Since the details in quasi-homogeneous region will be degraded from a conventional global image enhancement methods such as histogram equalization. This degradation causes from the saturation of the gray levels in equalization process. Then the local histogram equalization for each quasi-homogeneous region will be used in order to improve the details in the regions. To obtain the quasi-homogeneous regions the image can be segmented by using a watershed transform. The watershed transform is based on mathematical morphology; therefore the segmented regions can be effectively separated meaningful. Two adjacent regions in images which almost similar in gray levels can be separted by the watershed transform. In the color image enhancement three spectral bands are enhanced independently. Then all the three bands are assigned to each processed image in order to produce a color composite image. By the proposed algorithm the result images show the better perception on image details. Therefore the high efficiency of image classification can be obtained by using this color image.

The segmentation of interesting objects within the image is an important process to provide information for other procedures. The major problem of image segmentation is the number of objects suitable for classifying objects. This research aims to present on Image Segmentation using Fuzzy C- Mean Via Image Filter. The objects will be split up using their object intensities. The proposed method can classify several objects at the same process. Therefore it saves time to process less than other existing methods. The proposed method is consisted of three steps: noise reduction histogram smoothing and object classification. The results of the proposed method found that objects are automatically classified by Fuzzy C-Mean. Moreover the algorithm that successfully calculates the proper number of segmentations