[HTML][HTML] Brain tumor segmentation based on a hybrid clustering technique

E Abdel-Maksoud, M Elmogy, R Al-Awadi - Egyptian Informatics Journal, 2015 - Elsevier
Image segmentation refers to the process of partitioning an image into mutually exclusive
regions. It can be considered as the most essential and crucial process for facilitating the …

Information retrieves from brain MRI images for tumor detection using hybrid technique K-means and artificial neural network (KMANN)

M Sharma, GN Purohit, S Mukherjee - Networking Communication and …, 2018 - Springer
Medical imaging plays a significant role in the field of medical science. In present scenario
image segmentation is used to extract abnormal tissues from normal tissues clearly in …

Brain tumor segmentation based on hybrid clustering and morphological operations

C Zhang, X Shen, H Cheng… - International journal of …, 2019 - Wiley Online Library
Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data
remains challenging owing to the complex structure of brain tumors, blurred boundaries, and …

Brain image segmentation using variation in structural elements of morphological operators

A Kulshreshtha, A Nagpal - International Journal of Information …, 2023 - Springer
Image segmentation is considered to be an efficient way to extract out the tumor region in
brain from the magnetic resonance imaging (MRI) images. In this paper, the morphological …

Brain tumor detection using self-adaptive K-means clustering

N Kaur, M Sharma - … data analytics and soft computing (ICECDS …, 2017 - ieeexplore.ieee.org
Brain tumor detection is an important diagnostic process in medical field. Magnetic
resonance imaging (MRI) is the prime imaging technique while analysing the brain/skull with …

An unsupervised learning with feature approach for brain tumor segmentation using magnetic resonance imaging

K Ejaz, MSM Rahim, UI Bajwa, N Rana… - Proceedings of the 2019 …, 2019 - dl.acm.org
Segmentation methods are so much efficient to segment complex tumor from challenging
datasets. MACCAI BRATS 2013-2017 brain tumor dataset (FLAIR, T2) had been taken for …

Efficient segmentation of brain tumor using FL-SNM with a metaheuristic approach to optimization

A Natarajan, S Kumarasamy - Journal of medical systems, 2019 - Springer
Nowadays, automatic tumor detection from brain images is extremely significant for many
diagnostic as well as therapeutic purposes, due to the unpredictable shape and appearance …

An efficient approach for brain image (tissue) compression based on the position of the brain tumor

S Kumarganesh, M Suganthi - International Journal of Imaging …, 2016 - Wiley Online Library
Medical image processing plays an important role in brain tissue detection and
segmentation. In this paper, a computer aided detection of brain tissue compression based …

Brain tumor classification for MR imaging using support vector machine

Monika, R Rani, A Kamboj - … : Proceedings of ICACIE 2017, Volume 2, 2019 - Springer
Nowadays, brain tumor segmentation is most challenging task in the field of medical image
processing. Manual segmentation of these images by the domain experts is a time …

Automated Detection of Brain Stroke in MRI with Hybrid Fuzzy -Means Clustering and Random Forest Classifier

A Subudhi, SS Jena, S Sabut - International Journal of …, 2019 - World Scientific
Neuroimaging investigation is an essential parameter to detect infarct lesion in stroke
patients. Precise detection of brain lesions is an important task related to impaired behavior …