Abnormality detection of specific brain structure in MR images based on m ulti-atlas and texture descriptor

2015 ◽  
pp. 205-210
Keyword(s):  
Brain Structure ◽  
Abnormality Detection ◽  
Texture Descriptor ◽  
Mr Images

Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images

2015 ◽  
Vol 15 (01) ◽  
pp. 1550001 ◽  
Author(s):  
A. Suruliandi ◽  
G. Murugeswari ◽  
P. Arockia Jansi Rani
Keyword(s):  
Image Segmentation ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Rate Sensitivity ◽  
Digital Mammogram ◽  
Abnormality Detection ◽  
Texture Descriptor ◽  
Texture Pattern ◽  
Local Texture ◽  
Mammogram Images

Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images.

scholarly journals A pediatric brain structure atlas from T1-weighted MR images

2006 ◽  
Author(s):  
Zuyao Y. Shan ◽  
Carlos Parra ◽  
Qing Ji ◽  
Robert J. Ogg ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Brain Structure ◽  
Mr Images ◽  
Pediatric Brain

Reliability of brain structure morphometry in hydrocephalic children using MR images

1996 ◽  
Vol 14 (6) ◽  
pp. 649-655 ◽  
Author(s):  
Michael E. Brandt ◽  
Timothy P. Bohan ◽  
Kelly Thorstad ◽  
Steven R. McCauley ◽  
Kevin C. Davidson ◽  
...  
Keyword(s):  
Brain Structure ◽  
Mr Images

scholarly journals A Digital Pediatric Brain Structure Atlas from T1-Weighted MR Images

2006 ◽  
pp. 332-339 ◽  
Author(s):  
Zuyao Y. Shan ◽  
Carlos Parra ◽  
Qing Ji ◽  
Robert J. Ogg ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Brain Structure ◽  
Mr Images ◽  
Pediatric Brain

scholarly journals Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 938
Author(s):  
Takaaki Sugino ◽  
Toshihiro Kawase ◽  
Shinya Onogi ◽  
Taichi Kin ◽  
Nobuhito Saito ◽  
...  
Keyword(s):  
Brain Structure ◽  
Class Imbalance ◽  
Cross Entropy ◽  
Loss Functions ◽  
Mr Images ◽  
Distance Map ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Frequency Weighting ◽  
Class Segmentation

Brain structure segmentation on magnetic resonance (MR) images is important for various clinical applications. It has been automatically performed by using fully convolutional networks. However, it suffers from the class imbalance problem. To address this problem, we investigated how loss weighting strategies work for brain structure segmentation tasks with different class imbalance situations on MR images. In this study, we adopted segmentation tasks of the cerebrum, cerebellum, brainstem, and blood vessels from MR cisternography and angiography images as the target segmentation tasks. We used a U-net architecture with cross-entropy and Dice loss functions as a baseline and evaluated the effect of the following loss weighting strategies: inverse frequency weighting, median inverse frequency weighting, focal weighting, distance map-based weighting, and distance penalty term-based weighting. In the experiments, the Dice loss function with focal weighting showed the best performance and had a high average Dice score of 92.8% in the binary-class segmentation tasks, while the cross-entropy loss functions with distance map-based weighting achieved the Dice score of up to 93.1% in the multi-class segmentation tasks. The results suggested that the distance map-based and the focal weightings could boost the performance of cross-entropy and Dice loss functions in class imbalanced segmentation tasks, respectively.

An Iterative Implementation of Level Set for Precise Segmentation of Brain Tissues and Abnormality Detection from MR Images

2017 ◽  
Vol 63 (6) ◽  
pp. 769-783 ◽  
Author(s):  
Sudipta Roy ◽  
Debnath Bhattacharyya ◽  
Samir Kumar Bandyopadhyay ◽  
Tai-Hoon Kim
Keyword(s):  
Level Set ◽  
Abnormality Detection ◽  
Mr Images ◽  
Brain Tissues
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