F. et al , this paper proposes a novel method for enhancing and sharpening
medical color digital images. Low contrast and poor quality are main problems
in the production of medical images. By using the wavelet transforms and Haar
transform followed by using Sobel and Laplacian operator to obtain the
sharpened image. First, a medical image was decomposed with wavelet transform.
Secondly, all high-frequency sub-images were decomposed with Haar transform.
Thirdly, noise in the frequency field was reduced by the soft-threshold method.
Fourthly, high-frequency coefficients were enhanced by different weight values
in different sub-images. Then, the enhanced image was obtained through the inverse
wavelet transform and inverse Haar transform. Lastly, the filters are applied
to sharpen the image; the resulting image is then subtracted from the original
image. Experiments showed that this method can not only enhance an image’s
details but can also preserve its edge features effectively.
et al 1998, a general framework is presented in this paper for edge detection
and enhancement of medical images. The method is based on multi scale analysis
using filter banks, and it is adaptive to a large number of features.
Initially, an optimal one-scale filter is designed for the required detection.
This one-scale filter is further extended to a set of multi scale filters,
which in turn are used in designing the filter bank that would provide the
desired multi scale responses. Subsequently, the scale space information is
optimally combined in a maximum posteriori (MAP) classifier, whose design
depends on the desired feature and the resulting filter bank. The method is
robust to noisy conditions which are common to medical images in angiography,
echocardiography, blood vessels, and others based on ultrasonic imaging, X-ray,
et al 1998, an algorithm is developed that detects well-localized, unregimented,
thin edges in medical images based on optimization of edge configurations using
a genetic algorithm (GA). Several enhancements were added to improve the
performance of the algorithm over a traditional GA. The edge map is split into
connected sub regions to reduce the solution space and simplify the problem.
The edge-map is then optimized in parallel using incorporated genetic operators
that perform transforms on edge structures. Adaptation is used to control
operator probabilities based on their participation. The GA was compared to the
simulated annealing (SA) approach using ideal and actual medical images from
different modalities including magnetic resonance imaging (MRI), computed
tomography (CT), and ultrasound. Quantitative comparisons were provided based
on the Pratt figure of merit and on the cost-function minimization. The
detected edges were thin, continuous, and well localized. Most of the basic
edge features were detected; results for different medical image modalities are
promising and encourage further investigation to improve the accuracy and
experiment with different cost functions and genetic operators.
et al 1998, one of the most common degradations in medical images is their poor
contrast quality. This suggests the use of contrast enhancement methods as an
attempt to modify the intensity distribution of the image. In this paper, a new
edge detected morphological filter is proposed to sharpen digital medical
images. This is done by detecting the positions of the edges and then applying
a class of morphological filtering. Motivated by the success of threshold
decomposition, gradient based operators are used to detect the locations of the
edges. A morphological filter is used to sharpen these detected edges.
Experimental results demonstrate that the detected edge declaring filter
improved the visibility and perceptibility of various embedded structures in
digital medical images. Moreover, the performance of the proposed filter is
superior to that of other sharpener-type filters.
et al 1998, medical images edge detection is an important work for object
recognition of the human organs and it is an important pre-processing step in
medical image segmentation and 3D reconstruction. Conventionally, edge is
detected ac-cording to some early brought forward algorithms such as
gradient-based algorithm and template-based algorithm, but they are not so good
for noise medical image edge detection. In this paper, basic mathematical
morphological theory and operations are introduced at first, and then a novel
mathematical morphological edge detection algorithm is proposed to detect the edge
of lungs C Tim age with salt- and pepper noise. The experimental results show
that the proposed algorithm is more efficient for medical image demising and
edge detection than the usually used template-based edge detection algorithms
and general morphological edge detection algorithms.
et al 1998, medical Image Processing and its applications in Computer Assisted
Diagnoses (CAD) and therapy (e.g. Computer Assisted Surgery-CAS) are of
increasing importance in modern medicine. The most common degradations in
medical images are their poor contrast quality. Medical Image Enhancement is
the art of examining images for identifying objects and judging their
significance. The proposed paper uses the concept of Genetic Algorithm which
was proved to be the most powerful unbiased Optimization techniques for
sampling a large solution space. Because of unbiased stochastic sampling, they
can be quickly adapted in medical image processing. They can be applied for the
medical image enhancement, segmentation, feature extraction, classification and
image generation. In this paper, we deal with medical image enhancement using
Genetic Algorithm (GA) and the Morphological filter to sharpen the detected
edges thus improving the contrast of the image.
this paper, was proposed method of image enhancement based on edge detection
with adding image. The structure of the paper is arranged as follows: section 1
included the introduction and section 2included the background of theoretical. The
samples images and proposed scheme of Algorithm in section 3. Section 4
included the results. Conclusions and future works are shown in section 5.