Computer Image Registration Techniques Applied to Nuclear Medicine Images

Alves, Raquel S.; Tavares, João Manuel R. S.

doi:10.1007/978-3-319-15799-3_13

Cited by 26 publications

(14 citation statements)

References 95 publications

(89 reference statements)

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“…al [40] proposed a method that assumes nonrigid lung deformation and rigid tumor. They use the B-Spline-based nonrigid transformation to model the lung deformation while imposing rigid transformation on the tumor to preserve its volume and shape [104,105,106]. A 2D graph-cut algorithm is used to segment a 3D dataset.…”

Section: Selected Workmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

202

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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Section: Selected Workmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

202

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“…The metrics to evaluate mosaics are used in image registration when is necessary to measure the similarity between images, because provides a value that should be use for measurement of similarity between the mosaic image and the input images/frames [26,27,28,29]. According to [30] the metrics evaluate the quality images and to measure the degradation in digital images in order to improve the quality of the resultant image.…”

Section: Methodsmentioning

confidence: 99%

New Analysis Method Application in Metallographic Images through the Construction of Mosaics Via Speeded Up Robust Features and Scale Invariant Feature Transform

et al. 2015

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In many applications in metallography and analysis, many regions need to be considered and not only the current region. In cases where there are analyses with multiple images, the specialist should also evaluate neighboring areas. For example, in metallurgy, welding technology is derived from conventional testing and metallographic analysis. In welding, these tests allow us to know the features of the metal, especially in the Heat-Affected Zone (HAZ); the region most likely for natural metallurgical problems to occur in welding. The expanse of the Heat-Affected Zone exceeds the size of the area observed through a microscope and typically requires multiple images to be mounted on a larger picture surface to allow for the study of the entire heat affected zone. This image stitching process is performed manually and is subject to all the inherent flaws of the human being due to results of fatigue and distraction. The analyzing of grain growth is also necessary in the examination of multiple regions, although not necessarily neighboring regions, but this analysis would be a useful tool to aid a specialist. In areas such as microscopic metallography, which study metallurgical products with the aid of a microscope, the assembly of mosaics is done manually, which consumes a lot of time and is also subject to failures due to human limitations. The mosaic technique is used in the construct of environment or scenes with corresponding characteristics between themselves. Through several small images, and with corresponding characteristics between themselves, a new model is generated in a larger size. This article proposes the use of Digital Image Processing for the automatization of the construction of these mosaics in metallographic images. The use of this proposed method is meant to significantly reduce the time required to build the mosaic and reduce the possibility of failures in assembling the final image; therefore increasing efficiency in obtaining results and expediting the decision making process. Two different methods are proposed: One using the transformed Scale Invariant Feature Transform (SIFT), and the second using features extractor Speeded Up Robust Features (SURF). Although slower, the SIFT method is more stable and has a better performance than the SURF method and can be applied to real applications. The best results were obtained using SIFT with Peak Signal-to-Noise Ratio = 61.38, Mean squared error = 0.048 and mean-structural-similarity = 0.999, and processing time of 4.91 seconds for mosaic building. The methodology proposed shows be more promissory in aiding specialists during analysis of metallographic images.

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“…Moreover, the detection of MSP is required in registration [11] of medical images as the first step for spatial normalization [12] and anatomical standardization [13] of the brain images. However, legitimate evaluation of symmetric and asymmetric patterns in brain images is possible only when the symmetry axis or the symmetry plane (MSP) is accurately aligned and appropriately oriented within the coordinate system of the MRI scanner [14].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Automatic Midsagittal Plane Extraction in Brain MRI

Rehman

Lee

2018

Applied Sciences

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In this paper, a fully automatic and computationally efficient midsagittal plane (MSP) extraction technique in brain magnetic resonance images (MRIs) has been proposed. Automatic detection of MSP in neuroimages can significantly aid in registration of medical images, asymmetric analysis, and alignment or tilt correction (recenter and reorientation) in brain MRIs. The parameters of MSP are estimated in two steps. In the first step, symmetric features and principal component analysis (PCA)-based technique is used to vertically align the bilateral symmetric axis of the brain. In the second step, PCA is used to achieve a set of parallel lines (principal axes) from the selected two-dimensional (2-D) elliptical slices of brain MRIs, followed by a plane fitting using orthogonal regression. The developed algorithm has been tested on 157 real T 1 -weighted brain MRI datasets including 14 cases from the patients with brain tumors. The presented algorithm is compared with a state-of-the-art approach based on bilateral symmetry maximization. Experimental results revealed that the proposed algorithm is fast (<1.04 s per MRI volume) and exhibits superior performance in terms of accuracy and precision (a mean z-distance of 0.336 voxels and a mean angle difference of 0.06).2 of 23 misalignment of brain MRIs. A general phenomenon in brain MRI scanning is that many neuroimaging scanners produce tilted and distorted brain images. The tilt of the head is not always detectable, due to many reasons such as the health conditions, immobility of patients, imprecision of the data calibration systems, and the inexperience of the technicians. Consequently, the slices of the brain MRIs are no more alike within the same orientation, at either the axial or coronal level [15]. Disoriented and misaligned brain MRIs can betray visual inspection and prevalently yield erroneous clinical perception [16]. In summary, assessment of brain MRIs for any anomaly based on cross-referencing of brain hemispheres (left and right), either by a human expert or computer-based software could be affected by false geometrical representation. Consequently, it is essential to correct the tilt and realign the brain MRIs data before further analysis.Manual misalignment correction is extremely time-consuming and laborious to perform on a huge scale. It also demands an urbane knowledge of brain anatomy. Therefore, it is neither sufficient nor efficient. Alignment or tilt correction of brain MRIs is tantamount to realigning the MSP with the center of the image matrix or image coordinate system [14]. If the MSP is computed precisely, the orientation problem of the MRI volume can be resolved. Thus, the tilt of the head volume can be assessed and adjusted. An ideal MSP can be defined as a virtual geometric plane passing through the interhemispheric fissure (IF) [17], about which the three-dimensional (3-D) anatomical structure of the brain (such as the ventricles, anterior/posterior commissures, corpus callosum, thalamus) exhibits maximum bilateral symmetry [18].Previously, ...

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Computer Image Registration Techniques Applied to Nuclear Medicine Images

Cited by 26 publications

References 95 publications

Automatic 3D pulmonary nodule detection in CT images: A survey

Automatic 3D pulmonary nodule detection in CT images: A survey

New Analysis Method Application in Metallographic Images through the Construction of Mosaics Via Speeded Up Robust Features and Scale Invariant Feature Transform

An Efficient Automatic Midsagittal Plane Extraction in Brain MRI

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