Principal component analysis (PCA) in medical image processing using digital imaging and communications in medicine (dicom) medical images

Hospitals and clinics produce a great number of medical images that are stored in large databases. Content-based medical image retrieval systems (CBMIRs) is one of the solutions to access rapidly and efficiently to these databases using their visual content. In this paper, we propose a new algorithm for medical image indexing. It consists on a combination of bio-orthogonal CDF wavelet transform (WT) based on lifting scheme and principal component analysis (PCA). We use this WT to decompose images, then we apply the PCA method to reduce the number of features and select the pertinent components which represent image signature. Finally, Euclidean distance is used to retrieve most similar image from databases when query image is submitted. We have tested our algorithm on the retinal image database. The results obtained by our algorithm are compared with several published methods cited in the literature and shows an efficiency of 80%, which is significantly higher and much faster than recent methods in CBMIRs domain.

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A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis

Applied Sciences ◽

10.3390/app9224733 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4733

Author(s):

Cuiping Shao ◽

Huiyun Li ◽

Zheng Wang ◽

Jiayan Fang

Keyword(s):

Image Processing ◽

Principal Component Analysis ◽

Principal Component ◽

Error Resilience ◽

Component Analysis ◽

Cmos Technology ◽

Error Correcting Codes ◽

Memory Storage ◽

Protection Scheme ◽

Bit Flip

Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.

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Fusion of Multimodal Color Medical Images Using Quaternion Principal Component Analysis

Communications in Computer and Information Science - Data Science ◽

10.1007/978-981-10-6388-6_33 ◽

2017 ◽

pp. 401-413

Author(s):

Qamar Nawaz ◽

Xiao Bin ◽

Li Weisheng ◽

Isma Hamid

Keyword(s):

Principal Component Analysis ◽

Medical Images ◽

Principal Component ◽

Component Analysis

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The Location of Cancer Area Based on Region Growing Algorithm in Medical Image

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.760-762.1552 ◽

2013 ◽

Vol 760-762 ◽

pp. 1552-1555 ◽

Cited By ~ 1

Author(s):

Jing Jing Wang ◽

Xiao Wei Song ◽

Mei Fang

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Medical Image ◽

Medical Images ◽

Medical Image Processing ◽

Region Growing ◽

Challenging Problem ◽

Correct Judgment ◽

Threshold Segmentation ◽

Seed Points

Image segmentation in medical image processing has been extensively used which has also been applied in different fields of medicine to assist doctors to make the correct judgment and grasp the patient's condition. However, nowadays there are no image threshold segmentation techniques that can be applied to all of the medical images; so it has became a challenging problem. In this paper, it applies a method of identifying edge of the tissues and organs to recognize its contour, and then selects a number of seed points on the contour range to locate the cancer area by region growing. And finally, the result has demonstrated that this method can mostly locate the cancer area accurately.

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Reduction of speckle noise from medical images using principal component analysis image fusion

2014 9th International Conference on Industrial and Information Systems (ICIIS) ◽

10.1109/iciinfs.2014.7036562 ◽

2014 ◽

Cited By ~ 6

Author(s):

Indrajeet Kumar ◽

H.S Bhadauria ◽

Jitendra Virmani ◽

Jyoti Rawat

Keyword(s):

Principal Component Analysis ◽

Image Fusion ◽

Medical Images ◽

Principal Component ◽

Speckle Noise ◽

Component Analysis

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IBK – A NEW TOOL FOR MEDICAL IMAGE PROCESSING

Science and Technology Development Journal ◽

10.32508/stdj.v13i4.2165 ◽

2010 ◽

Vol 13 (4) ◽

pp. 20-27

Author(s):

Linh Duy Tran ◽

Linh Quang Huynh

Keyword(s):

Image Processing ◽

Diagnostic Imaging ◽

3D Reconstruction ◽

Programming Language ◽

Medical Image ◽

Medical Images ◽

Rapid Development ◽

Medical Image Processing ◽

Matlab Programming

Along with the rapid development of diagnostic imaging equipment, software for medical image processing has played an important role in helping doctors and clinicians to reach accurate diagnoses. In this paper, methods to build a multipurpose tool based on Matlab programming language and its applications are presented. This new tool features enhancement, segmentation, registration and 3D reconstruction for medical images obtained from commonly used diagnostic imaging equipment.

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