A Novel Supervised Approach for Segmentation of Lung Parenchyma from Chest CT for Computer-Aided Diagnosis

Darmanayagam, Shiloah Elizabeth; Harichandran, Khanna Nehemiah; Cyril, Sunil Retmin Raj; Kannan, A.

doi:10.1007/s10278-012-9539-6

Cited by 23 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 6: calculate the random motion for random diffusion RD i using Equation ( 12) which is characterized with high diffusion speed and a random vector. RD i = RD max × ð1 − I/ I max Þ × δ: (12) In the above formula, RD max is the maximum random diffusion speed; δ is the random directional vector; I is the current iteration number, and I max is the maximum number of iterations.…”

Section: Rd Maxmentioning

confidence: 99%

See 1 more Smart Citation

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Murugesan

Bhuvaneswaran

Nehemiah

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).

show abstract

Section: Rd Maxmentioning

confidence: 99%

“…A CDSS to diagnose Urticaria using Bayes classification is proposed in [8]. CDSSs to diagnose lung disorders are proposed in [9][10][11][12][13][14]. A CDSS to diagnose the severity of gait disturbances using a Q-backpropogated time delay neural network on patients affected by Parkinson's disease is proposed in [15].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Murugesan

Bhuvaneswaran

Nehemiah

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…This can also lead to cases of misdiagnosis or missed diagnosis; in addition, diagnosis is based on a doctor's judgement, which is subjective, so the effectiveness of diagnosis varies. Training a doctor is time consuming and requires significant capital investment, and it is not uncommon in many hospitals to find it difficult to meet the current clinical demand [21].…”

Section: Computer-aided Mri Diagnosis Of Breast Cancer Using Convolutional Neural Network and Adversarial Learningmentioning

confidence: 99%

Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

2020

View full text Add to dashboard Cite

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of breast tissue are discussed. The algorithms are based on recent advances in multi-dimensional signal processing and aim to advance current state-of-the-art computer-aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi-parametric computer-aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi-supervised deep learning and self-supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE-MRI. Since some of the approaches discussed are also based on time-lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.

show abstract

“…Biomedical images produced by some imaging techniques, such as CT scanning, indeed contain some visual noise. The appearance of noise restricts the radiologist's performance to differentiate inhomogeneous regions in the image [4]. This noise may lead to uncertainty in interpreting the image and degrade diagnostic performance [5].…”

Section: Introductionmentioning

confidence: 99%

Implementing a non-local means method to CTA data of aortic dissection

Fitria

Morariu

Pauli

et al. 2021

J. Teknol. dan Sist. Komput

View full text Add to dashboard Cite

It is necessary to conserve important information, like edges, details, and textures, in CT aortic dissection images, as this helps the radiologist examine and diagnose the disease. Hence, a less noisy image is required to support medical experts in performing better diagnoses. In this work, the non-local means (NLM) method is conducted to minimize the noise in CT images of aortic dissection patients as a preprocessing step to produce accurate aortic segmentation results. The method is implemented in an existing segmentation system using six different kernel functions, and the evaluation is done by assessing DSC, precision, and recall of segmentation results. Furthermore, the visual quality of denoised images is also taken into account to be determined. Besides, a comparative analysis between NLM and other denoising methods is done in this experiment. The results showed that NLM yields encouraging segmentation results, even though the visualization of denoised images is unacceptable. Applying the NLM algorithm with the flat function provides the highest DSC, precision, and recall values of 0.937101, 0.954835, and 0.920517 consecutively.

show abstract

A Novel Supervised Approach for Segmentation of Lung Parenchyma from Chest CT for Computer-Aided Diagnosis

Cited by 23 publications

References 16 publications

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

Implementing a non-local means method to CTA data of aortic dissection

Contact Info

Product

Resources

About