Texture Analysis in Lung HRCT Images

Tolouee, Azar; Abrishami-Moghaddam, Hamid; Garnavi, Rahil; Forouzanfar, Mohamad; Giti, M

doi:10.1109/dicta.2008.27

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…In contrast, high resolution CT provides detailed information regarding the lung parenchyma and can delineate structures down to the level of the secondary pulmonary lobule. Of particular interest are the reports on the relationship between the bronchial tree properties and texture of lung CT images [2]. This information allows for an accurate morphologic analysis of the pathologic processes affecting the bronchial tree in lung.…”

Section: Introductionmentioning

confidence: 99%

Classification of texture patterns in CT lung imaging

et al. 2011

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Since several lung diseases can be potentially diagnosed based on the patterns of lung tissue observed in medical images, automated texture classification can be useful in assisting the diagnosis. In this paper, we propose a methodology for discriminating between various types of normal and diseased lung tissue in computed tomography (CT) images that utilizes Vector Quantization (VQ), an image compression technique, to extract discriminative texture features. Rather than focusing on images of the entire lung, we direct our attention to the extraction of local descriptors from individual regions of interest (ROIs) as determined by domain experts. After determining the ROIs, we generate "locally optimal" codebooks representing texture features of each region using the Generalized Lloyd Algorithm. We then utilize the codeword usage frequency of each codebook as a discriminative feature vector for the region it represents. We compare k-nearest neighbor, support vector machine and neural network classification approaches using the normalized histogram intersection as a similarity measure. The classification accuracy reached up to 98% for certain experimental settings, indicating that our approach may potentially assist clinicians in the interpretation of lung images and facilitate the investigation of relationships among structure, texture and function or pathology related to several lung diseases.

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Section: Introductionmentioning

confidence: 99%

Classification of texture patterns in CT lung imaging

et al. 2011

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“…Physicians tend to use computed texture measures from regions of interest (ROIs) for diagnosis purposes and for eventually choosing the appropriate treatment procedure. Many techniques have been applied for the purpose of lungs texture analysis: as using the fractal dimension to exploit the fractal nature of the lung tissue structure [8][9][10], overcomplete wavelet filters -also called wavelet frames -to investigate the tissue at multiple resolutions [11,12], combining Gabor filter response with histogram features [13], and using the co-occurrence matrix [14]. A review on the various methods used in computer analysis of lung CT scans can be found in [15].…”

Section: Introductionmentioning

confidence: 99%

Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images

Al-Kadi

2010

Computerized Medical Imaging and Graphics

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Noise is one of the major problems that hinder an effective texture analysis of disease in medical images, which may cause variability in the reported diagnosis. In this paper seven texture measurement methods (two wavelet, two model and three statistical based) were applied to investigate their susceptibility to subtle noise caused by acquisition and reconstruction deficiencies in computed tomography (CT) images. Features of lung tumours were extracted from two different conventional and contrast enhanced CT image data-sets under filtered and noisy conditions. When measuring the noise in the background open-air region of the analysed CT images, noise of Gaussian and Rayleigh distributions with varying mean and variance was encountered, and Fishers' distance was used to differentiate between an original extracted lung tumour region of interest (ROI) with the filtered and noisy reconstructed versions. It was determined that the wavelet packet (WP) and fractal dimension measures were the least affected, while the Gaussian Markov random field, runlength and co-occurrence matrices were the most affected by noise. Depending on the selected ROI size, it was concluded that texture measures with fewer extracted features can decrease susceptibility to noise, with the WP and the Gabor filter having a stable performance in both filtered and noisy CT versions and for both data-sets. Knowing how robust each texture measure under noise presence is can assist physicians using an automated lung texture classification system in choosing the appropriate feature extraction algorithm for a more accurate diagnosis.

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“…Various combinations of wavelet transforms, in combination with support vector machines (SVM's), were also used to discriminate among several texture patterns from patients affected by interstitial lung diseases. Two sets of over-complete wavelet filters, discrete wavelet frames (DWF) and rotated wavelet frames (RWF) were used to extract the features, which best characterise the lung tissue patterns (Tolouee et al, 2008). The system was able to successfully classify four different lung patterns with the best multi-class accuracy achieved when combining DWF and RWF.…”

Section: Introduction H I G H R E S O L U T I O N C T ( H R C T ) T Ementioning

confidence: 99%

“…The goal of computerised medical image analysis and interpretation is to detect abnormal appearance of the imaged anatomy and to assist radiologists in identifying and integrating all the useful information available in an image (Brown & McNitt-Gray, 2000). There is a growing number of computer-aided diagnosis (CAD) systems aimed at automating the analysis of lung CT images and supporting diagnosis (Uppaluri, et al, 1999;Uchiyama et al, 2003;Sluimer, 2005;Zrimec et al, 2007;Tolouee et al, 2008). Uppaluri et al (1999) presented a CAD system for detecting six lung tissue patterns using textural features.…”

Section: Introduction H I G H R E S O L U T I O N C T ( H R C T ) T Ementioning

confidence: 99%