scholarly journals Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering

2018 ◽  
Vol 32 (2) ◽  
pp. 322-335 ◽  
Author(s):  
S. N. Kumar ◽  
A. Lenin Fred ◽  
P. Sebastin Varghese
Keyword(s):  
Lesion Segmentation ◽  
Mr Images ◽  
Spatial Feature ◽  
Suspicious Lesion ◽  
Fuzzy C Means ◽  
Breast Mr ◽  
Fuzzy C Means Clustering ◽  
Feature Based

scholarly journals Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed

2009 ◽  
Vol 36 (10) ◽  
pp. 4359-4369 ◽  
Author(s):  
Yunfeng Cui ◽  
Yongqiang Tan ◽  
Binsheng Zhao ◽  
Laura Liberman ◽  
Rakesh Parbhu ◽  
...  
Keyword(s):  
Malignant Lesion ◽  
Lesion Segmentation ◽  
Mr Images ◽  
Contrast Enhanced ◽  
Breast Mr

scholarly journals Brain Tumor Classification and Segmentation using DTCW Transform, Back Propagation Neural Network and Spatial Fuzzy C-Means Clustering

2020 ◽  
Vol 9 (5) ◽  
pp. 2073-2079
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Mri ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Mr Images ◽  
Data Set ◽  
Fuzzy C Means ◽  
Tumor Identification ◽  
Fuzzy C Means Clustering

A novel method is presented in this paper for finding brain tumor and classifying it using the back-propagation neural network is proposed. Spatial Fuzzy C-Means clustering is utilized for the segmentation of image to identify the influenced area of brain MRI picture. Automated detection of tumors in brain MR images is urgent in many diagnosis processes. Because of noise, blurred edges, the detection, and classification of brain tumor are very difficult. This paper presents one programmed brain tumor identification strategy to expand the exactness and yield and diminishing the determination time. The objective is ordering the tissues to three classes of typical, start and malignant. The size and the location tumor is very important for doctors for defining the treatment of tumor. The proposed determination strategy comprises of four phases, pre-processing of MR images, feature extraction, and classification. The features are extracted using Dual-Tree Complex wavelet transformation (DTCWT). Back Propagation Neural Network (BPN) is employed for finding brain tumor in MRI images. In the last stage, a productive scheme is proposed for segmentation depends on the Spatial Fuzzy C-Means Clustering. The performance analysis clearly proves that the proposed scheme is more efficient and the efficiency of the scheme is measured with sensitivity and specificity. The evaluation is performed on the image data set of 15 MRI images of brain.

Sign in / Sign up

Export Citation Format

Share Document