This paper proposes an approach to improving color images su ering from low dynamic range by employing both Histogram matching and Histogram stretching techniques. In the  rst stage, the color image was transformed from RGB into HSV color space, in which the color information was separated from intensity. Then, an appropriate reference image was selected by comparing component V of the enhanced image with component V of the database images using a similarity measure. The selected image was used as the target image in Histogram matching algorithm for enhancement. In the second stage, components V and S were linearly stretched in order to recover the image color information. Finally, using the treated V and S components and untreated component of H, the enhanced image was obtained in RGB color space by inverse transform. The qualitative and quantitative results showed that the contrast and color of the resultant image were greatly improved using the proposed method, which outperformed the current state-of-the-art methods.

Background: Breast cancer is one of the most important diseases in females. Malaysian women have not excluded. According to the Malaysian Oncology Society [1], about 4% of women (who are 40 years old and above) have involved by breast cancer. Masses and microcalcifications are two important signs for breast cancer diagnosis on mammography. According to our estimation, radiologists could diagnose breast cancer on mammogram screening program, with approximately 75% accuracy. About 25% of breast cancers have missed on mammograms. This study aimed to explore the effects of enhancement methods on digital mammograms.Methods: SPSS software have used for data analysis. Wilcox on ranked test and ROC have used to compare the original and manipulated images. In this study, 60 digital mammogram images which include 20 normal and 40 confirmed diagnosed cases of breast cancer (masses), have selected and manipulated by using Histogram equation, Histogram stretching and median filter.Results: The results have shown that the Histogram stretching and median filter methods could improve image quality for detection of masses with increased sensitivity and specificity by 5%.Conclusion: The sensitivity and specificity have improved by using Histogram stretching and median filter. The results of this study have shown results as below ; the Histogram equation have improved the sensitivity up to 97.5% ,while the median filter could improve sensitivity (97.5%) and specificity (85.5%). It means that the median filter could be more effective than the other enhancement methods.