A Review of Point Feature Based Medical Image Registration

Guan, Shaopeng; Wang, Tianmiao; Cai, Meng; Junchen, Wang

doi:10.1186/s10033-018-0275-9

Cited by 42 publications

(21 citation statements)

References 88 publications

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“…A real time-based computer vision interface was built in order to recognize and localize the targeted UAV. Among the local feature-based algorithms [30]- [32], the ORB was employed due to its fast image processing time [32], [33]. In preliminary studies on classes of local feature-based algorithms, ORB with low feature-points detection (less than 1,000) was the most efficient and ensured promising characteristics (i.e., computational efficiency, the efficiency of feature-matching per feature-point, speed efficiency, etc.…”

Section: E Object Recognitionmentioning

confidence: 99%

Deployable Hook Retrieval System for UAV Rescue and Delivery

Nguyen

Jung

et al. 2021

IEEE Access

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The rapid development of unmanned aerial vehicles (UAVs) has helped expand their practical use to many industrial applications. However, UAVs sometimes suffer from a flight time limitation and/or a loss in communication. Such undesired malfunctions can endanger public safety and incur economic losses. This paper presents a new class of UAV that can retrieve a disabled or malfunctioned UAV from the ground. We developed a deployable hook retrieval system (DHRS) which integrates three principal mechanisms (i.e., deployment, slider-linkage-release, and hook release). Each mechanism plays a role in deploying and retrieving multiple hooks while using a simple control strategy. Through a Finite Element Method simulation, the hook was topologically optimized in order to achieve a high strength while reducing weight. The deployed multiple hooks allow the device to capture the target regardless of its orientation. Due to these design strategies, object recognition using a computer vision was simply demonstrated by exploiting ORB and FLANN algorithms. Through an experimental study, we discussed the target range, success rate, and the practical uses that the DHRS could achieve. The results show that the proposed designs were versatile and consistently successful in capturing the targets while addressing constraints such as power consumption, computational load, and lack of prior knowledge or information about the target.

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Section: E Object Recognitionmentioning

confidence: 99%

Deployable Hook Retrieval System for UAV Rescue and Delivery

Nguyen

Jung

et al. 2021

IEEE Access

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“…Intermodal image registration can be a means of engaging with the problems related to the alignment of two images having different areas of view and different modalities, resolutions, and slice orientations. The expression "image registration" is applied to indicate the mapping equivalent points" procedure in both modalities, known as spatial transformation [12]. During this process, two images, namely the target and the reference, are aligned into a single coordinate system.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Optimal Algorithm Selection in Multimodal Medical Image Registration

Elkeshreu

Basir

2021

IRA-JAS

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Many medical applications benefit from the diversity inherent in imaging technologies to obtain more reliable diagnoses and assessments. Typically, the images obtained from multiple sources are acquired at distinct times and from different viewpoints, rendering a multitude of challenges for the registration process. Furthermore, different areas of the human body require disparate registration functional capabilities and degrees of accuracy. Thus, the benefit attained from the image multiplicity hinges heavily on the imaging modalities employed as well as the accuracy of the alignment process. It is no surprise then that a wide range of registration techniques has emerged in the last two decades. Nevertheless, it is widely acknowledged that despite the many attempts, no registration technique has been able to deliver the required accuracy consistently under diverse operating conditions. This paper introduces a novel method for achieving multimodal medical image registration based on exploiting the complementary and competitive nature of the algorithmic approaches behind a wide range of registration techniques. First, a thorough investigation of a wide range of registration algorithms is conducted for the purpose of understanding and quantifying their registration capabilities as well as the influence of their control parameters. Subsequently, a supervised randomized machine learning strategy is proposed for selecting the best registration algorithm for a given registration instance, and for determining the optimal control parameters for such algorithm. Several experiments have been conducted to verify the capabilities of the proposed selection strategy with respect to registration reliability, accuracy, and robustness.

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“…HOG focuses on the shape of the region of interest that clearly describes the edges of images with gradient and orientation. Localized portions or regions are formed by breaking the complete image into pieces [9,10]. These descriptors later form histogram for the gradient and orientation of the pixels.…”

Section:  Histogram Of Oriented Gradient Featuresmentioning

confidence: 99%

Detection of Renal carcinoma in Ultrasound Images using HOG and SURF features

Thamizhvani*,

Chandrasekaran,

Dhivya

et al. 2019

IJRTE

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Cancer can be defined as the abnormal growth of cells in any region of the human. Cancer cells possess a special property called metastasis that involves the movement of cells from one location to another location. Renal cancer is becoming predominant and there are different types. One among them is Renal cell carcinoma mainly occurs in the renal tubules. In this study, ultrasound images are considered for the detection of renal cell carcinoma. The images undergo pre-processing to remove speckle noises. The region of interest is defined using region growing technique. Later Feature descriptors like histogram of oriented gradient features and speeded up robust features are extracted from the segmented region for the analysis of carcinoma. Texture features are also derived along with these descriptors. These features are classified using Adaptive Support Vector Machine for the diagnosis of the renal cell carcinoma from normal images. With the performance of the classifier, it is defined that feature descriptors illustrate the region of carcinoma more effectively

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A Review of Point Feature Based Medical Image Registration

Cited by 42 publications

References 88 publications

Deployable Hook Retrieval System for UAV Rescue and Delivery

Deployable Hook Retrieval System for UAV Rescue and Delivery

Optimal Algorithm Selection in Multimodal Medical Image Registration

Detection of Renal carcinoma in Ultrasound Images using HOG and SURF features

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