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 diagnos...

Masses in the breast can be located in digital mammogram images by computationally analysing various feature statistics from the image. Any algorithm used to analyse digital mammogram images can be both time-consuming and errorprone because many areas of these images appear to have features that are mass-like but not masses. Thus false positives are produced which detract from the effectiveness...

Micro calcification in digital mammogram are an important early sign of breast cancer and their early detection is very important to improve its diagnosis. Computerbased detection system can assist the radiologist to improve the diagnostic accuracy. Digital Mammogram is the low cost and effective for early detection of masses or abnormalities which related to the high risk factor of breast canc...

A digital mammogram generally detects varying degrees of breast cancer such as clustered microcalcifications, speculated lesions, circumscribed masses, ill-defined masses, and architectural distortions. Many methods of analysing digital mammograms have been recently examined and yielded varied success. Common techniques from the field of image processing have been applied to digital mammograms ...

This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate ...

Digital image processing techniques are useful in abnormality detection in mammogram images. Recently, texture based image segmentation of mammogram images has become popular due to its better precision and accuracy. Local Binary Pattern has been a recently proposed texture descriptor which attracted the research community rigorously towards texture based analysis of digital images. Many textur...

Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes cr...