Detailed Description About The Lung Cancer Detection Biology Essay

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This project provides us the detailed description about the lung cancer detection. The nodules in the peripherals of the lung fields are detected by the study of the following filters like median filter, Adaptive wiener filter, Gabor filter and also by the wavelet thresholding techniques. The main criteria in the study of the above filters helps serving serving better computer aided diagnosis of lung carcinoma tissue images in which there is a huge ability in the identification of the early tumors for the sake of the successful treatment. Huge amount of the analysis are required because both the cancerous as well as the non cancerous regions appear with the small change on an X Ray image. Therefore there is a need to differentiate the cancer nodules from the non cancer nodules to overcome the small changes and for the purpose of the accurate detection of the cancer nodules.

In the world this lung cancer is the leading cancer which are giving rise to the deaths. To improve the survival rate of the cancer patients it is important to detect and treat cancer in the early stages. When the lung cells grow at the uncontrollable rate that may gives rise to the development of the cancer. Inside the lungs the abnormal tissue masses are present and so called the tumors and these are of the following two types they are non cancerous that is benign tumor and the cancerous that is by name malignant tumor. The diagnosis of cancer includes X-rays chest films ,CT scan ,MRI, isotope ,bronchoscope. It is important to develop a reliable Computer Aided Diagnosis (CAD) system for detection of lung cancer. Because it is important to detect lung cancer and also to diagnise lung cancer in chest x-ray images as there are many tissues overlapping each other in the X-ray chest film and also there are many objects obscuring the cancer tissue such as ribs, blood vessels and other anatomic structures.

So therefore to overcome the above mentioned problems it is important to develop a reliable computer aided diagnosis system for the detection of the lung cancer. So therefore in order to separate the tumor region from the chest cavity, Ribs and there are many objects obscuring the cancer tissues such as ribs blood vessel and other anatomic structures. CAD involves the use of computers to bring suspicious areas on a medical image to the attention of radiologist. CAD systems for lung cancer detection. So therefore there are two stages in which the processing takes place. That is in the first stage a set of the potential nodule is detected in which the initial processing of the image takes place. And in this process the original image is set for the purpose of the enhancement, Smoothing and also the noise removal takes place.

It not only enhances the image but also removes the unwanted background from the image. And now moving to the second stage of the process in this it classifies the suspicious regions into positive as well as the negative regions. And now moving towards the positive regions here the suspicion of finding possible tumors in that region. That is the stage where the image gets segmented and got classified according to the requirements or the wanted or the required features. The CAD systems are evaluated in terms of Relative Operation Characteristic (ROC) analysis, sensitivity, accuracy and specificity. The results of these characteristics have improved the radiologist's performance significantly.

The original image obtained from the diagnosis are got processed by the various filtering methodologies and deals with the image processing part of the CAD systems. After the acquisition of the image it normally leads to the degradation. Due to some mechanical problems like blurring, inappropriate illumination, motion effects and noise the quality of the digitized image can be differed from the original. Generally linear and the non linear filters, Local or the global filters are used for the purpose of the image enhancement. Therefore The main goal of image enhancement is to produce a visually pleasing image from the original recorded image. Inverse or the weiner filters are used for the purpose of the de blurring techniques.

DIFFERENT FILTERS USE DFOR THE PROCESSING OF AN IMAGE

Median filter

It considers the each pixel in the image and by the by simultaneously verifies the neighboring pixels to decide whether it is representative to the surrounding or not. In this instead of simply replacing the with the calculation of mean of the neighboring pixel values It calculates the median by first sorting all the pixel values from the surrounding neighborhood into numerical order and then replacing the pixel being considered with the middle pixel value. It is very effective in the removal of the noise from the images where less than the half of the pixels in a smoothing neighborhood have been effected. It allows a great deal of the high spatial frequency detail to pass.

In this median filter it is necessary to find all the values from the neighborhood in a numerical order with the fast sorting algorithms for example considering the quick sort. The basic algorithm is somehow enhanced for the purpose of the speed. Whenever the image is set for the thresholding there may be some interference of the noise due to the presence of the minute grey scale variations in the image. And that noise is the salt and pepper noise. Therefore the median filter is used for the removal of the afore mentioned salt and pepper noise from the image without effecting the sharpness of the original image. These are the popular filters used for the purpose of the noise reduction, Linear smoothing and the less blurring effect. It offers a great deal to pass a high spatial frequency while effective in removing the noise from the images there by affecting less than half of the image pixels in smoothing neighborhood.

Bright or dark high-frequency features appearing indiscriminately over the image characterizes the impulse noise. Statistically, impulse noise falls well outside the peak of the distribution of any given pixel neighborhood, so the median filter is appropriate to find out where impulse noise is not present and if it is present then removes it by exclusion. The mean is calculated by taking into account the median of a list of sample values and sorting them in any order randomly, and then pick the central value. Suppose if the list is even sized then consider the mean between the two central values. If the list of values has a strong central tendency, which manifested as a single, well defined peak on the histogram. Then the median is said to be a good estimator of the peak position. If the distribution has two peaks, or if it is has no central peak, then the median is normally meaningless.

Adaptive weiner filters

we consider the adaptive Wiener filtering of noisy images and image sequences. We begin by using an adaptive weighted averaging (AWA) approach to estimate the second-order statistics required by the Wiener filter. Experimentally, the resulting Wiener filter is improved by about 1dB in the sense of peak-to-peak SNR (PSNR). Also, the subjective improvement is significant in that the annoying boundary noise, common with the traditional Wiener filter, has been greatly suppressed.

The second, and more substantial part extends the AWA concept to the wavelet domain. The proposed AWA wavelet Wiener filter is superior to the traditional wavelet Wiener filter by about 0.5dB (PSNR). Images and image sequences are frequently corrupted by noise in the acquisition and transmission phases. The goal of de noising is to remove the noise, both for aesthetic and compression reasons, while retaining as much as possible the important signal features. Very commonly, this is achieved by approaches such as Wiener filtering which is the optimal estimator (in the sense of mean squared error (MSE)) for stationary Gaussian process.

Since natural images typically consist of smooth areas, textures, and edges, they are clearly not globally stationary. Similarly, non stationarity in video may further be caused by inter-frame motion. However, image and video can be reasonably treated as being locally stationary.

These insights have motivated the design of adaptive Wiener filters, called local linear minimum mean square error (LLMMSE) filters. The LLMMSE filter proposed. Extensively used for video de noising, is successful in the sense that it effectively removes noise while preserving important image features. However the Lee filter suffers from annoying noise around edges, due to the assumption that all samples within a local window are from the same ensemble. This assumption is invalidated if there is a sharp edge within the window, for example; in particular, the sample variance near an edge will be biased large because samples from two different ensembles are combined, and similarly the sample mean will tend to smear. The main problem, then, is how to effectively estimate local statistics.

The adaptive Wiener filters are similar to median filters which are used for the application of the process of tailored de-ionizing from the local neighborhood of each image pixel based on the statistical estimation. The smoothing amount performed by this filter invariantly depends on the mean of local image and variance around the pixel of interest .The Wiener filter is popular linear filter but it adaptive implementation preserves better the high frequency parts of the image. Because of its low-pass characteristics the standard formulation of the Wiener filter has met limited success in image processing, which give rise to unacceptable blurring of lines and edges. The reason why the Wiener filter blurs the image significantly is that a fixed filter is used throughout the entire image i.e. the filter is space invariant. If the signal is a realization of a non-Gaussian process such as in natural images, the Wiener filter is outperformed by nonlinear estimators.

Wavelet thresholding

An image is often corrupted by noise during the rtime of acquition and transmission. Image denoising is used to remove the additive noise while retaining as much as possible the important signal features. In the recent years there has been a fair amount of research on wavelet thresholding and threshold selection for signal de-noising because wavelet provides an appropriate basis for separating noisy signal from the image signal. The motivation is that as the wavelet transform is good at energy compaction, the small coefficient are more likely due to noise and large coefficient due to important signal features. These small coefficients can be thresholded without affecting the significant features of the image.

Thresholding is a simple non-linear technique, which operates on one wavelet coefficient at a time. In its most basic form, each coefficient is thresholded by comparing against threshold, if the coefficient is smaller than threshold, set to zero; otherwise it is kept or modified. Replacing the small noisy coefficients by zero and inverse wavelet transform on the result may lead to reconstruction with the essential signal characteristics and with less noise. There has been much research on finding thresholds, however few are specifically designed for images. In this paper, a near optimal threshold estimation technique for image de noising is proposed which is sub band dependent i.e. the parameters for computing the threshold are estimated from the observed data, one set for each sub band.

Image enhancement is very much important for the better visualization of the object. Therefore for the pre processing the removal of the noise from the original image is required. Generally most often spatial filters are used for the purpose of the removal of the noise. These spatial filters are used for the smoothening of the data for the reduction of the noise. Many spatial filters are used for the reduction of the noise from an image. But at the time of the reduction of the noise there the problem is arised that is the blurring effect. But the most optimal method used for the removal of the noise is the wavelet threshold method it provides the excellent performance for the purpose of the noise removal. It is one of the method for the removal of the noise. The method of wavelet thresholding has been used for the purpose of the de noising in the medical images. Thresholding produces a segmentation that yields all the pixels that, in principle, belong to the object or objects of interest in an image.

The main idea of the wavelet thresholding is to convert the data into the wavelet basis in which it separates in to two that is large coefficients as well as the small coefficients. The large coefficients are termed as the signal and the small coefficients are termed as the noise. Therefore by suitably changing or modifying these coefficients the noise can be removed from the data. Therefore this wavelet thresholding technique is simple, Fast and efficient method for the suppression of the corrupting noise that the noise which is disturbing the pure signal and also to preserve the edges well.

Gabor filter

These Gabor filter is termed as a linear filter mainly used for the purpose of the edge detection. The human visual system is similar to that of the gabor filter representation and the frequency orientation and is mainly used for the purpose of the texture representation and it has been particularly appropriate for the texture representation and discrimination. The Gaussian kernel function modulated by a sinusoidal plane wave is termed as a 2 dimensional Gabor filter in the spatial domain.

Therefore the impulse response of the Gabor filter is obtained by the multiplication of the harmonic function and the Gaussian function which was stated in the convolution theorem and is also called multiplication convolution property. Due to 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.

Gabor filters are directly related to Gabor wavelets, since they can be designed for a number of dilations and rotations. However, in general, expansion is not applied for Gabor wavelets, since this requires computation of bi-orthogonal wavelets, which may be very time-consuming. Therefore, usually, a filter bank consisting of Gabor filters with various scales and rotations is created. The filters are convolved with the signal, resulting in a so-called Gabor space. This process is closely related to processes in the primary visual cortex.

The Gabor space is very useful in image processing applications such as optical character recognition, iris recognition and fingerprint recognition. Relations between activations for a specific spatial location are very distinctive between objects in an image. Furthermore, important activations can be extracted from the Gabor space in order to create a sparse object representation.

Gabor filter is a linear filter whose impulse is obtained by the multiplication of harmonic function and the Gaussian function. Gabor filters provides promising image de-noising and texture analysis. Due to its perpetual relevance the multi-channel filtering is considered as an excellent preprocessing choice for image registration. Gabor filters enhance the detection of the nodules by emphasizing their spatial frequency components and rejecting other components. These filters are used for orientation responses of simple cells in the primary visual cortex and modeling of the spatial frequency .These filters are also proven to be optimal in the sense of minimizing the joint two dimensional uncertainties in space and frequency. Gabor filters are essentially band pass filters as they are derived from wavelet based. Gabor filters are related to Gabor wavelets in a direct way because can be designed for a number of rotations and dilations. Nevertheless the expansion is not applied for Gabor wavelets.

Computer aided diagnosis

These are procedures in medicine that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI (Magnetic resonance for imaging), and Ultrasound diagnostics yield a great deal of information, which the radiologist has to analyze and evaluate comprehensively in a short time. CAD systems help scan digital images, e.g. from computed tomography, for typical appearances and to highlight conspicuous sections, such as possible diseases.

CAD is a relatively young interdisciplinary technology combining elements of artificial intelligence and digital image processing with radiological image processing. A typical application is the detection of a tumor. For instance, some hospitals use CAD to support preventive medical check-ups in mammography (diagnosis of breast cancer), the detection of polyps in the colon, and lung cancer.

It is mainly based on the high pattern recognition. X-ray images are scanned for suspicious structures. Normally a few thousand images are required to optimize the algorithm. Digital image data are copied to a CAD server in a DICOM-format and are prepared and analyzed in several steps. And as follows:

Reducing the artifacts.

Image noise reduction.

Normalizing the image quality for the clearing of the different basic conditions.

Segmenting or the differentiation of the different structures of the image.

Matching with the data bank.

Compactness.

Size and location.

Reference to the close by structures.

Evaluation.

CAD systems seek to highlight suspicious structures. Today's CAD systems cannot detect 100% of pathological changes. The sensitivity can be up to 90% depending on system and application. A correct hit is termed a True Positive (TP), while the incorrect marking of healthy sections constitutes a False Positive (FP). The less FPs indicated, the higher the specificity is. A low specificity reduces the acceptance of the CAD system because the user has to identify all of these wrong hits. The FP-rate in lung overview examinations (CAD Chest) could be reduced to 2 per examination. In other segments (e.g. CT lung examinations) the FP-rate could be 25 or more.

Detection performance was evaluated with Receiver Operating Characteristic (ROC) Analysis which is an analytical procedure for measuring the accuracy of the system. This characteristic may be used to differentiate between true- positive probability and false-positive probability. The desirable index of accuracy and the appropriate basis for an index of efficiency are provided by the ROC characteristic.

The sensitivity is the other evaluation method for CAD which is the number of the correctly classified suspected nodule area (SNA). Out of the number of the positive SNA's. Actually the identification is based on the number of corrected diagnosed negative SNAs out of all negative SNAs .The Accuracy is the total number of correctly diagnosed SNAs out of total number of SNAs.

Therefore from the above project describes the problem of detection of the cancer nodules in X-Ray, CT images because of some of the overlapping of the anatomical structures or the low quality of the image or the Obstructing anatomical structures. Therefore CAD (computer aided design) is the most fast and the efficient method for the detection of the lung cancer nodules and provides the better decision making prospect for radiologists. Actually it is a two stage approach in which That is in the first stage a set of the potential nodule is detected in which the initial processing of the image takes place. And in this process the original image is set for the purpose of the enhancement, Smoothing and also the noise removal takes place.

It not only enhances the image but also removes the unwanted background from the image. And now moving to the second stage of the process in this it classifies the suspicious regions into positive as well as the negative regions. And now moving towards the positive regions here the suspicion of finding possible tumors in that region. That is the stage where the image gets segmented and got classified according to the requirements or the wanted or the required features. The CAD systems are evaluated in terms of Relative Operation Characteristic (ROC) analysis, sensitivity, accuracy and specificity. The results of these characteristics have improved the radiologist's performance significantly.

Results

PSNR values

PSNR and MSE values for wavelets (ST) are

MSE = 0.0011 PSNR = 77.8484

PSNR and MSE values for Wiener filter are

MSE = 0.0792 PSNR = 59.1424

PSNR and MSE values for Gabor filter are

MSE = 0.0018 PSNR = 75.5826

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