The purpose of this research is to analyse various algorithms for feature extraction in CT images and thereby developing an efficient scheme for feature extraction that supports effective visualisation of three-dimensional (3D) bony structures. An extensive study has been made on the single-scale and multi-scale spatial domain feature extraction algorithms. In order to overcome the issues associated with spatial domain feature extraction algorithms, a frequency domain-based scheme involving curvelet transform has been introduced. The significant features are extracted by designing a filter based on the statistical parameters of the curvelet coefficients across multiple scales. The slices with significant features are stacked along the z-plane to generate a point cloud. The effectiveness of the proposed curvelet-based algorithm is proved by comparing it with other methods based on Feature Similarity Index Measure (FSIM). Feature similarity was found to be between 85% and 90% for the curvelet-based feature extraction scheme. © Copyright 2016 Inderscience Enterprises Ltd.

Menaka R

Electronics

School of Electronics Engineering

Chennai Campus

Karthik R

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

International Journal of Biomedical Engineering and Technology

Automation in plant disease detection and diagnosis is one of the challenging research areas that has gained significant attention in the agricultural sector. Traditional disease detection methods rely on extracting handcrafted features from the acquired images to identify the type of infection. Also, the performance of these works solely depends on the nature of the handcrafted features selected. This can be addressed by learning the features automatically with the help of Convolutional Neural Networks (CNN). This research presents two different deep architectures for detecting the type of infection in tomato leaves. The first architecture applies residual learning to learn significant features for classification. The second architecture applies attention mechanism on top of the residual deep network. Experiments were conducted using Plant Village Dataset comprising of three diseases namely early blight, late blight, and leaf mold. The proposed work exploited the features learned by the CNN at various processing hierarchy using the attention mechanism and achieved an overall accuracy of 98% on the validation sets in the 5-fold cross-validation. © 2019 Elsevier B.V.

Fulltext

Applied Soft Computing

Attention embedded residual CNN for disease detection in tomato leaves

IEEE Transactions on Image Processing

FSIM: A Feature Similarity Index for Image Quality Assessment

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Segmentation of brain tumors in MRI images using multi-scale gradient vector flow

2011 IEEE International Symposium on Medical Measurements and Applications

Multi-resolution analysis using curvelet and wavelet transforms for medical imaging

Annals of Anatomy - Anatomischer Anzeiger

Three-dimensional surface models of detailed lumbosacral structures reconstructed from the Visible Korean

Electronics Letters

Formula for Harris corner detector

A novel feature extraction scheme for visualisation of 3D anatomical structures

Journal	International Journal of Biomedical Engineering and Technology
Publisher	Inderscience Publishers
ISSN	1752-6418
Open Access	No