Brain segmentation in MR images using a texture-based classifier associated with mathematical morphology

Abstract

Skull stripping, which refers to the segmentation of brain tissue from non-brain tissue, has been challenging due to the ramification of the human brain structures and volatile parameters in the magnetic resonance imaging (MRI) procedures. It has been one of the most critical preprocessing steps in medical image analysis. We propose a hybrid skull stripping algorithm that is based on texture feature analysis, fuzzy possibilistic c-means (FPCM), and morphological operations. The input MR image is first processed to obtain two texture feature maps, to which the FPCM is applied for acquiring brain and non-brain masks. A number of morphological operations are subsequently performed to extract the brain. Our algorithm has been compared with two famous methods and evaluated on the internet brain segmentation repository (IBSR) datasets. Preliminary experimental results suggested that this new framework achieved high accuracy and outperformed the compared methods. We believe that the proposed scheme is of effectively potential in a wide variety of brain MR image segmentation applications.

Segmentación de tejido cerebral mediante redes neuronales convolucionales basadas en U-Net

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Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET

“…Principal component analysis [ 1 ], fuzzy c-means Hsieh [ 2 ], Gabor filter [ 3 ], and multilevel fuzzy c-means [ 4 ] are examples of traditional machine learning techniques. However, the performance of these algorithms in the field of computer vision is not sufficient.…”

Efficient Morphological Segmentation of Brain Hemorrhage Stroke Lesion Through MultiResUNet

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