software options 
and some outlined ROIs. These ROIs were split in 
small ROIs of 32x 32 pixels and used as a training and 
test set in the classification process. 
910 V. Vasconcelos, J.S. Silva, and J. Barroso
 
  
 
IFMBE Proceedings Vol. 25
 
 
Fig. 1 Some outlined ROIS done by a radiologist using Study Lung Tool 
framework 
Figure 2 shows some examples of the ROIs highlighted 
by radiologists, such as normal lung tissue and several 
abnormal patterns. The data were collected from the regular 
practice from Radiology Department of Coimbra University 
Hospital. The images were acquired on a LightSpeed VCT 
64 multidetector spiral scanner from General Electrics 
Healthcare. The images were stored as individual DICOM 
16-bit grayscale files, with the pixel intensity proportional 
to tissue density. Images were displayed using a lung 
window with a centre of -700 Hounsfield Units (HU) and a 
width of 1500 HU. Each ROI was selected from three 
predefined anatomic levels: apex to carina, carina to the 
lower lung veins and lower lung veins to the diaphragm. 
III. TEXTURE ANALYSIS  
Texture plays an important role in medical images 
analysis. In statistical texture methods, texture features are 
computed from statistics distribution of observed 
combinations of intensities positions relative to each other 
in the image. The results presented is this paper are based 
on statistical methods including first-order gray level 
statistics, gray level run length (GLRL) method and gray 
level co-occurrence (GLCM) method. 
The spatial dependence of gray levels distributions were 
computed from a set of texture descriptors extracted from a 
set of co-occurrence matrices. The co-occurrence matrix 
P(i,j|d,θ) corresponds to the number of pairs of gray levels 
encountered in the neighborhood of each pixel [10]. The six 
texture descriptors used in our method, are presented in 
equations (1) to (6), where L is the number of gray levels, P 
is the normalized symmetric GLCM of dimension LxL and 
P(i,j) is the (i, j)th element of P. The direction θ=0º, θ=45º, 
θ=90º and θ=135º with a range of d=1 and d=2 was used in 
this work, in a total of eight GLCM. 
 
Fig. 2 Several examples of tissue patterns as outlined by radiologist. On 
each image, a region of interest (ROI) highlights a typical pattern. (A) 
Normal lung tissue. (B) Bullous emphysema. (C) Ground-glass opacity. 
(D) Honeycombing 
1. Angular Second Moment (ASM) or Energy 
( )2
1 1
,
L L
i j
ASM P i j
= =
=∑∑
                                         
(1) 
2. Entropy (ENT) 
( ) ( )( )2
1 1
, log ,
L L
i j
ENT P i j P i j
= =
= −∑∑
                        
(2) 
3. Maximum Probability (MaxProb) 
( )
1 1
,
L L
i j
MaxProb Max P i j
= =
⎛ ⎞
= ⎜ ⎟⎜ ⎟
⎝ ⎠
∑∑
                            
(3) 
4. Inverse Difference Moment (IDM) 
( )
( )21 1
,
1
L L
i j
P i j
IDM
i j= =
=
+ −
∑∑
                                     
(4) 
5. Contrast (CON) 
( )( )2
1 1
,
L L
i j
CON P i j i j
= =
= −∑∑
                                 
(5) 
6. Correlation (COR) 
( ) ( )( )
1 1
,
L L i j
i j i j
i j
COR P i j
μ μ
σ σ= =
⎡ ⎤− −
⎢ ⎥=
⎢ ⎥
⎣ ⎦
∑∑
                    
(6) 
Study Lung Tool: A Way to Understand HRTC Lung Parenchyma 911
 
  
 
IFMBE Proceedings Vol. 25
 
where iμ  is the Mean and iσ the Standard Deviation 
( )
1 1
,
L L
i
i j
P i j iμ
= =
=∑∑
                                                          
(7) 
( )( )2
1 1
,
L L
i i
i j
P i j iσ μ
= =
= −∑∑
                                             
(8) 
The shape aspects of the gray level primitives of each ROI 
were described through RLM features. The run length features 
were extracted from run length matrix M. A gray level run 
primitive is defined as a set of consecutive and collinear pixels 
which have the same gray level intensity, the run length is the 
number of pixels in the run and the run length value is the 
number of times such a run occurs in the image. Fine textures 
tend to contain more short runs with similar gray level 
intensities, while coarse textures have more long runs with 
significantly different gray level intensities [11]. 
Each element of a run length matrix M(a,r|θ) represents 
the number of runs with pixels of gray level intensity a and 
length r along the orientation θ. The typical orientations of 
0°, 45°, 90°, and 135° were used in this work. Calculating 
the run length encoding for each direction will produce four 
run length matrices. The size of each matrix M is LxNr, 
where L is the number of gray levels and Nr is the longest 
run length in the ROI.  
Each ROI was quantized into 16 gray levels scale. A set 
of four run length matrices were computed, one for each 
direction θ and eleven scalar measures were extracted as 
proposed by Galloway [12], Chu et al. [13] and Dasarathy 
and Holder [14]: 
1. Short Run Emphasis (SRE): 
2
1 1
1 ( , )L Nr
a rr
M a rSRE
n r= =
= ∑∑
                                              
(9) 
where nr is the total number of observed runs 
1 1
( , )
L Nr
r
a r
n M a r
= =
=∑∑
                                                 
(10) 
2. Long Run Emphasis (LRE): 
2
1 1
1 ( , )
L Nr
a rr
LRE M a r r
n = =
= ∑∑
                                          
(11) 
3. Gray Level Non-uniformity (GLNU): 
2
1 1
1 ( , )
L Nr
a rr
GLNU M a r
n = =
⎛ ⎞
= ⎜ ⎟
⎝ ⎠
∑ ∑   
                                       
(12) 
4. Run Length Non-uniformity (RLNU): 
2
1 1
1 ( , )
Nr L
r ar
RLNU M a r
n = =
⎛ ⎞
= ⎜ ⎟
⎝ ⎠
∑ ∑                                          (13) 
5. Run Percentage (RP) 
r
p
nRP
n
=                                                                  (14) 
where nr is the total number of pixels. 
6. Low Gray Level Run Emphasis (LGLRE) 
2
1 1
1 ( , )L Nr
a rr
M a rLGLRE
n a= =
= ∑∑
                                               
(15) 
7. High Gray Level Run Emphasis (HGLRE) 
2
1 1
1 ( , )
L Nr
a rr
HGLRE M a r a
n = =
= ∑∑
                                            
(16) 
8. Short Run Low Gray Level Emphasis (SRLGLE) 
2 2
1 1
1 ( , )L Nr
a rr
M a rSRLGLE
n a r= =
= ∑∑
                                             
(17) 
9. Short Run High Gray Level Emphasis (SRHGLE) 
2
2
1 1
1 ( , )L Nr
a rr
M a r aSRHGLE
n r= =
= ∑∑
                                         
(18) 
10. Long Run Low Gray Level Emphasis (SRLGLE) 
2
2
1 1
1 ( , )L Nr
a rr
M a r rLRLGLE
n a= =
= ∑∑
                                          
(19) 
11. Long Run High Gray Level Emphasis (SRHGLE) 
2 2
1 1
1 ( , )
L Nr
a rr
LRHGLE M a r a r
n = =
= ∑∑
                                        
(20) 
In order to describe the HRCT attenuation characteristics of 
each ROI, a set of textural features were obtained in the 
form of a 97-dimensional vector v. These features included 
mean, standard deviation, skewness, and kurtosis first-order 
features, 48 co-occurrence features, and 44 run length 
features. 
IV. PERFORMANCE ASSESSMENT  
To perform automated discrimination of the samples, 
between normal and abnormal lung tissue, we used a 
Support Vector Machine (SVM) classifier. SVM is a 
machine-learning method, introduced by Vapnik in 1995 
[15], based on the principle of structural risk minimization. 
The mapping from original feature space to a higher 
dimension feature space is done using a set of mathematical 
functions known as kernels. As the feature extraction is 
implicit in the training procedure we did not performed 
feature selection [16]. The implementation of the SVM 
classifier was done through the Bioinformatics Toolbox 
912 V. Vasconcelos, J.S. Silva, and J. Barroso
 
  
 
IFMBE Proceedings Vol. 25
 
available in Matlab R2007b and a Gaussian radial basis 
function kernel was used. 
From a set of 241 scans, radiologists outlined several 
ROIs. Each one was split into smaller ROIs of 32x32 pixels. 
To make optimal use of the available dataset a four-fold 
cross validation method was performed, randomly dividing 
the dataset in four groups. On average there were 630 ROIs 
in the train set and 210 ROIs in the test set. 
The system was evaluated using sensitivity, specificity 
and overall accuracy. A positive sample is a ROI where 
lung pathology is present, and a negative sample is a ROI 
where lung tissue is normal. Sensitivity is defined as the 
number of samples correctly classified as positive divided 
by total number of positive samples. Specificity is defined 
as the number of samples correctly classified as negative 
divided by total number of negative samples. Overall 
accuracy is defined as the total number of correctly 
classified samples divided by the total number of samples. 
The results achieved were an accuracy of 89,2%, a 
sensitivity of 92,7%, and a specificity of 83,6%. 
V. CONCLUSIONS  
We described a CAD system that automatically 
distinguishes normal from pathologic tissue in HRCT lung 
scans. The system was based on a pattern recognition 
approach, in which a set of statistical texture descriptors 
were used to characterize each ROI.  
Classification was done based on a database of typical 
samples from different pathologies, acquired from Coimbra 
University Hospital daily routine. This database was 
acquired through Study Lung Tool framework that showed 
to be very practical and also a valuable teaching tool.  
Due to the large amount of extracted features for each 
ROI, a feature selection algorithm should be included to 
reduce computational complexity. We also intend to 
evaluate the ROI size in overall performance of our system. 
ACKNOWLEDGMENT 
The authors thank to Dr. Luísa Teixeira and Dr. Miguel 
Sêco, physicians from Radiology Department of Coimbra 
University Hospital, for their medical knowledge and 
assistance. 
REFERENCES  
1. Verschakelen J A, Wever W D (2007) Computed Tomography of the 
Lung - A Pattern Approach. Springer Berlin Heidelberg Edition 
2. Sluimer I, Waes P F, Viergever M A (2003) Computer-aided 
diagnosis in high resolution CT of the lungs. Med Phys  30:3081–
3090 
3. Uppaluri R, Hoffman E A et al (1999) Computer Recognition of 
Regional Lung Disease Patterns. Am J Respir Crit Care Med 
160:648-654 
4. Xu Y, Sonka M, McLennan G et al (2006) MDCT-Based 3-D Texture 
Classification of Emphysema and Early Smoking Related Lung 
Pathologies, IEEE Transactions on Medical Imaging vol. 25, nº 4, 
2006, pp 464-474 
5. Uchiyama Y, Katsuragawa S et al (2003) Quantitative computerized 
analysis of diffuse lung disease in high-resolution computed 
tomography. Med Phys 30:2440-2454 
6. Sluimer I, Prokop M et al (2006) Automated classification of 
hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-
resolution CT of the lung. Med. Phys 33:2610-2620 
7. Zrimec T, Busayarat S (2007) A System for Computer Aided 
Detection of Diseases Patterns in High Resolution CT images of the 
Lungs, Twentieth IEEE International Symposium on Computer-Based 
Medical Systems, 2007, pp 41-46 
8. Sluimer I, Schilham A, et al. (2006) Computer Analysis of Computed 
Tomography Scans of the Lung: A Survey, IEEE Transactions on 
Medical Imaging vol.25(4), 2006, pp 385-405 
9. Webb W R, Muller N L, Naidich D P (1993) Standardized terms for 
high-resolution computed tomography of the lung: a proposed 
glossary. J Thoracic Imaging 8:167–185 
10. Haralick R M (1979), Textural Features for Image Classification, 
Proc. of the IEEE vol.67(5), 1979, pp 786-804  
11. Albregtsen F (1995) Statistical texture measures computed from gray 
level run-length matrices. University of Oslo 
12. Galloway M M (1975) Texture analysis using gray level run lengths. 
Comput. Graphics Image Process 4:172–179 
13. Chu A, Sehgal C M, Greenleaf J F (1990) Use of gray value 
distribution of run lengths for texture analysis. Pattern Recognition 
Lett 11:415–420 
14. Dasarathy B R, Holder E B (1991) Image characterizations based on 
joint gray-level run-length distributions. Pattern Recognition Lett 
12:497–502 
15. Vapnik V (1995) The Nature of Statistical Learning Theory. New 
York, Springer-Verlag 
16. Kim H, Howland P, Park H (2006) Dimension Reduction in Text 
Classification with Support Vector Machines. J Machine Learning 
Research 6:37–53 
 
Author: Verónica Maria Vasconcelos 
Institute: Instituto Superior de Engenharia de Coimbra 
Street: Rua Pedro Nunes – Quinta da Nora 
City: Coimbra 
Country: Portugal 
Email:  veronica@isec.pt 
 
 
 
 


Study Lung Tool: A Way to Understand HRTC Lung Parenchyma

http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=FD4337EC909719D6CB1D3B5B524E7D86?doi=10.1.1.1050.783&rep=rep1&type=pdf

Study Lung Tool: A Way to Understand HRTC Lung Parenchyma

Abstract

Similar works

Full text

Available Versions

CiteSeerX