Lung cancer Detection in matlab
Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages, where the time factor is very important to discover the abnormality issues in target images, especially in various cancer tumours such as lung cancer, breast cancer, etc. Image quality and accuracy is the core factors of this research, image quality assessment as well as improvement are depending on the enhancement stage where low pre-processing techniques is used based on Gabor filter within Gaussian rules. Following the segmentation principles, an enhanced region of the object of interest that is used as a basic foundation of feature extraction is obtained. Relying on general features, a normality comparison is made. In this research, the main detected features for accurate images comparison are pixels percentage and mask-labelling. Keywords Cancer Detection; Image processing; Feature extraction; Enhancement Watershed; Masking.
Some of the methods used:
Gabor filter
Image presentation based on Gabor function constitutes an excellent local and multiscale decomposition in terms of logons that are simultaneously (and optimally) localization in space and frequency domains [5]. A Gabor filter is a linear filter whose impulse response is defined by a harmonic function multiplied by a Gaussian function. Because of the multiplication-convolution property (Convolution theorem), the Fourier transform of a Gabor filter's impulse response is the convolution of the Fourier transform of the harmonic function and the Fourier transform of the Gaussian function [6]. Figure 2 describes (a) the original image and (b) the enhanced image using Gabor Filter
Thresholding approach
Thresholding is one of the most powerful tools for image segmentation. The segmented image obtained from thresholding has the advantages of smaller storage space, fast processing speed and ease in manipulation, compared with gray level image which usually contains 256 levels. Therefore, thresholding techniques have drawn a lot of attention during the past 20 years [10]. Thresholding is a non-linear operation that converts a gray-scale image into a binary image where the two levels are assigned to pixels that are below or above the specified threshold value. In this research, Otsu’s method that uses (gray thresh) function to compute global image threshold is used. Otsu’s method is based on threshold selection by statistical criteria.
Sample Matlab Implementation
Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages, where the time factor is very important to discover the abnormality issues in target images, especially in various cancer tumours such as lung cancer, breast cancer, etc. Image quality and accuracy is the core factors of this research, image quality assessment as well as improvement are depending on the enhancement stage where low pre-processing techniques is used based on Gabor filter within Gaussian rules. Following the segmentation principles, an enhanced region of the object of interest that is used as a basic foundation of feature extraction is obtained. Relying on general features, a normality comparison is made. In this research, the main detected features for accurate images comparison are pixels percentage and mask-labelling. Keywords Cancer Detection; Image processing; Feature extraction; Enhancement Watershed; Masking.
Some of the methods used:
Gabor filter
Image presentation based on Gabor function constitutes an excellent local and multiscale decomposition in terms of logons that are simultaneously (and optimally) localization in space and frequency domains [5]. A Gabor filter is a linear filter whose impulse response is defined by a harmonic function multiplied by a Gaussian function. Because of the multiplication-convolution property (Convolution theorem), the Fourier transform of a Gabor filter's impulse response is the convolution of the Fourier transform of the harmonic function and the Fourier transform of the Gaussian function [6]. Figure 2 describes (a) the original image and (b) the enhanced image using Gabor Filter
Thresholding approach
Thresholding is one of the most powerful tools for image segmentation. The segmented image obtained from thresholding has the advantages of smaller storage space, fast processing speed and ease in manipulation, compared with gray level image which usually contains 256 levels. Therefore, thresholding techniques have drawn a lot of attention during the past 20 years [10]. Thresholding is a non-linear operation that converts a gray-scale image into a binary image where the two levels are assigned to pixels that are below or above the specified threshold value. In this research, Otsu’s method that uses (gray thresh) function to compute global image threshold is used. Otsu’s method is based on threshold selection by statistical criteria.
Sample Matlab Implementation
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Thanks for this great project, I am doing Lungs detection for COPD. So if possible kindly do send me the project so I will do my research with the students group.
ReplyDeleteRegards,
Syed Faisal Ali
Asst. Prof.
Department of Computer Science
Usman Institute of Technology
NED University
sfaisal@uit.edu