Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models

Lee, Wen-Li; Chang, Koyin; Hsieh, Kai‐Sheng

doi:10.1007/s11517-015-1412-6

Cited by 15 publications

(7 citation statements)

References 39 publications

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“…Fuzzy c-means clustering [28,30,37] Better performance compared to K-means The lower value of β requires more iterations Active contour and morphology [29,39] Active contour can estimate the real lung boundary…”

Section: Gamma Correction Is Requiredmentioning

confidence: 99%

“…Anatomical structure segmentation of the chest can be divided into two groups of conventional handcrafted features and deep feature-based methods. Starting from the baseline of handcrafted features-based methods that just consider the single class lung segmentation [2] using local features, researchers have mainly focussed on the general image processing-based methods for the chest anatomy segmentation, as presented in studies [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. As this study is based on multiclass deep learning-based semantic segmentation, we mainly focus on learned feature-based literature.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction.

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Section: Gamma Correction Is Requiredmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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“…This section provides a general description of machine learning techniques and will help understanding their applications in the field of radiology, as described in subsequent sections. [46,45] roughness, granulation, and homogeneity Gabor features [47,48,49] Co-occurrence [50] Curvelet-based [51,52] Wavelet-based [ The linear model uses linear functions to separate the data yet is not suitable for non-linear cases. SVM is one way to separate non-linear models using different kernel functions.…”

Section: Overview Of Machine Learning Methodsmentioning

confidence: 99%

Radiological images and machine learning: Trends, perspectives, and prospects

Zhang

Sejdić

2019

Computers in Biology and Medicine

156

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The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computeraided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.

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“…Lee et al [117] presented an unsupervised LFS method based on multi‐resolution fractal feature vector. HVPP analysis is performed to obtain ROI.…”

Section: Lfs Methodsmentioning

confidence: 99%