A comparison of image enhancement techniques for recognizing and classifying automatically the medical images and implement on MRI brain image

الملخص EN

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.