Diagnosis of Hippocampal Sclerosis from Clinical Routine Head MR Images Using Structure-constrained Super-Resolution Network

Image registration is a powerful tool in medical image analysis and facilitates the clinical routine in several aspects. There are many well established elastic registration methods, but none of them can so far preserve discontinuities in the displacement field. These discontinuities appear in particular at organ boundaries during the breathing induced organ motion. In this paper, we exploit the fact that motion segmentation could play a guiding role during discontinuity preserving registration. The motion segmentation is embedded in a continuous cut framework guaranteeing convexity for motion segmentation. Furthermore we show that a primal-dual method can be used to estimate a solution to this challenging variational problem. Experimental results are presented for MR images with apparent breathing induced sliding motion of the liver along the abdominal wall.

Download Full-text

Classification of brain lesions on magnetic resonance imaging using superpixel, PSO and convolutional neural network

10.5753/wvc.2019.7640 ◽

2019 ◽

Author(s):

Carolina L. S. Cipriano ◽

Giovanni L. F. Da Silva ◽

Jonnison L. Ferreira ◽

Aristófanes C. Silva ◽

Anselmo Cardoso De Paiva

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Active Regions ◽

Pso Algorithm ◽

Resonance Imaging ◽

Mr Images ◽

Clinical Routine ◽

The Brain ◽

Manual Classification

One of the most severe and common brain tumors is gliomas. Manual classiﬁcation of injuries of this type is a laborious task in the clinical routine. Therefore, this work proposes an automatic method to classify lesions in the brain in 3D MR images based on superpixels, PSO algorithm and convolutional neural network. The proposed method obtained results for the complete, central and active regions, an accuracy of 87.88%, 70.51%, 80.08% and precision of 76%, 84%, 75% for the respective regions. The results demonstrate the difﬁculty of the network in the classiﬁcation of the regions found in the lesions.

Download Full-text

Realizing the Effective Detection of Tumor in Magnetic Resonance Imaging using Cluster-Sparse Assisted Super-Resolution

The Open Biomedical Engineering Journal ◽

10.2174/1874120702115010170 ◽

2021 ◽

Vol 15 (1) ◽

pp. 170-179

Author(s):

Kathiravan Srinivasan ◽

Ramaneswaran Selvakumar ◽

Sivakumar Rajagopal ◽

Dimiter Georgiev Velev ◽

Branislav Vuksanovic

Keyword(s):

Magnetic Resonance ◽

Super Resolution ◽

Mr Images ◽

Self Similarity ◽

Significant Research ◽

Non Local ◽

Brain Mr Images ◽

Texture Detection ◽

Sr Method ◽

Robust To Noise

Recently, significant research has been done in Super-Resolution (SR) methods for augmenting the spatial resolution of the Magnetic Resonance (MR) images, which aids the physician in improved disease diagnoses. Single SR methods have drawbacks; they fail to capture self-similarity in non-local patches and are not robust to noise. To exploit the non-local self-similarity and intrinsic sparsity in MR images, this paper proposes the use of Cluster-Sparse Assisted Super-Resolution. This SR method effectively captures similarity in non-locally positioned patches by training on clusters of patches using a self-adaptive dictionary. This method of training also leads to better edge and texture detection. Experiments show that using Cluster-Sparse Assisted Super-Resolution for brain MR images results in enhanced detection of lesions leading to better diagnosis.

Download Full-text