Open AccessFeature extraction and segmentation of medical images for MRI and Digital mammogram
Author(s) -
Mehdi Tinhinane,
Asma Boudrioua,
Abdelouhab Aloui
Publication year - 2019
Publication title -
medical technologies journal
Language(s) - English
Resource type - Journals
ISSN - 2572-004X
DOI - 10.26415/2572-004x-vol2iss4p308-313
Subject(s) - artificial intelligence , computer science , cad , histogram , pattern recognition (psychology) , segmentation , context (archaeology) , feature extraction , adaptive histogram equalization , computer vision , histogram equalization , feature (linguistics) , computer aided diagnosis , meningioma , radiology , medicine , image (mathematics) , paleontology , linguistics , philosophy , engineering drawing , engineering , biology
Developing a computer aided diagnosis system (CAD) is an extremelychallenging task. One of the major goals of CAD is to help the radiologist to makegood 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 homomorphicfiltering and adaptive histogram equalization technique. This work was proposedby Zhang Chaofu et al. [3]. A region of interest is determinated using K meansclustering. And then, we employed basically wavelet transform to extract pertinent features for meningioma tumor, geometric and texture characteristics forbreast tumor in order to classify malignancy lesion.