Image segmentation for lung region in chest X-ray images using edge detection and morphology

Saad, Mohd Zuwairi Mat; Muda, Zurina; Sahari, Noraidah; Hamid, Hamzaini Abdul

doi:10.1109/iccsce.2014.7072687

Cited by 54 publications

(28 citation statements)

References 14 publications

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“…Harris corner detector [34,36] Convolutional mask refines the contour Edge detection is affected by noise…”

Section: Results Are Affected By Overlapped Regionsmentioning

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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“…Harris corner detector [34,36] Convolutional mask refines the contour Edge detection is affected by noise…”

Section: Results Are Affected By Overlapped Regionsmentioning

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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“…There are no methods developed, to date, for segmenting X-ray images of COVID-19 patients. Still, there are several models proposed for CXR segmentation in pneumonia (24–26).…”

Section: Results and Discusionsmentioning

confidence: 99%

Curbing the AI-induced enthusiasm in diagnosing COVID-19 on chest X-Rays: the present and the near-future

Burlacu

Criṣan-Dabija

Popa

et al. 2020

Preprint

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In the current context of COVID-19 pandemic, a rapid and accessible screening tool based on image processing of chest X-rays (CXRs) using machine learning (ML) approaches would be much needed. Initially, we intended to create and validate an ML software solution able to discriminate on the basis of the CXR between SARS-CoV-2-induced bronchopneumonia and other bronchopneumonia etiologies. A systematic search of PubMed, Scopus and arXiv databases using the following search terms ["artificial intelligence" OR "deep learning" OR "neural networks"], AND ["COVID-19" OR "SARS-CoV-2"] AND ["chest X-ray" OR "CXR" OR "X-ray"] found 14 recent studies. Most of them declared to be able to confidently identify COVID-19 based on CXRs using deep neural networks. Firstly, weaknesses of artificial intelligence (AI) solutions were analyzed, tackling the issues with datasets (from both medical and technical points of view) and the vulnerability of used algorithms. Then, arguments were provided for why our study design is stronger and more realistic than the previously quoted papers, balancing the possible false expectations with facts. The authors consider that the potential of AI use in COVID-19 diagnosis on CXR is real. However, scientific community should be careful in interpreting statements, results and conclusions regarding AI use in imaging. It is therefore necessary to adopt standards for research and publication of data, because it seems that in the recent months scientific reality suffered manipulations and distortions. Also, a call for responsible approaches to the imaging methods in COVID-19 is raised. It seems mandatory to follow some rigorous approaches in order to provide with adequate results in daily routine. In addition, the authors intended to raise public awareness about the quality of AI protocols and algorithms and to encourage public sharing of as many CXR images with common quality standards.

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“…Several studies have been conducted on lung segmentation using conventional image processing techniques such as edge detection, threshold, and clustering [ 9 ]. However, the image processing methods have relatively simple algorithms and exhibit poor segmentation performance when the input image contains noise.…”

Section: Related Workmentioning

confidence: 99%

Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network

Kim

Lee

2021

Sensors

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Accurate identification of the boundaries of organs or abnormal objects (e.g., tumors) in medical images is important in surgical planning and in the diagnosis and prognosis of diseases. In this study, we propose a deep learning-based method to segment lung areas in chest X-rays. The novel aspect of the proposed method is the self-attention module, where the outputs of the channel and spatial attention modules are combined to generate attention maps, with each highlighting those regions of feature maps that correspond to “what” and “where” to attend in the learning process, respectively. Thereafter, the attention maps are multiplied element-wise with the input feature map, and the intermediate results are added to the input feature map again for residual learning. Using X-ray images collected from public datasets for training and evaluation, we applied the proposed attention modules to U-Net for segmentation of lung areas and conducted experiments while changing the locations of the attention modules in the baseline network. The experimental results showed that our method achieved comparable or better performance than the existing medical image segmentation networks in terms of Dice score when the proposed attention modules were placed in lower layers of both the contracting and expanding paths of U-Net.

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Image segmentation for lung region in chest X-ray images using edge detection and morphology

Cited by 54 publications

References 14 publications

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Curbing the AI-induced enthusiasm in diagnosing COVID-19 on chest X-Rays: the present and the near-future

Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network

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