Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

Abstract

The drive of this study is to develop a robust system. A method to classify brain magnetic resonance imaging (MRI) image into brain‐related disease groups and tumor types has been proposed. The proposed method employed Gabor texture, statistical features, and support vector machine. Brain MRI images have been classified into normal, cerebrovascular, degenerative, inflammatory, and neoplastic. The proposed system has been trained on a complete dataset of Brain Atlas‐Harvard Medical School. Further, to achieve robustness, a dataset developed locally has been used. Extraordinary results on different orientations, sequences of both of these datasets as per accuracy (up to 99.6%), sensitivity (up to 100%), specificity (up to 100%), precision (up to 100%), and AUC value (up to 1.0) have been achieved. The tumorous slices are further classified into primary or secondary tumor as well as their further types as glioma, sarcoma, meningioma, bronchogenic carcinoma, and adenocarcinoma, which could not be possible to determine without biopsy, otherwise.

Preliminary study on identification of estrogen receptor-positive breast cancer subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis

“…Texture analysis is an emerging computer-aided diagnosis technology that can quantitatively display subtle changes in image pixels and arrangement, independence of image grayscale and subjective factors of the diagnostics, and its evaluation value for various tumor heterogeneities is obtained. It has generally been confirmed that it has gradually become a research hotspot in recent years (6)(7)(8)(9).…”

Brain tumor grade classification using multi‐step pre‐training

“…Classical machine-learning algorithms have been used for brain tumor grading and other automated medical image analysis tasks. [25][26][27] The limitation of these techniques is the need to handcraft suitable features from the images/volumes, which are then fed to the classifier.…”

Diagnosis of Brain Hemorrhage Using KNN based Radial Basis Classifier

“…A modified segmentation algorithm have been implied with the use of GLCM and minimal LBP(local binary pattern).Here classification have been performed by NB-PKC that precisely determines the position thereby decreased the human errors. [6] The suggested technique implemented statistical features, SVM and Gabor texture. The MRI images was classified into degenerative, inflammatory, cerebrovascular, normal and and neoplastic.…”