Robust pulmonary segmentation for chest radiography, combining enhancement, adaptive morphology and innovative active contours

Vital, Daniel Aparecido; Sais, Barbara Teixeira; Moraes, Matheus Cardoso

doi:10.1590/2446-4740.180035

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…Segmentation is an image processing process that divides an image into several parts for separating or finding an object in the image. The segmentation algorithm in this study aims to create an active contour model in the image that is similar to the previous study [18]. However, the image processing procedure used in this study is simpler.…”

Section: Methodsmentioning

confidence: 99%

Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network

Khoiro

Firdaus

Suaebah

et al. 2022

J. Phys.: Conf. Ser.

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The effect of segmentation on lung X-ray image classification has been analyzed in this study. The 150 lung x-ray images in this study were separated into 78 as training data, 30 as validation data, and 42 as testing in three categories: normal lungs, effusion lungs, and cancer lungs. In pre-processing, the images were modified by adaptive histogram equalization to improve image quality and increase image contrast. The segmentation aims to mark the image by contouring the lung area obtained from the thresholding and some morphological manipulation processes such as filling holes, area openings, and labelling. Image classification uses Convolutional Neural Network (CNN) with five convolution layers, an Adam optimizer, and 30 epochs. The segmentation effect is analyzed by comparing the classification performance of the segmented and unsegmented images. In the study, the unsegmented X-ray image dataset classification reached an overall accuracy of 59.52% in the network testing process. The segmented X-ray image dataset obtained greater accuracy, 73.81%. It indicated that the segmentation process could improve network performance because the input pattern of the segmented image is easier to classify. Furthermore, the segmentation technique in the study can be one of the alternatives to developing image classification technologies, especially for medical image diagnosis. Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network.

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Section: Methodsmentioning

confidence: 99%

Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network

Khoiro

Firdaus

Suaebah

et al. 2022

J. Phys.: Conf. Ser.

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“…Preprocessing involves removing the useless parts and redundant clutter to improve the image quality, allowing better results to be achieved to reduce FPs. For lung segmentation, the existing techniques can be divided into four categories: the threshold-based method, the variable model, and the shape or edge-based models [27]. In this paper, we use unsharp mask (UM) image sharpening technology to enhance the nodule signal in Charge eXchange Recombination Spectroscopy (CXRS).…”

Section: Methodsmentioning

confidence: 99%

A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning

Huang

Xue

2019

PLoS ONE

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Background Detection of pulmonary nodules is an important aspect of an automatic detection system. Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis and early treatment of lung cancer. Currently, the detection of pulmonary nodules depends mainly on doctor experience, which varies. This paper aims to address the challenge of pulmonary nodule detection more effectively. Methods A method for detecting pulmonary nodules based on an improved neural network is presented in this paper. Nodules are clusters of tissue with a diameter of 3 mm to 30 mm in the pulmonary parenchyma. Because pulmonary nodules are similar to other lung structures and have a low density, false positive nodules often occur. Thus, our team proposed an improved convolutional neural network (CNN) framework to detect nodules. First, a nonsharpening mask is used to enhance the nodules in computed tomography (CT) images; then, CT images of 512×512 pixels are segmented into smaller images of 96×96 pixels. Second, in the 96×96 pixel images which contain or exclude pulmonary nodules, the plaques corresponding to positive and negative samples are segmented. Third, CT images segmented into 96×96 pixels are down-sampled to 64×64 and 32×32 size respectively. Fourth, an improved fusion neural network structure is constructed that consists of three three-dimensional convolutional neural networks, designated as CNN-1, CNN-2, and CNN-3, to detect false positive pulmonary nodules. The networks’ input sizes are 32×32×32, 64×64×64, and 96×96×96 and include 5, 7, and 9 layers, respectively. Finally, we use the AdaBoost classifier to fuse the results of CNN-1, CNN-2, and CNN-3. We call this new neural network framework the Amalgamated-Convolutional Neural Network (A-CNN) and use it to detect pulmonary nodules. Findings Our team trained A-CNN using the LUNA16 and Ali Tianchi datasets and evaluated its performance using the LUNA16 dataset. We discarded nodules less than 5mm in diameter. When the average number of false positives per scan was 0.125 and 0.25, the sensitivity of A-CNN reached as high as 81.7% and 85.1%, respectively.

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“…A wavelet enhances the CXR images, and in the second step, OTSU thresholding is combined with mathematical morphology. For the third step, the active contour method improves the performance [ 18 ]. Ahmad et al proposed a Gaussian derivative filter that considered seven different orientations of top segment fields.…”

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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Robust pulmonary segmentation for chest radiography, combining enhancement, adaptive morphology and innovative active contours

Cited by 3 publications

References 32 publications

Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network

Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network

A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning

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

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