Segmentation of Lungs from Chest X Rays Using Firefly Optimized Fuzzy C-Means and Level Set Algorithm

Jangam, Ebenezer; Rao, A. C.

doi:10.1007/978-981-13-9184-2_27

Cited by 9 publications

(7 citation statements)

References 28 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

View full text Add to dashboard Cite

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“…Jangam et al presented a hybrid segmentation scheme that utilized an optimized clustering approach to exclude the lung field from the background in CXR images [ 17 ]. Vital et al introduced an automatic system for lung field segmentation.…”

Section: Related Workmentioning

confidence: 99%

CardioNet: Automatic Semantic Segmentation to Calculate the Cardiothoracic Ratio for Cardiomegaly and Other Chest Diseases

Jafar

Hameed²,

Akram³

et al. 2022

JPM

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Semantic segmentation for diagnosing chest-related diseases like cardiomegaly, emphysema, pleural effusions, and pneumothorax is a critical yet understudied tool for identifying the chest anatomy. A dangerous disease among these is cardiomegaly, in which sudden death is a high risk. An expert medical practitioner can diagnose cardiomegaly early using a chest radiograph (CXR). Cardiomegaly is a heart enlargement disease that can be analyzed by calculating the transverse cardiac diameter (TCD) and the cardiothoracic ratio (CTR). However, the manual estimation of CTR and other chest-related diseases requires much time from medical experts. Based on their anatomical semantics, artificial intelligence estimates cardiomegaly and related diseases by segmenting CXRs. Unfortunately, due to poor-quality images and variations in intensity, the automatic segmentation of the lungs and heart with CXRs is challenging. Deep learning-based methods are being used to identify the chest anatomy segmentation, but most of them only consider the lung segmentation, requiring a great deal of training. This work is based on a multiclass concatenation-based automatic semantic segmentation network, CardioNet, that was explicitly designed to perform fine segmentation using fewer parameters than a conventional deep learning scheme. Furthermore, the semantic segmentation of other chest-related diseases is diagnosed using CardioNet. CardioNet is evaluated using the JSRT dataset (Japanese Society of Radiological Technology). The JSRT dataset is publicly available and contains multiclass segmentation of the heart, lungs, and clavicle bones. In addition, our study examined lung segmentation using another publicly available dataset, Montgomery County (MC). The experimental results of the proposed CardioNet model achieved acceptable accuracy and competitive results across all datasets.

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“…Antibiotics are used to treat bacterial pneumonia, while supportive treatment is required for viral pneumonia, which makes the diagnostic accuracy very significant. Diagnosis of pneumonia is typically by analyzing x-rays of the chest, that is, by radiographic examination [1]. If there's a lack of radiologists, the diagnosis speed would be reflect negatively.…”

Section: Introductionmentioning

confidence: 99%

“…There are many works relating to the use features in chest radiographs for pneumonia detection. Ebenezer J et al, proposed firefly based fuzzy c-means for chest x rays images segmentation [1]. X. Wang et al, used deep convolution neural network based "reading chest x-rays", to recognize and locate common patterns of disease trained with image-level labels only [9].…”

Section: Introductionmentioning

confidence: 99%

Pneumonia detection using butterfly optimization and hybrid butterfly optimization algorithm

Khaleel

Ahmed

2021

Bulletin EEI

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Pneumonia affects so many people around the world, which leads to many of them being killed. In order to identify and diagnose the disease, the patient must first undergo an x-ray scan of the chest (CXR). Disease will be identified according to the CXR images. Software diagnostic tools are used to help decision-making and to promote the pneumonia diagnosis process. From these tools is the gray level distribution moments (GLDM) algorithm which is used for CXR image features extraction, and we used the meta-heuristic algorithm representing the basic butterfly optimization algorithm using lèvy flight (BOALF) and the modified butterfly optimization algorithm (BOARN) to detect pneumonia on the basis of these extracted features. And then we've also been making hybrid between the BOA with fuzzy membership function to get as novel method called it fuzzy butterfly optimization algorithm (FBOA). These methods were based on various x-ray images of the chest. In testing phase, the proposed method obtained the highest diagnostic rate of the disease compared to the other two methods in this work.

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Segmentation of Lungs from Chest X Rays Using Firefly Optimized Fuzzy C-Means and Level Set Algorithm

Cited by 9 publications

References 28 publications

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

CardioNet: Automatic Semantic Segmentation to Calculate the Cardiothoracic Ratio for Cardiomegaly and Other Chest Diseases

Pneumonia detection using butterfly optimization and hybrid butterfly optimization algorithm

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