22-11-2012, 04:07 PM
Efficient Pre-processing of USF and MIAS Mammogram Images
[attachment=40694]
Abstract:
High quality mammogram images are high resolution and large size images. Processing
these images require high computational capabilities. The transmission of these images over the net is
sometimes critical especially if the diagnosis of remote radiologists is required. In this paper, a preprocessing
technique for reducing the size and enhancing the quality of USF and MIAS mammogram
images is introduced. The algorithm analyses the mammogram image to determine if 16-bit to 8-bit
conversion process is required. Enhancement is applied later followed by a scaling process to reduce
the mammogram size. The performances of the algorithms are evaluated objectively and subjectively.
On average 87% reduction in size is obtained with no loss of data at the breast region.
INTRODUCTION
Early detection is the best way to improve breast
cancer prognosis since the causes of the disease are still
unknown. Breast cancer is the second most prevalent
cancer among women after skin cancer[1]. In addition, it
accounts for most cancer deaths coming only second to
lung cancer[1]. Currently, three methods are used for
breast cancer diagnosis: mammography, fine needle
aspirate and surgical biopsy. Mammography has a
reported malignant sensitivity which varies between 68
and 79%[2]. Fine needle aspirate depends on extracting
fluids from a breast lump and inspecting it under the
microscope. This method has a reported sensitivity
varying from 65 to 98%[2]. Surgical biopsy is more
evasive and costly but it is the only test that can
confirm malignancy. Efficient machine learning
algorithms can enhance the performance of
mammogram analysis and provide an equivalent
performance in terms of robustness and accuracy for
surgical biopsy without its evasiveness and cost.
Digitized mammography techniques:
There have
been various advancements in digital image processing
in the fields of filtering, enhancement, segmentation
and others. However, the usefulness of the new
techniques depends mainly on two important
parameters: the spatial and grey-level resolutions[9]. An
efficient algorithm should provide a diagnostic
accuracy in digital images equivalent to that of
conventional films. Pixel size and pixel depth are
important factors that could critically affect the
visibility of small-low contrast objects, which may
carry significant information for diagnosis[10].
Therefore, digital image recording systems for medical
purposes must provide high spatial resolution and high
contrast sensitivity. Nevertheless, this requirement
retards the implementation of digital technologies due
to the increment in processing and transmission time,
storage capacity and cost. For instance, it has been
shown that isolated clusters of microcalcifications are
one of the most frequent radiological features of
asymptomatic breast cancer[10]. A careful search for the
clustered microcalcifications that may herald an earlystage
cancer should be carried out on all
mammograms[11]. Microcalcifications frequently appear
as small-size low-contrast radiopacities[12].
Scaling description:
As illustrated earlier,
mammogram images need to be scaled down to enable
better transfer and processing. The bicubic interpolation
technique is used to provide efficient reduction in the
size of the mammogram without affecting its quality or
regions of interest. The micro-calcification cluster is
defined to be at least 3 micro-calcifications within a 1
cm2 region of mammogram[33]. Therefore, the scaling
ratio for the mammogram image should be suitable to
keep the micro-calcification cluster clear and easily
detected by radiologists. In most mammogram cases,
the smallest micro-calcification cluster area has about
37 pixels in high resolution images. Therefore, the
maximum scaling down ratio was set to 50% of the
image height and 50% of the image width. This ratio
will ensure that the microcalcification clusters can still
be detected by radiologists.
CONCLUSION
Two algorithms for reduction are proposed. The
resulted algorithm could successfully reduce the
mammogram images with 87% percent. For example,
an image that has an original size 15,338,672 bytes
became 1,925,120 bytes with minimum processing time
which is 100 seconds. The shrinking algorithm that is
used as a pre-reduction process is developed and
implemented. It maintained the original image features
without any lose of important data, but the image
brightness was less than the original. However, the
pixel-depth conversion algorithm could convert the 16-
bits to 8-bits. This conversion also produced good
results as the most important data are concentrated in
the first 8-bits. Thus, the data loss in the breast region
was minimal. The enhanced algorithm of pixel- depth
conversion has produced excellent results and the
output image was similar to the original one with the
same brightness and data.
[attachment=40694]
Abstract:
High quality mammogram images are high resolution and large size images. Processing
these images require high computational capabilities. The transmission of these images over the net is
sometimes critical especially if the diagnosis of remote radiologists is required. In this paper, a preprocessing
technique for reducing the size and enhancing the quality of USF and MIAS mammogram
images is introduced. The algorithm analyses the mammogram image to determine if 16-bit to 8-bit
conversion process is required. Enhancement is applied later followed by a scaling process to reduce
the mammogram size. The performances of the algorithms are evaluated objectively and subjectively.
On average 87% reduction in size is obtained with no loss of data at the breast region.
INTRODUCTION
Early detection is the best way to improve breast
cancer prognosis since the causes of the disease are still
unknown. Breast cancer is the second most prevalent
cancer among women after skin cancer[1]. In addition, it
accounts for most cancer deaths coming only second to
lung cancer[1]. Currently, three methods are used for
breast cancer diagnosis: mammography, fine needle
aspirate and surgical biopsy. Mammography has a
reported malignant sensitivity which varies between 68
and 79%[2]. Fine needle aspirate depends on extracting
fluids from a breast lump and inspecting it under the
microscope. This method has a reported sensitivity
varying from 65 to 98%[2]. Surgical biopsy is more
evasive and costly but it is the only test that can
confirm malignancy. Efficient machine learning
algorithms can enhance the performance of
mammogram analysis and provide an equivalent
performance in terms of robustness and accuracy for
surgical biopsy without its evasiveness and cost.
Digitized mammography techniques:
There have
been various advancements in digital image processing
in the fields of filtering, enhancement, segmentation
and others. However, the usefulness of the new
techniques depends mainly on two important
parameters: the spatial and grey-level resolutions[9]. An
efficient algorithm should provide a diagnostic
accuracy in digital images equivalent to that of
conventional films. Pixel size and pixel depth are
important factors that could critically affect the
visibility of small-low contrast objects, which may
carry significant information for diagnosis[10].
Therefore, digital image recording systems for medical
purposes must provide high spatial resolution and high
contrast sensitivity. Nevertheless, this requirement
retards the implementation of digital technologies due
to the increment in processing and transmission time,
storage capacity and cost. For instance, it has been
shown that isolated clusters of microcalcifications are
one of the most frequent radiological features of
asymptomatic breast cancer[10]. A careful search for the
clustered microcalcifications that may herald an earlystage
cancer should be carried out on all
mammograms[11]. Microcalcifications frequently appear
as small-size low-contrast radiopacities[12].
Scaling description:
As illustrated earlier,
mammogram images need to be scaled down to enable
better transfer and processing. The bicubic interpolation
technique is used to provide efficient reduction in the
size of the mammogram without affecting its quality or
regions of interest. The micro-calcification cluster is
defined to be at least 3 micro-calcifications within a 1
cm2 region of mammogram[33]. Therefore, the scaling
ratio for the mammogram image should be suitable to
keep the micro-calcification cluster clear and easily
detected by radiologists. In most mammogram cases,
the smallest micro-calcification cluster area has about
37 pixels in high resolution images. Therefore, the
maximum scaling down ratio was set to 50% of the
image height and 50% of the image width. This ratio
will ensure that the microcalcification clusters can still
be detected by radiologists.
CONCLUSION
Two algorithms for reduction are proposed. The
resulted algorithm could successfully reduce the
mammogram images with 87% percent. For example,
an image that has an original size 15,338,672 bytes
became 1,925,120 bytes with minimum processing time
which is 100 seconds. The shrinking algorithm that is
used as a pre-reduction process is developed and
implemented. It maintained the original image features
without any lose of important data, but the image
brightness was less than the original. However, the
pixel-depth conversion algorithm could convert the 16-
bits to 8-bits. This conversion also produced good
results as the most important data are concentrated in
the first 8-bits. Thus, the data loss in the breast region
was minimal. The enhanced algorithm of pixel- depth
conversion has produced excellent results and the
output image was similar to the original one with the
same brightness and data.