Development of automatic framework for efficient characterization of brain lesions is a significant research concern due to the complex properties exhibited by the brain tissues. This study focuses on observing the properties of such composite structures in order to identify optimal features for characterizing the properties of normal and abnormal brain tissues. This work initially applies Fuzzy C Mean algorithm to identify the region of interest. After segmentation, four different types of features are extracted from the region of interest. These features include first-order parameters, Gray-level Co-occurrence Matrix (GLCM) parameters, Laws texture features, and Gray-Level Run-Length Matrix (GLRLM) parameters. These identification features were ranked in order of pertinence with the help of Mutual Information and Statistical Dependence-based feature ranking algorithms. Based on the inference obtained from the Mutual Information and Statistical Dependence-based feature ranking algorithms, twelve best features are selected for characterizing the properties of the normal and abnormal brain tissues. © Springer Nature Singapore Pte Ltd. 2018.

Karthik R

Electronics

School of Electronics Engineering

Chennai Campus

Anish Mukherjee

Abhishek Kanaujia

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Feature Ranking of Spatial Domain Features for Efficient Characterization of Stroke Lesions

Lecture Notes in Electrical Engineering Computational Signal Processing and Analysis

The principle restorative step in the treatment of ischemic stroke depends on how fast the lesion is delineated from the Magnetic Resonance Imaging (MRI) images. This will serve as a vital aid to estimate the extent of damage caused to the brain cells. However, manual delineation of the lesion is time-consuming and it is subjected to intra-observer and inter-observer variability. Most of the existing methods for ischemic lesion segmentation rely on extracting handcrafted features followed by application of a machine learning algorithm. Identifying such features demand multi-domain expertise in Neuro-radiology as well as Image processing. This can be accomplished by learning the features automatically using Convolutional Neural Network (CNN). To perform segmentation, the spatial arrangement of pixel needs to be preserved in addition to learning local features of an image. Hence, a deep supervised Fully Convolutional Network (FCN) is presented in this work to segment the ischemic lesion. The highlight of this research is the application of Leaky Rectified Linear Unit activation in the last two layers of the network for a precise reconstruction of the ischemic lesion. By doing so, the network was able to learn additional features which are not considered in the existing U-Net architecture. Also, an extensive analysis was conducted in this research to select optimal hyper-parameters for training the FCN. A mean segmentation accuracy of 0.70 has been achieved from the experiments conducted on ISLES 2015 dataset. Experimental observations show that our proposed FCN method is 10% better than the existing works in terms of Dice Coefficient. © 2019 Elsevier B.V.

Applied Soft Computing

A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network

Ischemic stroke is a severe neuro disorder typically characterized by a block inside a blood vessel supplying blood to the brain. It remains the third leading cause of death, after a heart attack and cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were the major imaging modalities used in diagnosing this disorder. While the CT imaging can be used at the primary stage, MRI proves to be a necessary aid for progressive diagnostic planning in the treatment of stroke injuries. Developing a fully automatic approach for lesion segmentation is a challenging issue due to the complex nature of the lesion structures. This research aims at examining the properties of such complex structures. It analyses the characteristics of the normal brain tissues and abnormal lesion structures using a three-level wavelet decomposition procedure. Four different wavelet functions namely Daubechies, Symlet, Coiflet and De-Meyer were applied to the different datasets and the resulting observations were examined based on their feature statistics. Experiments indicate that the feature statistics obtained using the Daubechies and the De-Meyer wavelets were able to distinguish between normal brain tissues and abnormal lesion structures.

Fulltext

ELCVIA Electronic Letters on Computer Vision and Image Analysis

A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries

Computer Speech & Language

Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits

Digital Signal Processing

Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction

BMC Bioinformatics

Random KNN feature selection - a fast and stable alternative to Random Forests

Journal	Data powered by TypesetLecture Notes in Electrical Engineering Computational Signal Processing and Analysis
Publisher	Data powered by TypesetSpringer Singapore
ISSN	1876-1100
Open Access	No