Open AccessIdentification of emphysema patterns in high resolution computed tomography images
Author(s) -
Musibau A. Ibrahim,
Oladotun A. Ojo,
Peter A. Oluwafisoye
Publication year - 2017
Publication title -
journal of biomedical engineering and informatics
Language(s) - English
Resource type - Journals
eISSN - 2377-939X
pISSN - 2377-9381
DOI - 10.5430/jbei.v4n1p16
Subject(s) - artificial intelligence , fractal dimension , pattern recognition (psychology) , segmentation , high resolution computed tomography , fractal analysis , computer science , metric (unit) , box counting , fractal , computer vision , computation , computed tomography , mathematics , radiology , medicine , algorithm , mathematical analysis , operations management , economics
Fractal dimension (FD) is a very useful metric for the analysis of image structures with statistically self-similar properties. It has applications in areas such as texture segmentation, shape classification and analysis of medical images. Several approaches can be used for calculating the fractal dimension of digital images; the most popular method is the box-counting method. It is also very challenging and difficult to classify patterns in high resolution computed tomography images (HRCT) using this important descriptor. This paper applied the Holder exponent computation of the local intensity values for detecting the emphysema patterns in HRCT images. The absolute differences between the normal and the abnormal regions in the images are the key for a successful classification of emphysema patterns using the statistical analysis. The results obtained in this paper demonstrated the effectiveness of the predictive power of the features extracted from the Holder exponent in the analysis and classification of HRCT images. The overall classification accuracy achieved in lung tissue layers is greater than 90%, which is an evidence to prove the effectiveness of the methods investigated in this paper.