Principal component analysis in medical image processing: a study

Nandi, Dibyadeep; Ashour, Amira S.; Samanta, Sourav; Chakraborty, Sayan; Salem, Mohammed A.-M.; Dey, Nilanjan

doi:10.1504/ijim.2015.070024

Cited by 83 publications

(38 citation statements)

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“…PCA is used to obtain information about the existence of a spatial correlation between the different samples. We obtained the eigenvalues of the window since they provide information about intensity changes in different directions [28]. The 4 highest and the 2 lowest eigenvalues were selected.…”

Section: Window Featuresmentioning

confidence: 99%

Automatic identification and characterization of the epiretinal membrane in OCT images

Baamonde

Moura

Novo

et al. 2019

Biomed. Opt. Express

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Optical coherence tomography (OCT) is a medical image modality that is used to capture, non-invasively, high-resolution cross-sectional images of the retinal tissue. These images constitute a suitable scenario for the diagnosis of relevant eye diseases like the vitreomacular traction or the diabetic retinopathy. The identification of the epiretinal membrane (ERM) is a relevant issue as its presence constitutes a symptom of diseases like the macular edema, deteriorating the vision quality of the patients. This work presents an automatic methodology for the identification of the ERM presence in OCT scans. Initially, a complete and heterogeneous set of features was defined to capture the properties of the ERM in the OCT scans. Selected features went through a feature selection process to further improve the method efficiency. Additionally, representative classifiers were trained and tested to measure the suitability of the proposed approach. The method was tested with a dataset of 285 OCT scans labeled by a specialist. In particular, 3,600 samples were equally extracted from the dataset, representing zones with and without ERM presence. Different experiments were conducted to reach the most suitable approach. Finally, selected classifiers were trained and compared using different metrics, providing in the best configuration an accuracy of 89.35%.

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Section: Window Featuresmentioning

confidence: 99%

Automatic identification and characterization of the epiretinal membrane in OCT images

Baamonde

Moura

Novo

et al. 2019

Biomed. Opt. Express

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“…The exhibited outcomes and exchanges demonstrated additionally its effectiveness in different medicinal imaging applications. [8] This paper introduces an overview of the uses of PCA in the field of restorative picture preparing. In this contemplate, different restorative picture application-based PCA results are shown to demonstrate its effectiveness.…”

Section: Fig 4: Roc Curve Of the Methods Usedmentioning

confidence: 99%

Advancement of Principal Component Judgment for the Classification and Prediction of Alzheimer’s Disease

2019

ijrte

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Alzheimer is a dynamic issue of dementia, which deals with mind issue that assaults synapses, cerebrum cells, and nerves, memory, and practices and afterward finally causing dementia on old individuals. In spite of its significance, there is at present no remedy for it. In any case, there are drugs accessible on remedy that can help defer the advancement of the condition. Principal Component Analysis is an incredible system to recognize the examples of vast informational indexes, offers an in vogue factual strategy to dissect multivariate information by building a brief information portrayal utilizing the predominant Eigen vectors of the information covariance lattice. Along these lines, early conclusion of AD is basic for patient consideration and pertinent examines. In this paper, we have assessed a calculation utilizing Principal Component Analysis for its application in information investigation. In the exploration field, it is exceptionally hard to comprehend the expansive measure of information and is very tedious as well. In this way, so as to maintain a strategic distance from wastage of time and for the simplicity in understanding we have examined a PCA calculation that can diminish the gigantic component of the information. Principal Component Analysis (PCA) has been utilized in this paper to locate the base number of credits to improve the classifiers for quicker execution, cost-adequacy and precision. The strategy for PCA is utilized to pack the greatest measure of data into initial two sections of the changed lattice known as the vital parts by ignoring alternate vectors that conveys the immaterial data or repetitive information. Utilizing PCA we expect to locate the important highlights of the informational indexes. This paper proposes a system for forecast of Alzheimer sickness by discovering the most critical highlights important to Alzheimer Disease and furthermore different therapeutic picture application-based PCA results are displayed to demonstrate its productivity

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“…Then, the fused image is obtained by combination of the new high frequency and low frequency images. In the medical field, PCA has been applied in the fusion of MRI, CT, PET and US [41][42][43]. PCA can also combine with decomposition methods, such as IHS, the pyramid method, Discrete wavelet transform [44], the Curvelet transform, Contourlet transform [45] and Non-Subsampled Contourlet transform [31,[46][47][48][49][50][51][52][53][54][55][56].…”

Section: Principal Component Analysismentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.

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Principal component analysis in medical image processing: a study

Cited by 83 publications

References 0 publications

Automatic identification and characterization of the epiretinal membrane in OCT images

Automatic identification and characterization of the epiretinal membrane in OCT images

Advancement of Principal Component Judgment for the Classification and Prediction of Alzheimer’s Disease

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

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