A computationally efficient 3D/2D registration method based on image gradient direction probability density function

Ghafurian, Soheil; Hacihaliloglu, Ilker; Metaxas, Dimitris N.; Tan, Virak; Li, Kang

doi:10.1016/j.neucom.2016.07.070

Cited by 16 publications

(6 citation statements)

References 28 publications

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“…Figure 3 shows the second stage of the method proposed, where after having calculated the features vector (FV M ,l ) for each one of 1,300 images (N ) of the ten different classes (M ), we proceed to calculate the features vector for each particular class (V µ F M ). Considering that there are 16 spectral indicators or predictors (W ) for each image, therefore also V µ F M will have 16 spectral indicators that will define each class based on a specific probability density function (pdf ) for each spectral indicator and class, i.e., pdf (F M (i)) [31]- [33], as (4) shows:…”

Section: Determination Of General Correlation Vector For Each Classmentioning

confidence: 99%

Multi-Class Diagnosis of Skin Lesions Using the Fourier Spectral Information of Images on Additive Color Model by Artificial Neural Network

2021

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This article presents a new methodology to diagnostics ten types of skin lesions based on the image' s Fourier spectral information in an additive color model. All spectral information and correlation coefficients between the skin lesions classes conform the input signals to an Artificial Neural Network. In general, the results show the well-defined classification for all the skin lesions classes based on the high values for Accuracy, Precision, Sensitivity, and Specificity metrics performance and a reduced images misclassification percentage (≈5.9%) for the Testing sub-dataset, and less for Training (≈2.8%) and Validation (≈5.6%) sub-dataset even considering the strange objects, not-clarity, and black sections in some images analyzed. The general achieved classification Accuracy, Precision, Sensitivity, and Specificity percentages of the proposed method are 99.33 %, 94.16 %, 92.9 %, and 99.63 %, respectively. In particular, the skin lesions related to Basal Cell Carcinoma, Seborrhoeic Keratosis, and Melanocytic Nevus present the best performance regarding the Receiver Operating Characteristics, while the Pyogenic Granuloma was the worst classified.INDEX TERMS Artificial neural networks, biomedical computing, image processing, medical diagnostic imaging, Fourier spectral analysis.

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Section: Determination Of General Correlation Vector For Each Classmentioning

confidence: 99%

Multi-Class Diagnosis of Skin Lesions Using the Fourier Spectral Information of Images on Additive Color Model by Artificial Neural Network

2021

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“…In addition, only one 2D image may be enough to achieve 2D/3D registration in the projection and back-projection strategy. However, the reconstruction method requires at least two or more 2D images to reconstruct 3D images with high enough accuracy to achieve accurate and robust 3D/3D registration for high-resolution 3D images collected before surgery [4,25,26]. In general, the greater the number of intraoperative 2D images, the higher the registration accuracy.…”

Section: Introductionmentioning

confidence: 99%

2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

et al. 2022

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Image-guided surgery (IGS) can reduce the risk of tissue damage and improve the accuracy and targeting of lesions by increasing the surgery’s visual field. Three-dimensional (3D) medical images can provide spatial location information to determine the location of lesions and plan the operation process. For real-time tracking and adjusting the spatial position of surgical instruments, two-dimensional (2D) images provide real-time intraoperative information. In this experiment, 2D/3D medical image registration algorithm based on the gray level is studied, and the registration based on normalized cross-correlation is realized. The Gaussian Laplacian second-order differential operator is introduced as a new similarity measure to increase edge information and internal detail information to solve single information and small convergence regions of the normalized cross-correlation algorithm. The multiresolution strategy improves the registration accuracy and efficiency to solve the low efficiency of the normalized cross-correlation algorithm.

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“…The high complexity of projection space dramatically affects the timeliness of registration. Ghafurian et al [30] considered that the 2D/3D registration problem needs to search the complex solution space, leading to many calculations, so they proposed a spatial parameter-decoupling method to achieve registration. According to the dimensional differences between the registration images, image registration can be divided into 2D/2D registration, 2D/3D registration, 3D/3D registration, and time-series registration [31].…”

Section: Introductionmentioning

confidence: 99%

2D/3D Multimode Medical Image Alignment Based on Spatial Histograms

et al. 2022

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The key to image-guided surgery (IGS) technology is to find the transformation relationship between preoperative 3D images and intraoperative 2D images, namely, 2D/3D image registration. A feature-based 2D/3D medical image registration algorithm is investigated in this study. We use a two-dimensional weighted spatial histogram of gradient directions to extract statistical features, overcome the algorithm’s limitations, and expand the applicable scenarios under the premise of ensuring accuracy. The proposed algorithm was tested on CT and synthetic X-ray images, and compared with existing algorithms. The results show that the proposed algorithm can improve accuracy and efficiency, and reduce the initial value’s sensitivity.

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A computationally efficient 3D/2D registration method based on image gradient direction probability density function

Cited by 16 publications

References 28 publications

Multi-Class Diagnosis of Skin Lesions Using the Fourier Spectral Information of Images on Additive Color Model by Artificial Neural Network

Multi-Class Diagnosis of Skin Lesions Using the Fourier Spectral Information of Images on Additive Color Model by Artificial Neural Network

2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

2D/3D Multimode Medical Image Alignment Based on Spatial Histograms

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