20-06-2014, 12:14 PM
MRI Brain Image Enhancement Using
Filtering Techniques
[attachment=65096]
Abstract
Magnetic Resonance Image is one of the best technologies currently being used for diagnosing
brain cancer at advanced stages. This paper proposes a novel approach for the MRI image enhancement,
which is based on the Modified Tracking Algorithm, Histogram Equalization and Center Weighted
Median (CWM) filter. This method consists of two approaches. The first approach is applying the
modified tracking algorithm to remove the film artifacts, labels and skull region and then applying the
Histogram Equalization and Center Weighted Median (CWM) filter techniques separately to enhance the
images.
INTRODUCTION
Brain tumor is one of the major causes for the increase in Mortality among people. A tumor is an abnormal
growth caused by cells reproducing themselves in an uncontrolled manner. This year 2012, an estimated 22,910
adults (12,630 men and 10,280 women) in the United States will be diagnosed with primary malignant tumors of
the brain and spinal cord. It is estimated that 13,700 adults (7,720 men and 5,980 women) will die from this
disease this year. Brain tumors are the tenth most common cause of cancer death in women. About 4,000
children and teens will be diagnosed with a brain or central nervous system tumor this year. More than half of
these are in children younger than 15. About 20% to 40% of patients with other types of cancer will have it
spread to the brain. The most common primary cancers that spread to the brain are lung, breast, unknown
primary, melanoma, and colon cancer [10].
The tumor is divided in to two categories: Primary brain tumor and secondary tumor. A primary malignant
brain tumor is one that originates in the brain itself although these tumors often shed cancerous cells to other
sites in the central nervous system and spread to other parts of the body. A secondary or metastatic brain tumor
occurs when cancer cells spread to the brain from a primary cancer in another part of the body. Detection of
brain tumor requires high-resolution brain MRI. Most Medical Imaging Studies and detection conducted using
MRI, Positron Emission Tomography (PET) and Computed tomography (CT) Scan. Now a days MRI systems
are very important in medical image analysis. The MRI image shows the clear distinction between the tissues,
bones and fluid, so it makes easy to distinguish the tumor part from the image. In order to find the tumor part
efficiently the MRI image should be enhanced properly.
Wide range of image processing techniques [1-5] has been developed for image enhancement, segmentation,
restoration and classification. Image enhancement refers to emphasis, or sharpening, of image features such as
edges, boundaries, or contrast to make a graphic display more useful for display and analysis. Image
enhancement consists of gray level and contrast manipulation, noise reduction, edge crispening, and sharpening,
filtering, interpolation and magnification[6-7]. For processing the MRI brain images the image should be
preprocessed to remove the film artifacts, labels and the Skull regions. Many papers given in the literature [11-
12] explains about the tracking algorithm.Weighted median (WM) filters are a the extension of median filters,
which exploit not only rank-order information but also spatial information of input signal. Among WM filters,
center weighted median (CWM) filters are very special because of its simplicity and perfectness. This is
because, first, CWM filters are the simplest WM filters and the easiest to design and implement. Second, CWM
filters are better understood theoretically.
This paper is organized as follows: section 2 presents image acquisition, section 3 presents preprocessing
technique, section 4 presents histogram modeling for brain MRI images, section 5 presents the center weighted
median filter, section 6 presents experiments and results techniques and the section 7 presents summary and
conclusions.
IMAGE ACQUISITION
To access the real medical images for carrying out research is a very difficult due to heavy technical hurdles.
The MRI data is obtained from the Brain Web Database at the McConnell Brain Imaging center of the Montreal
Neurological Institute (MNI), McGillUniversity.(http://www.bic.mni.mcgill.ca/brainweb). A sample of 80 T1
weighted images are taken and used for enhancement purpose. T1- weighted images shows water darker and
the fat brighter. An image of a patient obtained by MRI scan is displayed as an array of Pixels (a Two
Dimensional unit based on the matrix size and the field of view) are stored. Images are three types (a) Binary
Images (b) Gray Scale Image © Color Image. In this paper, we consider grayscale or Intensity Images to
display an image with default size of 256 x 256. The following figure (3.1) displays a MRI Brain Image. A
grayscale Image can be specified by giving a large matrix whose entries are numbers between 0 and 255, with 0
to black and 255 to white.
PREPROCESSING TECHNIQUES
A. Removal of film artifacts
This paper presents an integrated method of the adaptive enhancement of brain tissues in two-dimensional
(2-D) MRI images. The MRI brain image consists of film artifacts or label on the MRI such as patient name, age
and marks. film artifacts that are removed using tracking algorithm .Here, starting from the first row and first
column, the intensity value of the pixels are analyzed and the threshold value of the film artifacts are found.
The threshold value, greater than that of the threshold value is removed from MRI. The high intensity values of
film artifacts are removed from MRI brain image. The following figures explain the process of preprocessing
stage.
CENTER WEIGHTED MEDIAN FILTER
In medical image processing, necessary to perform a high degree of noise reduction in an image before
performing high-level processing steps. Median Filter can remove the noise, high frequency components from
MRI without disturbing the edges and it is used to reduce’ salt and pepper’ noise. This technique calculates the
median of the surrounding pixels to determine the new (denoised) value of the pixel. A median is calculated by
sorting all pixel values by their size, then selecting the median value as the new value for the pixel. The amount
of pixels which should be used to calculate the median [8].
A weighted median filter controlled by evidence fusion is proposed for removing noise from MRI brain
images with contrast. It has a great potential for being used in rank order filtering and image processing. The
weights of the filter are set based on intensity value of the pixels in the MRI image. Here we used four weights
such as 0, 0.1, 0.2 and 0.3. if the intensity value of the pixel is 0 then consider the weight of the pixel is 0. Else if
the range of pixel intensity between 1-100 then the weight is 0.1,else if the range of pixel intensity between
101-200 then the weight is 0.2, otherwise the weight of the pixel is 0.3. the above weights are multiplied with
pixel intensity .after that the median filter is applied for calculate weighted median filter.
The center weighted median (CWM) filter, which is a weighted median filter giving more weight only to the
central value of each window, is used. This filter can preserve image details while suppressing additive white
and/or impulsive-type noise. The statistical properties of the CWM filter are analyzed. It is shown that the CWM
filter can outperform the median filter [13-16]. It is shown that the CWM filter is an excellent detail preserving
smoother that can suppress signal-dependent noise as well as signal-independent noise [9].
In a CWM filter, the center sample is assigned a larger weight, i.e. w(0, 0) = 2K + 1
where K >= 0, and all other non-zero weights are equal to one, i.e. w(i,j) = 1 for i not equal to 0 and j not
equal to 0. K is a nonnegative integer.
A CWM filter is completely specified by two parameters, the window size and the center weight. The
filtering behavior of a CWM filter will thus be controlled by these two parameters. Denote a CWM filter with
center weight 2K + 1 by CWM(M; 2K + 1), where M is the number of the samples in the window, e.g., M = (2N
+ 1) x (2N + 1) for a connected square window.
EXPERIMENTS AND RESULTS
This algorithm has been tested with various types of textured images acquired in the database. This proposed
approach has been implemented by using mat lab. The experimental results are tested in Intel Pentium IV 2.4
GHz processor with 256 MB RAM. It is very difficult measure to the improvement of the enhancement
objectively. If the enhanced image can make observer perceive the region of interest better, then we can say that
the original image has been improved. In order to compare different enhancement algorithms, it is better to
design some methods for the evaluation of enhancement objectively. The statistical measurements such as
variance or entropy can always measure the local contrast enhancement; however that show no consistency for
the MRI. Performance of the Median filter, Weighted Median filter, and center weighted median filters are
analysed and evaluated. Table1 shows the performance of listed filters.
Filtering Techniques
[attachment=65096]
Abstract
Magnetic Resonance Image is one of the best technologies currently being used for diagnosing
brain cancer at advanced stages. This paper proposes a novel approach for the MRI image enhancement,
which is based on the Modified Tracking Algorithm, Histogram Equalization and Center Weighted
Median (CWM) filter. This method consists of two approaches. The first approach is applying the
modified tracking algorithm to remove the film artifacts, labels and skull region and then applying the
Histogram Equalization and Center Weighted Median (CWM) filter techniques separately to enhance the
images.
INTRODUCTION
Brain tumor is one of the major causes for the increase in Mortality among people. A tumor is an abnormal
growth caused by cells reproducing themselves in an uncontrolled manner. This year 2012, an estimated 22,910
adults (12,630 men and 10,280 women) in the United States will be diagnosed with primary malignant tumors of
the brain and spinal cord. It is estimated that 13,700 adults (7,720 men and 5,980 women) will die from this
disease this year. Brain tumors are the tenth most common cause of cancer death in women. About 4,000
children and teens will be diagnosed with a brain or central nervous system tumor this year. More than half of
these are in children younger than 15. About 20% to 40% of patients with other types of cancer will have it
spread to the brain. The most common primary cancers that spread to the brain are lung, breast, unknown
primary, melanoma, and colon cancer [10].
The tumor is divided in to two categories: Primary brain tumor and secondary tumor. A primary malignant
brain tumor is one that originates in the brain itself although these tumors often shed cancerous cells to other
sites in the central nervous system and spread to other parts of the body. A secondary or metastatic brain tumor
occurs when cancer cells spread to the brain from a primary cancer in another part of the body. Detection of
brain tumor requires high-resolution brain MRI. Most Medical Imaging Studies and detection conducted using
MRI, Positron Emission Tomography (PET) and Computed tomography (CT) Scan. Now a days MRI systems
are very important in medical image analysis. The MRI image shows the clear distinction between the tissues,
bones and fluid, so it makes easy to distinguish the tumor part from the image. In order to find the tumor part
efficiently the MRI image should be enhanced properly.
Wide range of image processing techniques [1-5] has been developed for image enhancement, segmentation,
restoration and classification. Image enhancement refers to emphasis, or sharpening, of image features such as
edges, boundaries, or contrast to make a graphic display more useful for display and analysis. Image
enhancement consists of gray level and contrast manipulation, noise reduction, edge crispening, and sharpening,
filtering, interpolation and magnification[6-7]. For processing the MRI brain images the image should be
preprocessed to remove the film artifacts, labels and the Skull regions. Many papers given in the literature [11-
12] explains about the tracking algorithm.Weighted median (WM) filters are a the extension of median filters,
which exploit not only rank-order information but also spatial information of input signal. Among WM filters,
center weighted median (CWM) filters are very special because of its simplicity and perfectness. This is
because, first, CWM filters are the simplest WM filters and the easiest to design and implement. Second, CWM
filters are better understood theoretically.
This paper is organized as follows: section 2 presents image acquisition, section 3 presents preprocessing
technique, section 4 presents histogram modeling for brain MRI images, section 5 presents the center weighted
median filter, section 6 presents experiments and results techniques and the section 7 presents summary and
conclusions.
IMAGE ACQUISITION
To access the real medical images for carrying out research is a very difficult due to heavy technical hurdles.
The MRI data is obtained from the Brain Web Database at the McConnell Brain Imaging center of the Montreal
Neurological Institute (MNI), McGillUniversity.(http://www.bic.mni.mcgill.ca/brainweb). A sample of 80 T1
weighted images are taken and used for enhancement purpose. T1- weighted images shows water darker and
the fat brighter. An image of a patient obtained by MRI scan is displayed as an array of Pixels (a Two
Dimensional unit based on the matrix size and the field of view) are stored. Images are three types (a) Binary
Images (b) Gray Scale Image © Color Image. In this paper, we consider grayscale or Intensity Images to
display an image with default size of 256 x 256. The following figure (3.1) displays a MRI Brain Image. A
grayscale Image can be specified by giving a large matrix whose entries are numbers between 0 and 255, with 0
to black and 255 to white.
PREPROCESSING TECHNIQUES
A. Removal of film artifacts
This paper presents an integrated method of the adaptive enhancement of brain tissues in two-dimensional
(2-D) MRI images. The MRI brain image consists of film artifacts or label on the MRI such as patient name, age
and marks. film artifacts that are removed using tracking algorithm .Here, starting from the first row and first
column, the intensity value of the pixels are analyzed and the threshold value of the film artifacts are found.
The threshold value, greater than that of the threshold value is removed from MRI. The high intensity values of
film artifacts are removed from MRI brain image. The following figures explain the process of preprocessing
stage.
CENTER WEIGHTED MEDIAN FILTER
In medical image processing, necessary to perform a high degree of noise reduction in an image before
performing high-level processing steps. Median Filter can remove the noise, high frequency components from
MRI without disturbing the edges and it is used to reduce’ salt and pepper’ noise. This technique calculates the
median of the surrounding pixels to determine the new (denoised) value of the pixel. A median is calculated by
sorting all pixel values by their size, then selecting the median value as the new value for the pixel. The amount
of pixels which should be used to calculate the median [8].
A weighted median filter controlled by evidence fusion is proposed for removing noise from MRI brain
images with contrast. It has a great potential for being used in rank order filtering and image processing. The
weights of the filter are set based on intensity value of the pixels in the MRI image. Here we used four weights
such as 0, 0.1, 0.2 and 0.3. if the intensity value of the pixel is 0 then consider the weight of the pixel is 0. Else if
the range of pixel intensity between 1-100 then the weight is 0.1,else if the range of pixel intensity between
101-200 then the weight is 0.2, otherwise the weight of the pixel is 0.3. the above weights are multiplied with
pixel intensity .after that the median filter is applied for calculate weighted median filter.
The center weighted median (CWM) filter, which is a weighted median filter giving more weight only to the
central value of each window, is used. This filter can preserve image details while suppressing additive white
and/or impulsive-type noise. The statistical properties of the CWM filter are analyzed. It is shown that the CWM
filter can outperform the median filter [13-16]. It is shown that the CWM filter is an excellent detail preserving
smoother that can suppress signal-dependent noise as well as signal-independent noise [9].
In a CWM filter, the center sample is assigned a larger weight, i.e. w(0, 0) = 2K + 1
where K >= 0, and all other non-zero weights are equal to one, i.e. w(i,j) = 1 for i not equal to 0 and j not
equal to 0. K is a nonnegative integer.
A CWM filter is completely specified by two parameters, the window size and the center weight. The
filtering behavior of a CWM filter will thus be controlled by these two parameters. Denote a CWM filter with
center weight 2K + 1 by CWM(M; 2K + 1), where M is the number of the samples in the window, e.g., M = (2N
+ 1) x (2N + 1) for a connected square window.
EXPERIMENTS AND RESULTS
This algorithm has been tested with various types of textured images acquired in the database. This proposed
approach has been implemented by using mat lab. The experimental results are tested in Intel Pentium IV 2.4
GHz processor with 256 MB RAM. It is very difficult measure to the improvement of the enhancement
objectively. If the enhanced image can make observer perceive the region of interest better, then we can say that
the original image has been improved. In order to compare different enhancement algorithms, it is better to
design some methods for the evaluation of enhancement objectively. The statistical measurements such as
variance or entropy can always measure the local contrast enhancement; however that show no consistency for
the MRI. Performance of the Median filter, Weighted Median filter, and center weighted median filters are
analysed and evaluated. Table1 shows the performance of listed filters.