Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Contextual Clustering Based  Region Growing

Bhakkiyalakshmi, R; Ponnammal, P.; M, Srilekha.; .K, Abhishikt Sai

doi:10.14419/ijet.v7i2.24.12010

Cited by 2 publications

(2 citation statements)

References 6 publications

(8 reference statements)

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“…The proposed system was tested on 205 patient cases taken from the openly accessible online LIDC database. The experimental results obtained a sensitivity of 89.2% at 4.14 FPs/scan, and the system was found to be successful [101]. The results of the study showed that the genetic algorithm method developed by Boroczky et al for extracting features from private data obtained from lung CT scans (including 52 real nodules and 443 fake ones) was 100% accurate and 56.4% accurate [102].…”

Section: Classification: False Positive Reductionmentioning

confidence: 80%

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A review of convolutional neural network-based computer-aided lung nodule detection system

Sari

Sutikno²,

Soesanti³

et al. 2023

IJ-AI

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Worldwide, lung cancer is the leading cause of mortality and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided detection (CAD) systems for lung cancer diagnosis. Preprocessing, segmentation, detection, and classification are the stages of the CAD system. This review divides the preprocessing into three stages: image smoothing, edge sharpening, and noise removal. Additionally, lung segmentation is divided into three stages: histogram-based thresholding, linked component analysis, and lung extraction. The detecting phase aids in decreasing the workload. Several techniques are briefly described, including random forest, support vector machine (SVM), naive bayes, K-nearest neighbor (KNN), and convolutional neural network (CNN). Classification is the final stage; the image is then identified as containing or not possessing nodules. The prospect of incorporating CNN-based deep learning techniques into the CAD system is discussed. This paper is superior to other review studies on this topic due to its comprehensive examination of pertinent literature and structured presentation. We anticipate that our study may aid professional researchers and radiologists in developing more effective CAD systems for detecting lung cancer.

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Section: Classification: False Positive Reductionmentioning

confidence: 80%

“…Additionally, the test had a sensitivity of 85.91%. Han et al [101] and Boroczky et al [102] carried out yet another investigation in which the SVM method was utilized. In this particular investigation, the classification performed by feature-based SVM was dependent on categorical rules.…”

Section: Classification: False Positive Reductionmentioning

confidence: 99%

A review of convolutional neural network-based computer-aided lung nodule detection system

Sari

Sutikno²,

Soesanti³

et al. 2023

IJ-AI

View full text Add to dashboard Cite

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Breast Ultrasound Image Segmentation Algorithm Using Adaptive Region Growing and Variation Level Sets

Wang

et al. 2022

Mathematical Problems in Engineering

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To address the features of strong noise, blurred boundaries, and poor imaging quality in breast ultrasound images, we propose a method for segmenting breast ultrasound images using adaptive region growing and variation level sets. First, this method builds a template layer from the difference between the marked image and the original image. Second, the Otsu algorithm is used to measure the target and background using the maximum class variance method to set the threshold. Finally, through the level set of the pixel neighborhood, the boundary points of the adaptive region growth are specified by the level set of the pixel neighborhood, and it is therefore possible to accurately determine the contour perimeter and area of the lesion region. The results demonstrate that the value of Jaccard and Dice for benign tumors is greater than 0.99. Therefore, the segmentation effect of breast images can be achieved by utilizing a breast ultrasound image segmentation approach that uses adaptive region growth and variation level sets.

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Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Contextual Clustering Based Region Growing

Cited by 2 publications

References 6 publications

A review of convolutional neural network-based computer-aided lung nodule detection system

A review of convolutional neural network-based computer-aided lung nodule detection system

Breast Ultrasound Image Segmentation Algorithm Using Adaptive Region Growing and Variation Level Sets

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