A review of algorithms for medical image segmentation and their applications to the female pelvic cavity
Z Ma, JMRS Tavares, RN Jorge… - Computer Methods in …, 2010 - Taylor & Francis
This paper aims to make a review on the current segmentation algorithms used for medical
images. Algorithms are classified according to their principal methodologies, namely the …
images. Algorithms are classified according to their principal methodologies, namely the …
Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions
The explosive growth of data in volume, velocity and diversity that are produced by medical
applications has contributed to abundance of big data. Current solutions for efficient data …
applications has contributed to abundance of big data. Current solutions for efficient data …
Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images
The fuzzy C-means (FCM) algorithm has got significant importance due to its unsupervised
form of learning and more tolerant to variations and noise as compared to other methods in …
form of learning and more tolerant to variations and noise as compared to other methods in …
An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images
Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help
of an MRI scanner. With the slice images obtained using an MRI scanner, certain image …
of an MRI scanner. With the slice images obtained using an MRI scanner, certain image …
[PDF][PDF] An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map
T Logeswari, M Karnan - International Journal of Computer Theory and …, 2010 - Citeseer
Image segmentation denotes a process of partitioning an image into distinct regions. A large
variety of different segmentation approaches for images have been developed. Among …
variety of different segmentation approaches for images have been developed. Among …
Morphological edge detection and brain tumor segmentation in Magnetic Resonance (MR) images based on region growing and performance evaluation of modified …
CJJ Sheela, G Suganthi - Multimedia Tools and Applications, 2020 - Springer
The medical image processing has become indispensable with an increased demand for
systematic and efficient detection of brain tumor in a short period of time. There are various …
systematic and efficient detection of brain tumor in a short period of time. There are various …
State-of-the-art methods for brain tissue segmentation: A review
Brain tissue segmentation is one of the most sought after research areas in medical image
processing. It provides detailed quantitative brain analysis for accurate disease diagnosis …
processing. It provides detailed quantitative brain analysis for accurate disease diagnosis …
A review on the current segmentation algorithms for medical images
Z Ma, JMRS Tavares, RMN Jorge - International conference on …, 2009 - scitepress.org
This paper makes a review on the current segmentation algorithms used for medical images.
Algorithms are divided into three categories according to their main ideas: the ones based …
Algorithms are divided into three categories according to their main ideas: the ones based …
Role of the product in the transformation of a catalyst to its active state
GJ Hutchings, A Desmartin-Chomel, R Olier, JC Volta - Nature, 1994 - nature.com
OXIDE catalysts, which are used in a broad range of important industrial processes1–4, are
generally prepared in the form of a precursor which is converted into the active catalytic form …
generally prepared in the form of a precursor which is converted into the active catalytic form …
Improving projected fuzzy K-means clustering via robust learning
Fuzzy K-Means clustering has been an attractive research area for many multimedia tasks.
Due to the interference of the noise and outliers, the performance of fuzzy K-Means …
Due to the interference of the noise and outliers, the performance of fuzzy K-Means …