Registration of Brain Medical Images Based on SURF Algorithm and R-RANSAC Algorithm

Gu, Zongyun; Cai, Li; Yin, Yong; Ya-tao, Ding; Kan, Hongxing

doi:10.11591/telkomnika.v12i3.4500

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Once the keypoints are matched, outliers are excluded using the M-estimator SAmple Consensus (MSAC) algorithm. The process of registration of the input image to the reference image is similar to [19] where R-RANSAC algorithm is used instead to find the outliers. Out of the inliers, 2 points are selected to transform the image acquired to the reference image in the database.…”

Section: Image Transformationmentioning

confidence: 99%

Automated PCB identification and defect-detection system (APIDS)

Rehman

Thang

Lai

2019

IJECE

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Ever growing PCB industry requires automation during manufacturing process to produce defect free products. Machine Vision is widely used as popular means of inspection to find defects in PCBs. However, it is still largely dependent on user input to select algorithm set for the PCB under inspection prior to the beginning of the process. Continuous increase in computation power of computers and image quality of image acquisition devices demands new methods for further automation. This paper proposes a new method to achieve further automation by identifying the type of PCB under inspection prior to begin defect inspection process. Identification of PCB is achieved by using local feature detectors SURF and ORB and using the orientation data acquired to transform the PCB image to the reference image for inspection of defects. A close-loop system is produced as a prototype to reflect the practicality of the idea. A Graphical User Interface was developed using MATLAB to present the proposed system. Test data contained 29 PCBs. Each PCB was tested 5 times for camera acquired images and 3 times for database images. The identification accuracy is 98.66% for database images and 100% for images acquired from the camera. The time taken to detect the model of PCB is recorded and is significantly lower for ORB based identification than SURF based. The system is also a close loop system which detects defects in PCB units. The detection of defects has highest accuracy of 92.3% for best controlled environment scenario. With controlled environment, the system could detect defects in PCB pertaining to smallest of components such as SMDs.

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Section: Image Transformationmentioning

confidence: 99%

Automated PCB identification and defect-detection system (APIDS)

Rehman

Thang

Lai

2019

IJECE

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“…The algorithm is robust and efficient due to the combination of SURF and R-RANSAC. The algorithm is fast as well as accurate when compared with the other methods [52]. Zhang et al proposes a method of registration that uses wavelet transform.…”

Section: Group-wise Image Registrationmentioning

confidence: 99%

A Review on Registration of Medical Images Using Graph Theoretic Approaches

Akshaya

Menon

2018

IJEECS

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<p>Image registration is very essential for image analysis especially in the medical field. The registered image helps us to find a lot of things like the presence of a tumor, aneurysm, and many more. Image registration is a process of aligning two images that were captured during different conditions that makes analyzing of images possible. There are various methods in which images can be registered. This paper mainly discusses the methods that use the graph approach to register the medical images. The main focus is on rigid and non-rigid registration techniques and gives a brief description of the methods present.</p>

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“…However, studies on how to deploy these devices among common people would be of greater use to humankind. Enough efforts are being put in the recent past to improve the health information technology [38].…”

Section: Future Recommendationsmentioning

confidence: 99%