For the problem of noise and no reference image during brain magnetic resonance imagery (MRI) image segmentation, this paper proposes a new strategy to segment brain MRI image based on K-means ...

The experimental results show that MRI brain image divided into 4 classes is reasonable and selection of initial cluster centers by using gray matrix normalization method for brain tissue segmentation ...

Method This section first discusses the possibility of similarity between wormhole behavior and the complex shape of brain tumor segmentation to primarily determine the tumor contour of “bottle-neck” ...

The classic fuzzy c -means (FCM) algorithm is extremely sensitive to noise and offset fields. If the algorithm is used directly to segment the brain MRI image, the ideal segmentation result cannot be ...

An algorithm for improving abdominal ultrasound images is proposed based on combination of histogram equalization and wavelet transformation in [16] . This algorithm improves edges and surroundings of ...

It was the aim of this study to implement an algorithm modifying Dixon-based MR imaging datasets for attenuation correction in hybrid PET/MR imaging with a multiacquisition variable resonance image ...

Enhance medical image segmentation accuracy with our improved fuzzy c-means algorithm. Incorporating local and non-local information, our method reduces noise sensitivity and outperforms other brain ...