Medical image segmentation methods, algorithms, and applications
Medical images have made a great impact on medicine, diagnosis, and treatment. The most
important part of image processing is image segmentation. Many image segmentation …
important part of image processing is image segmentation. Many image segmentation …
Segmentation of images by color features: A survey
Image segmentation is an important stage for object recognition. Many methods have been
proposed in the last few years for grayscale and color images. In this paper, we present a …
proposed in the last few years for grayscale and color images. In this paper, we present a …
Fuzzy C-means clustering through SSIM and patch for image segmentation
In this study, we propose a new robust Fuzzy C-Means (FCM) algorithm for image
segmentation called the patch-based fuzzy local similarity c-means (PFLSCM). First of all …
segmentation called the patch-based fuzzy local similarity c-means (PFLSCM). First of all …
Image segmentation using computational intelligence techniques
Image segmentation methodology is a part of nearly all computer schemes as a pre-
processing phase to excerpt more meaningful and useful information for analysing the …
processing phase to excerpt more meaningful and useful information for analysing the …
Ambiguous D-means fusion clustering algorithm based on ambiguous set theory: Special application in clustering of CT scan images of COVID-19
P Singh, SS Bose - Knowledge-Based Systems, 2021 - Elsevier
Abstract Coronavirus Disease 2019 (COVID-19) has been considered one of the most
critical diseases of the 21st century. Only early detection can aid in the prevention of …
critical diseases of the 21st century. Only early detection can aid in the prevention of …
[PDF][PDF] Robust and sparse fuzzy k-means clustering.
The partition-based clustering algorithms, like K-Means and fuzzy K-Means, are most widely
and successfully used in data mining in the past decades. In this paper, we present a robust …
and successfully used in data mining in the past decades. In this paper, we present a robust …
Image enhancement and segmentation techniques for detection of knee joint diseases: A survey
Background: Knee bone diseases are rare but might be highly destructive. Magnetic
Resonance Imaging (MRI) is the main approach to identify knee cancer and its treatment …
Resonance Imaging (MRI) is the main approach to identify knee cancer and its treatment …
[HTML][HTML] Brain tumour segmentation from MRI using superpixels based spectral clustering
A Maruthamuthu - Journal of King Saud University-Computer and …, 2020 - Elsevier
The automated brain tumour segmentation method is becoming challenging in the field of
medical research as a brain tumour emerges with diverse size, shape and intensity. In this …
medical research as a brain tumour emerges with diverse size, shape and intensity. In this …
Self-weighted multi-view fuzzy clustering
Since the data in each view may contain distinct information different from other views as
well as has common information for all views in multi-view learning, many multi-view …
well as has common information for all views in multi-view learning, many multi-view …
Generalized possibilistic fuzzy c-means with novel cluster validity indices for clustering noisy data
A generalized form of Possibilistic Fuzzy C-Means (PFCM) algorithm (GPFCM) is presented
for clustering noisy data. A function of distance is used instead of the distance itself to damp …
for clustering noisy data. A function of distance is used instead of the distance itself to damp …