  This paper presents a tumor detection algorithm from mammogram. The proposed
system focuses on the solution of two problems. One is how to detect tumors as
suspicious regions with a very weak contrast to their background and another is
how to extract features which categorize tumors. The tumor detection method
follows the scheme of (a) mammogram enhancement. (b) The segmentation of the
tumor area. (c) The extraction of features from the segmented tumor area. (d)
The use of SVM classifier. The enhancement can be defined as conversion of the
image quality to a better and more understandable level. The mammogram
enhancement procedure includes filtering, top hat operation, DWT. Then the
contrast stretching is used to increase the contrast of the image. The
segmentation of mammogram images has been playing an important role to improve
the detection and diagnosis of breast cancer. The most common segmentation
method used is thresholding. The features are extracted from the segmented
breast area. Next stage include, which classifies the regions using the SVM
classifier. The method was tested on 75 mammographic images, from the mini-MIAS
database. The methodology achieved a sensitivity of 88.75%.
