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The brain cancer is a deadly disease affecting almost 1.7% of world’s population with top mortality rate. The basic cause of brain cancer is the abnormal growth of brain tissues in the early stage. Further, these tissues are converted into tumors. Early detection and exact location of brain tumor can assist for further therapies of brain. In this paper a computer vision-based approach is developed for exacting an exact location of the brain tumors from the MRI images. The method starts from skull stripping for removal of the outer portion of brain and segregate the white matter. Further communication with local agent (CLA) clustering technique is applied followed by morphological post processing methods for extraction of tumor regions from white matter regions of the brain. The method is tested on a publicly available MRI dataset. Quantitative and qualitative measures show that the proposed method achieves an accuracy of 99.64% as compared to others.

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Detection of artifacts produced in EEG data by eye blinks is a very common problem in EEG research. In this paper we address the detection of eye blink artifacts in a motor imagery (MI) EEG data. Artifacts are nothing but some kind of disturbances present in the brain signal whose origin is not the brain itself. Detection of unwanted artifacts plays a crucial role to acquire artifact free and clean brain EEG signals to analyze and detect brain activities. There are generally two ways of generation of artifacts. From a recorded signal most common and important artifacts in the form of eye blinks are recognized and encapsulated. In this paper a new software tool named BRAINSTORM is introduced for the detection of eye blink artifacts.