Assistive Technology Intervention in Healthcare

Shruti Jain, Sudip Paul

• Publisher: CRC Press
• Year: 2021
• ISBN:
  9781000514070
• Edition: 1
• Topic: Computer Science
• Subtopic: Information Technology
• Index: Computer Science

Assistive Technology Intervention in Healthcare focuses on various applications of intelligent techniques in biomedical engineering and health informatics. It aims to create awareness about disability reduction and recovery of accidental disability with the help of various rehabilitative systems. Novel technologies in disability treatment, management and assistance, including healthcare devices and their utility from home to hospital, are described. The book deals with simulation, modeling, measurement, control, analysis, information extraction and monitoring of physiological data in clinical medicine and biology.

Features

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• Covers the latest evolutionary approaches to solve optimization problems in the biomedical engineering field

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• Explains machine learning–based approaches to improvement in health engineering areas

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• Reviews the IoT, cloud computing and data analytics in healthcare informatics

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• Discusses modeling and simulations in the design of biomedical equipment

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• Explores monitoring of physiological data

This book is aimed at researchers and graduate students in biomedical engineering, clinical engineering and bioinformatics.

1 Detection of Scoliosis from Anteroposterior X-Ray Images

Hritam Basak and Rohit Kundu

DOI: 10.1201/9781003207856-1

CONTENTS

1.1 Introduction: Background and Driving Forces

1.2 Materials and Methods

1.2.1 Dataset Descriptions

1.2.2 Isolation of the Spine

1.2.3 Detection of Vertebrae

1.2.4 Segmentation of Vertebrae

1.2.5 Measurement of the Cobb Angle

1.3 Results and Discussion

1.3.1 Evaluation of Segmentation

1.3.2 Evaluation of Cobb Angle Measurement

1.4 Conclusion and Future Work

References

1.1 INTRODUCTION: BACKGROUND AND DRIVING FORCES

The human spinal cord consists of 33 vertebrae named cervical (7), thoracic (12), lumbar (5), sacral (5) and coccygeal (4). Among them, the upper 24 vertebrae are movable, while the lower 9 are fixed [15]. Scoliosis refers to a condition where the otherwise straight human spine has a lateral curvature, forming an angle, and the spine becomes “C”- or “S”-shaped.

This angle, called the “Cobb angle” [14], is the angle formed between the most tilted vertebrae above and below the apex of the curvature and is the measure of the severity of the disease. A Cobb angle in the range of 10 to 20 degrees is deemed mild scoliosis; it is moderate when the angle is between 20 and 40 degrees and severe when the angle is more than 40 degrees [7]. The diagnosis of scoliosis is done by determining the Cobb angle through the analysis of computed tomography (CT) scans, X-ray scans or magnetic resonance imaging (MRI).

The need for surgery can be avoided with early diagnosis of the disease. However, the detection of scoliosis from X-ray or MRI images sometimes turn out to be extremely difficult due to morphological differences between patients. Also, there are limitations on the intensity of radioactive rays that can be used, which often results in underdeveloped radiograph images.

Deep learning is an essential component of artificial intelligence (AI), which gained popularity due to its ability to learn non-redundant, informative features on its own through back-propagation, unlike machine-learning techniques, where handcrafted features need to be extracted and selected manually [23, 12].

In the literature, there exist numerous computer-aided methods, but all of them have shortcomings. Mathematical filter-based segmentation methods like the active contour model [2] and charged-particle models [18] were proposed to segment the vertebrae before calculating the Cobb angle. These methods suffered from excess computational requirements and were susceptible to errors due to slight variations in images. Alharbi et al. [1] proposed a fuzzy spatial relation associated with deformable models to perform semi-supervised segmentation of the vertebrae. Recently convolutional neural network (CNN) and deep neural network models have been widely used for this purpose, with a significant improvement in the result [3]. Fu et al. [9] proposed a lightweight multitask network that primarily detects the corner points followed by accurate contour segmentation. In the literature, there exist other deep-learning based object-detection and segmentation methods [8, 6], but they suffer from the problem of extreme computation requirements.

In this chapter, we develop an automated method for the detection of vertebrae to measure the Cobb angle for scoliosis using deep learning and image processing. For this, first, the region of interest (RoI), that is, the vertebral column in this case, is extracted from the images using image processing. Then the vertebral column is segmented from the RoI by locating spine centers (SCs) and the spine boundary. Next, deep learning is used to segment the vertebrae from the vertebral column and, finally, the Cobb angle is measured using a method known as minimum bounding rectangle (MBR). In this research, we use the VGG-19 [19] convolutional neural network as the backbone for U-Net [17] architecture to perform semantic segmentation of the vertebral column from X-ray images. Semantic segmentation refers to the pixel-level classification of each image; that is, each pixel is classified as belonging to an object class or background class.

1.2 MATERIALS AND METHODS

In this section, we describe in detail the proposed method for determining scoliosis by calculating the Cobb angle from anteroposterior (AP) X-ray images.

1.2.1 DATASET DESCRIPTIONS

In this chapter, we use the publicly available dataset by Wu et al. [22] consisting of 609 X-ray anteroposterior images of the spine. Each image contains four landmarks for every vertebra for measurement of the Cobb angle.

1.2.2 ISOLATION OF THE SPINE

The first step of the process is to select the spinal column from the...

Table of contents

• Cover Page
• Half Title Page
• Title Page
• Copyright Page
• Contents Page
• Preface Page
• Acknowledgments Page
• Editors Page
• Contributors Page
• Chapter 1 Detection of Scoliosis from Anteroposterior X-Ray Images
• Chapter 2 Review of Healthcare Management
• Chapter 3 IoT Technologies for Smart Healthcare
• Chapter 4 A Novel Design of Digital Circuits Using Reversible Logic Synthesis
• Chapter 5 Denoising of Biomedical Images Using Two-Dimensional Fourier-Bessel Series Expansion-Based Empirical Wavelet Transform
• Chapter 6 Alert System for Epileptic Seizures
• Chapter 7 Early Diabetic Retinopathy Detection Using Augmented Continuous Particle Swarm Optimization Clustering
• Chapter 8 Computational Fluid Dynamics of Carotid Artery Blood Flow for Low-Gravity Environments
• Chapter 9 Predictions of Loan E-Signing Based on Financial Status of Applicants Using Machine Learning
• Chapter 10 Heel-End- and Toe-End-Based Gait Kinematics of Female Young Adults: Implications of Therapeutic Intervention
• Chapter 11 Blockchain-Based Electronic Health Record System Enforced by Ensemble Multi-Contract Approach
• Chapter 12 EMG Features as an Indicator of Muscle Strength for the Assessment of Non-Specific Low Back Pain
• Chapter 13 IoT-Based Data Management and Systems for Public Healthcare
• Chapter 14 Biosensors in Healthcare
• Chapter 15 Early Detection of Autism Spectrum Disorder Using EEG, MRI and Behavioral Data: A Review
• Index