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.
In this research we introduce
a regularization based feature selection algorithm to benefit from
sparsity and feature grouping properties and incorporate it into the
medical image classification task. Using this group sparsity (GS)
method, the wh
ole group of features are either selected or removed.
The basic idea in GS is to delete features that do not affect the
retrieval process, instead of keeping them and giving these features
small weights. Therefore, GS improves system by increasing
accuracy of the results, plus reducing space and time requirements
needed by the system.