Feature extraction and segmentation of medical images for MRI and Digital mammogram

Résumé

Developing a computer aided diagnosis system (CAD) is an extremely
challenging task. One of the major goals of CAD is to help the radiologist to make
good decisions by detecting and analyzing characteristics of benign and malignant lesions. In this context, we present accurate and automatic method that, detect and extract malignancy descriptors of breast and meningioma brain tumor.
Our applied an algorithm that uses enhancement image based on homomorphic
filtering and adaptive histogram equalization technique. This work was proposed
by Zhang Chaofu et al. [3]. A region of interest is determinated using K means
clustering. And then, we employed basically wavelet transform to extract pertinent features for meningioma tumor, geometric and texture characteristics for
breast tumor in order to classify malignancy lesion.