The study suggests a new approach to segment the ultrasound
uterus images to obtain the fetus region. The approach consists of
three stages. The first includes the preprocessing in which the
speckle noise is removed from the ultrasound images depe
nding on
sequential filtering of Gabor filter and median filter. Second, an
improved active shape contour independent of edges is applied to
segment the uterus images. The last stage is the post processing
which depends on the morphological operation to eliminate the
undesired region and obtain the region of interest (fetus).
The designed system has been tested by means of medical database
of ultrasound uterus images downloaded from the ULTRASCAN
CENTRE site in Kaloor (India). The experimental tests show that
the proposed sequential filtering technique improves the active
shape contour algorithm performance significantly, so the system
segment the uterus images correctly even in the presence of speckle
noise.
The amount of digital images that are produced in hospitals is increasing rapidly. Effective
medical images can play an important role in aiding in diagnosis and treatment, they can
also be useful in the education domain for healthcare students by
explaining with these
images will help them in their studies, new trends for image retrieval using automatic
image classification has been investigated for the past few years. Medical image
Classification can play an important role in diagnostic and teaching purposes in medicine.
For these purposes different imaging modalities are used. There are many classifications
created for medical images using both grey-scale and color medical images. In this paper,
different algorithms in every step involved in medical image processing have been studied.
One way is the algorithms of preprocessing step such as Median filter [1], Histogram
equalization (HE) [2], Dynamic histogram equalization (DHE), and Contrast Limited
Adaptive Histogram Equalization (CLAHE). Second way is the Feature Selection and
Extraction step [3,4], such as Gray Level Co-occurrence Matrix(GLCM). Third way is the
classification techniques step, which is divided into three ways in this paper, first one is
texture classification techniques, second one is neural network classification techniques,
and the third one is K-Nearest Neighbor classification techniques.
In this paper, we have use MRI brain image to determine the area of tumor in brain. The
steps started by preprocessing operation to the image before inputting it to algorithm. The
image was converted to gray scale, later on remove film artifact using special algorithm,
and then remove the Skull portions from the image without effect on white and gray matter
of the brain using another algorithm, After that the image enhanced using optimized
median filter algorithm and remove Impurities that produced from first and second steps.
