Epilepsy is a chronic neurological disorder that causes unprovoked and recurrent seizures which according to WHO affects approximately 50 million people worldwide. Functional magnetic resonance images (MRI) help to identify certain affected areas of the brain, namely, the gliosis and hippocampal volume loss. These losses cause complex epilepsy, and is known as hippocampal sclerosis or Mesial Temporal Sclerosis (MTS). This work presents the development of a Computer Aided Diagnosis CAD system software package) that can be used to identify the characteristics and patterns of MTS from brain magnetic resonance images. The image processing techniques involve texture analysis, statistical features, evaluation of the 3D Region of interest (ROI), and threshold analysis. The software allows the automatic evaluation of the degeneration of hippocampal structures, hippocampal volume and signal intensity. We will describe and demonstrate the software (which can currently be accessed on GitHub). It is expected that this tool will be useful in new neurology/radiology specialists and can serve as a secondary diagnosis. However, it is necessary to validate the software system qualitatively and quantitatively in order to get more effectiveness and efficiency in a real-world clinical application.
Mesial temporal sclerosis (MTS) is the principal cause of complex epilepsy, is manifested principally by gliosis and hippocampal volume loss. This project aims to develop an algorithm that allows automatic measurement of hippocampal volume and signal intensity in magnetic resonance imaging. The algorithm developed uses preprocessing of the images to reduce the artifacts and for the extraction of the features were used techniques of machine learning (support vector machine) and texture analysis. Results can help to optimize time in the assessment of the mesial temporal sclerosis and can contribute to the best training to the youngers neuroradiologists.
Brain demyelination lesions occur due to damage of the myelin layer of nerve fibers, this deterioration is the cause of pathologies such as multiple sclerosis, leukodystrophy, encephalomyelitis. Brain ischemia is the interruption of the blood supply to the brain, and the flow of oxygen and nutrients needed to maintain the correct functioning of brain cells. This project presents the results of an algorithm processing images with the the main objective of identify and differentiate between demyelination and ischemic brain diseases through the automatic detection, classification and identification of their features found in the magnetic resonance images. The sequences of images used were T1, T2, and FLAIR and with a dataset of 300 patients with and without these or other pathologies, respectively. The algorithm in this stage uses Discrete Wavelet Transform (DWT), principal component analysis (PCA) and a kernel support vector machine (SVM). The algorithm developed indicates a 75% of accuracy, for that reason, with an effective validation could be applied for the fast diagnosis and contribute to an effective treatment of these brain diseases especially in the rural places.
This work presents the advance to development of an algorithm for automatic detection of demyelinating lesions and cerebral ischemia through magnetic resonance images, which have contributed in paramount importance in the diagnosis of brain diseases. The sequences of images to be used are T1, T2, and FLAIR.
Brain demyelination lesions occur due to damage of the myelin layer of nerve fibers; and therefore this deterioration is the cause of serious pathologies such as multiple sclerosis (MS), leukodystrophy, disseminated acute encephalomyelitis. Cerebral or cerebrovascular ischemia is the interruption of the blood supply to the brain, thus interrupting; the flow of oxygen and nutrients needed to maintain the functioning of brain cells. The algorithm allows the differentiation between these lesions.
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