Automatic Brain Tumor Detection refers to the problem of delineating tumorous tissues from MRI images for the purpose of medical diagnosis and surgical planning. The process uses tumor characteristics in images, such as sizes, shapes, locations and intensities for the isolation of the tumor which depends on manual tracing by experts. This paper proposes automatic brain tumor detection and isola...

AIM To evaluate the rates of endocrine abnormalities in survivors of childhood brain tumors and identify risk factors. METHODS The medical charts of patients were reviewed for background, disease-related and treatment-related data. Endocrine dysfunction was determined by clinical and laboratory evaluation. RESULTS The study group included 114 patients with a mean age of 15.57 ± 5.93 years. ...

INTRODUCTION The differentiation of metastatic and primary brain tumors with certainty is important since clinical management and treatment of these two types of tumors are radically different. The purpose of the present study was to evaluate the effect of peritumoral edema volume, tumor volume and mass effect of tumor on differential diagnosis of metastatic and primary brain tumors. Also we ha...

In the last couple of years, the EEG signal analysis was focused on epilepsy seizure detection. Epilepsy is a common chronic neurological disorder; they are result of transient and unexpected electrical disturbance of the brain. Epilepsy seizures also a symptom of brain tumor existence, 30% patients with brain tumor are affected with epilepsy seizure. This paper proposes a two level brain tumor...

Medical Image Processing is the fast growing and challenging field now a days. Medical Image techniques are used for Medical diagnosis. Brain tumor is a serious life threatening disease. Detecting Brain tumor using Image Processing techniques involves four stages namely Image Pre-Processing, Image segmentation, Feature Extraction, and Classification. Image processing and neural network techniqu...

Recent advances in medical image analysis have been made to improve our understanding of how disease develops, behaves, and responds treatment. Magnetic resonance imaging (MRI) positron emission tomography (PET) advanced strategies provide structural functional phenotypic biomarkers that correlate with key processes. Through radiomics radiogenomics, ML-medical has opened up new perspectives hig...

Radiation necrosis of the brain (BRN) is most frequent and dangerous iatrogenic complication radiation therapy tumors non-tumor diseases base skull. The risk its development increases with an increase in amount radiation, single total doses due to synergy action adjuvant chemotherapy used. pathogenesis BRN based on damage microcirculatory bed tumor surrounding tissues edema impaired trophic neu...

BACKGROUND Glioma is the most common and most difficult to treat brain cancer. Despite many efforts treatment, efficacy remains low. As neurosurgical removal is the standard procedure for glioma, a method, allowing for both early detection and exact determination of the location, size and extent of the tumor, could improve a patient's positive response to therapy. NEW METHOD We propose applic...

Brain is well protected inside the hard and bony skull that hampers the study of its functions as well as makes the diagnosis of brain diseases more difficult and challenging. In this paper we perform review study on brain tumor detection from Magnetic Resonance Image (MRI). Stages for brain tumor detection using MR image are Pre-processing, Segmentation, Feature Extraction, Classification. The...

Magnetic Resonance Imaging (MRI) established itself as a key imaging modality in diagnosis and treatment of brain tumors. Automatic segmentation of tumors becomes a tedious task due to complex anatomical brain structure. In addition, presence of noise degrades the quality of MRI scans. MRI images are usually corrupted by Rician noise which would mislead the image analysis algorithms and results...