Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …

Segmentation divides an image into discrete provinces containing pieces of pixels with
analogous attributes. To be expressive and useful for image analysis and interpretation, the …

Segregation of tumor region in brain MR image is a prominent task that instantly provides
easier tumor diagnosis, which leads to effective radiotherapy planning. For decades …

Segmentation of grayscale medical images is challenging because of the similarity of pixel
intensities and poor gradient strength between adjacent regions. The existing image …

Intensity inhomogeneity, a common artefact in brain magnetic resonance imaging, poses
challenges in medical image analysis. Intensity inhomogeneity, also known as bias field …

The proposed work introduces a modified method of fuzzy c means (FCM) algorithm using
bias field correction and partial supervision techniques. The proposed method is named as …

Picture fuzzy C-means clustering is a new computational intelligence method that has more
significant potential advantages than fuzzy clustering in medical image interpretation …

Objective: The paper applies spatial fuzzy clustering algorithm to explore the role and value
of neuroendoscopic assisted technology in the operation of tumors in the saddle region, and …

Brain imaging with data mining is rising in significance as it enables the provision of
prognoses, treatments and a better comprehension of brain functioning. Data mining …

This paper describes an enhanced process able to attain righteous classification of
morphological malformation in foetal head ultrasound images. These anomalies can be …