AUTOMATIC CLASSIFICATION OF LUNG TISSUE PATTERNS IN HRCT IMAGES OF PATIENTS AFFECTED WITH ILD

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TOLOUEE AZAR; ABRISHAMI MOGHADDAM HAMID; GITY MASUMEH

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Journal: SIGNAL AND DATA PROCESSING Year:2009 | Volume:- | Issue:2 (SERIAL 12) Page(s): 27-38

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Title

AUTOMATIC CLASSIFICATION OF LUNG TISSUE PATTERNS IN HRCT IMAGES OF PATIENTS AFFECTED WITH ILD

Author(s)

TOLOUEE AZAR | ABRISHAMI MOGHADDAM HAMID | GITY MASUMEH | Issue Writer Certificate

Keywords

LUNG SEGMENTATIONQ3

HRCT IMAGESQ3

INTERSTITIAL LUNG DISEASESQ3

DISCRETE WAVELET FRAMESQ3

ROTATED WAVELET FRAMESQ3

FUZZY KNN CLASSIFIERQ3

PFT PARAMETERSQ3

Abstract

Automatic classification of lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected by INTERSTITIAL LUNG DISEASES (ILD) is an important stage in the construction of a computer-aided diagnosis system. In this study, we propose a new image based system for classification of lung tissue patterns. The proposed system comprises three stages. In the first stage, the parenchyma region in HRCT lung images is separated using a set of thresholding, filtering and morphological operators. In the second stage, two sets of over complete wavelet filters, namely DISCRETE WAVELET FRAMES and ROTATED WAVELET FRAMES are utilized to extract features from defined regions of interest (ROIs) within parenchyma. Then, in the third stage, the fuzzy k-nearest neighbor algorithm is employed to perform the pattern classification. The proposed method is tested for classifying four different lung tissue patterns (ground glass, honey combing, reticular and normal) selected from a database of 340 images from 17 subjects. After applying our technique to classify these patterns in isolated ROIs, we extend the classification scheme to the whole lung in order to produce quantitative scores of abnormalities in lung parenchyma of patients. The performance of the proposed method is compared with two state-of-the-art texture based methods for lung tissue characterization and is also validated against experienced observers. The average kappa statistic of the agreement between two radiologists and the computer was found to be 0.6543 whereas the average kappa statistic for the inter-observer agreement was 0.6848. We also conducted an experiment to show the correlation between Pulmonary Function Test parameters and quantitative scores of computerized system. Results show that the extent of HRCT findings correlates significantly with functional impairment. The computer system is shown to approach the performance of the expert observers in diagnosing regions of interest and can help to produce objective measures of abnormal patterns in lung HRCT IMAGES.

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APA: Copy

TOLOUEE, AZAR, ABRISHAMI MOGHADDAM, HAMID, & GITY, MASUMEH. (2009). AUTOMATIC CLASSIFICATION OF LUNG TISSUE PATTERNS IN HRCT IMAGES OF PATIENTS AFFECTED WITH ILD. SIGNAL AND DATA PROCESSING, -(2 (SERIAL 12)), 27-38. SID. https://sid.ir/paper/160808/en

Vancouver: Copy

TOLOUEE AZAR, ABRISHAMI MOGHADDAM HAMID, GITY MASUMEH. AUTOMATIC CLASSIFICATION OF LUNG TISSUE PATTERNS IN HRCT IMAGES OF PATIENTS AFFECTED WITH ILD. SIGNAL AND DATA PROCESSING[Internet]. 2009;-(2 (SERIAL 12)):27-38. Available from: https://sid.ir/paper/160808/en

IEEE: Copy

AZAR TOLOUEE, HAMID ABRISHAMI MOGHADDAM, and MASUMEH GITY, “AUTOMATIC CLASSIFICATION OF LUNG TISSUE PATTERNS IN HRCT IMAGES OF PATIENTS AFFECTED WITH ILD,” SIGNAL AND DATA PROCESSING, vol. -, no. 2 (SERIAL 12), pp. 27–38, 2009, [Online]. Available: https://sid.ir/paper/160808/en

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