Image segmentation is one of the most important tasks in medical image analysis and is
often the first and the most critical step in many clinical applications. In brain MRI analysis …

Brain imaging techniques play an important role in determining the causes of brain cell
injury. Therefore, earlier diagnosis of these diseases can be led to give rise to bring huge …

In the analysis of medical images, one of the challenging tasks is the recognition of brain
tumours via medical resonance images (MRIs). The diagnosis process is still tedious due to …

Image segmentation is often required as a preliminary and indispensable stage in the
computer aided medical image process, particularly during the clinical analysis of magnetic …

Brain tumor analysis plays an important role in medical imaging applications and in
delivering a huge amount of anatomical and functional information, which increases and …

The study of brain disorders requires accurate tissue segmentation of magnetic resonance
(MR) brain images which is very important for detecting tumors, edema, and necrotic tissues …

Fuzzy clustering is a classical method to produce soft partitions of data. One of its typical
applications is image segmentation. Guided filter, on the other hand, is a powerful edge …

Thyroid hormones produced by the thyroid gland help regulation of the body's metabolism. A
variety of methods have been proposed in the literature for thyroid disease classification. As …

Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its
manual interpretation by experts is arduous and time-consuming. Thus, there is a need for …

Magnetic resonance imaging (MRI) segmentation has been implemented by many
clustering techniques, such as k-means, fuzzy c-means (FCM), learning-vector quantization …