An approach for X-ray image mosaicing based on Speeded-Up Robust Features

Gong, Junhui; Zhang, Junhua; An, Zhenzhou; Zhao, Weiwei; Liu, Huimin

doi:10.1109/icwamtip.2012.6413530

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…There has been a rapid growth in medical image stitching using feature based approach. Different feature based detectors are namely Scale Invariant feature Transform (SIFT) [2], Harris [4] [5] detector, Speedup Robust Feature detector (SURF) [3], (PCA-SIFT), Principal Component Analysis SIFT [6]. Depending on the nature of the problem appropriate detectors are chosen.…”

Section: Introductionmentioning

confidence: 99%

Automated Mosaicking of Medical x-ray Images using SIFT Algorithm

Harihar¹,

R²

2021

JUSST

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Image registration is a process of joining any number of images that have similar overlapping regions of the same scene in order to make a panoramic image. In the field of medical, multimedia, and image processing applications image registration process stands challenging. The work presented here on medical images can be applicable for long limb operations and scoliosis operations. Traditional x-ray machines produce a single frame of x-ray image containing a portion of the body part, but they can not generate a large view of body x-ray image in a single frame. This problem can be solved by creating panoramic images by combining multiple images. The work proposed in this paper can automatically produce panoramic x-ray images by stitching multiple x-ray images. The proposed work uses scale-invariant feature transform (SIFT) for mosaicking x-ray images as a local feature point extractor which uses the difference of Gaussian (DOG) and invariant to orientation and scale. Based on the location relationship of x-ray images, random sample consensus (RANSAC) is incorporated to remove the effect of mismatched point pairs in x-ray images and to generate the panoramic view. The performance of the system is computed by using structural and time constraint parameters and is compared with different feature detection techniques. The experimental results show that combing SIFT and RANSAC yields less processing time with an increase in similarity measures.

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Section: Introductionmentioning

confidence: 99%

Automated Mosaicking of Medical x-ray Images using SIFT Algorithm

Harihar¹,

R²

2021

JUSST

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“…There has been great progress in medical images stitching system using features based approach. They are SIFT (Scale Invariant Feature Transform) [2], SURF (Speedup Robust Feature detector) [3], HARRIS detector [4] [5], Principal Component Analysis SIFT (PCA-SIFT) [6], and FAST (Features from Accelerated Segment Test). A good feature detector can be choose depending on the nature of the problem.…”

Section: Introductionmentioning

confidence: 99%

“…In feature extraction, SURF detector was used. The weak point is that it can't detect correct features in image with noise [4] [5]. Singla presented images stitching using x-ray images by combining SIFT and SURF [12].…”

Section: Introductionmentioning

confidence: 99%

Automatic Stitching of Medical Images Using Feature Based Approach

Win¹,

Kitjaidure²,

Hamamoto³

2019

Adv. sci. technol. eng. syst. j.

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Image stitching is a process of creating a panoramic image by combining multiple images that have overlapping regions of the same scene. It is a challenging topic in image processing, multimedia, and medical applications. The proposed system can be applied in medical applications for scoliosis operations and other long limb operations. The problem of the traditional x-ray machine is the narrow view. So, it can't produce the large view of body x-ray image in a single frame. Therefore, this problem is solved by combining two or more x-ray images into a panorama one. This paper proposes a system which automatically stitches the x-ray images and produced a panorama x-ray image. The proposed stitching method is based on feature based approach, ORB (Oriented FAST and Rotated BRIEF). In feature detection stage, Oriented FAST approach is used. In feature description stage, Rotated BRIEF approach is applied. The two important criterias for determining the stitching performance are stitched image quality and processing time. Therefore, the aim of my proposed system is to produce a panorama x-ray image with high resolution and low processing time based on feature extraction approach. We compared our proposed method with three different features detectors. SIFT, SURF, and Harris corner detectors were tested and measured the rate of correct features detection and computation time. Finally, we measured the quality of result images that produced by stitching system of different feature based methods. According to the experimental results, ORB approach can produce high quality panorama image with least processing time.

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“…Given for example, in [60] [3] [55] [1] one of the color bands of the input RGB images are taken into consideration while obtaining the transformation parameters. On the other hand in [21] [37] [61] the RGB images are first converted to grayscale and then transformation parameters are obtained. In either case, after finding the optimal transformation parameters, all the color bands are processed and combined together during the re-projection step in order to produce color mosaic.…”

Section: Introductionmentioning

confidence: 99%

An In Depth Review Paper on Numerous Image Mosaicing Approaches and Techniques

Abdul-Rahim¹,

Zheng-sheng²

2018

IJEMR

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Image mosaicing is one of the most important subjects of research in computer vision at current. Image mocaicing requires the integration of direct techniques and feature based techniques. Direct techniques are found to be very useful for mosaicing large overlapping regions, small translations and rotations while feature based techniques are useful for small overlapping regions. Feature based image mosaicing is a combination of corner detection, corner matching, motion parameters estimation and image stitching. Furthermore, image mosaicing is considered the process of obtaining a wider field-of-view of a scene from a sequence of partial views, which has been an attractive research area because of its wide range of applications, including motion detection, resolution enhancement, monitoring global land usage, and medical imaging. Numerous algorithms for image mosaicing have been proposed over the last two decades. In this paper the authors present a review on different approaches for image mosaicing and the literature over the past few years in the field of image masaicing methodologies. The authors take an overview on the various methods for image mosaicing. This review paper also provides an in depth survey of the existing image mosaicing algorithms by classifying them into several groups. For each group, the fundamental concepts are first clearly explained. Finally this paper also reviews and discusses the strength and weaknesses of all the mosaicing groups.

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An approach for X-ray image mosaicing based on Speeded-Up Robust Features

Cited by 5 publications

References 7 publications

Automated Mosaicking of Medical x-ray Images using SIFT Algorithm

Automated Mosaicking of Medical x-ray Images using SIFT Algorithm

Automatic Stitching of Medical Images Using Feature Based Approach

An In Depth Review Paper on Numerous Image Mosaicing Approaches and Techniques

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