Limitations of the SSIM quality metric in the context of diagnostic imaging

Pambrun, Jean-François; Noumeir, Rita

doi:10.1109/icip.2015.7351345

Cited by 56 publications

(31 citation statements)

References 11 publications

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“…It is also possible to use image metrics, such as ones shortlisted in Section IV-A , as a distance metric if the chosen metric is differentiable. However, metrics like [52] are intended to be used primarily on natural and not medical images, which means that using such approaches could yield little performance gain.…”

Section: Discussionmentioning

confidence: 99%

3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images

Hou

Khanal

Alansary

et al. 2018

IEEE Trans. Med. Imaging

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Limited capture range, and the requirement to provide high quality initialization for optimization-based 2-D/3-D image registration methods, can significantly degrade the performance of 3-D image reconstruction and motion compensation pipelines. Challenging clinical imaging scenarios, which contain significant subject motion, such as fetal in-utero imaging, complicate the 3-D image and volume reconstruction process. In this paper, we present a learning-based image registration method capable of predicting 3-D rigid transformations of arbitrarily oriented 2-D image slices, with respect to a learned canonical atlas co-ordinate system. Only image slice intensity information is used to perform registration and canonical alignment, no spatial transform initialization is required. To find image transformations, we utilize a convolutional neural network architecture to learn the regression function capable of mapping 2-D image slices to a 3-D canonical atlas space. We extensively evaluate the effectiveness of our approach quantitatively on simulated magnetic resonance imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline. Our learning based registration achieves an average spatial prediction error of 7 mm on simulated data and produces qualitatively improved reconstructions for heavily moving fetuses with gestational ages of approximately 20 weeks. Our model provides a general and computationally efficient solution to the 2-D/3-D registration initialization problem and is suitable for real-time scenarios.

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Section: Discussionmentioning

confidence: 99%

3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images

Hou

Khanal

Alansary

et al. 2018

IEEE Trans. Med. Imaging

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“…SSIM evaluates how similar the brightness, contrast, and structural differences are compared to the original image. So, it is not suitable for evaluating our image [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

Image Pre-Processing Method of Machine Learning for Edge Detection with Image Signal Processor Enhancement

2021

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Even though computer vision has been developing, edge detection is still one of the challenges in that field. It comes from the limitations of the complementary metal oxide semiconductor (CMOS) Image sensor used to collect the image data, and then image signal processor (ISP) is additionally required to understand the information received from each pixel and performs certain processing operations for edge detection. Even with/without ISP, as an output of hardware (camera, ISP), the original image is too raw to proceed edge detection image, because it can include extreme brightness and contrast, which is the key factor of image for edge detection. To reduce the onerousness, we propose a pre-processing method to obtain optimized brightness and contrast for improved edge detection. In the pre-processing, we extract meaningful features from image information and perform machine learning such as k-nearest neighbor (KNN), multilayer perceptron (MLP) and support vector machine (SVM) to obtain enhanced model by adjusting brightness and contrast. The comparison results of F1 score on edgy detection image of non-treated, pre-processed and pre-processed with machine learned are shown. The pre-processed with machine learned F1 result shows an average of 0.822, which is 2.7 times better results than the non-treated one. Eventually, the proposed pre-processing and machine learning method is proved as the essential method of pre-processing image from ISP in order to gain better edge detection image. In addition, if we go through the pre-processing method that we proposed, it is possible to more clearly and easily determine the object required when performing auto white balance (AWB) or auto exposure (AE) in the ISP. It helps to perform faster and more efficiently through the proactive ISP.

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“…These criteria evaluated the performance of different penalties from various standpoints, though some of them presented potential limitations. For example, MSE-based criteria (ISNR and PSNR) do not always accurately model perceptual quality, CNR can become meaningless when applied to the ROI and background with constant intensity because it achieves infinity, while SSIM is not stable enough in regions of low intensity variance [38].…”

Section: Evaluation Criteriamentioning

confidence: 99%

Structure-adaptive CBCT reconstruction using weighted total variation and Hessian penalties

Shi¹,

Sun²,

Sun³

et al. 2016

Biomed. Opt. Express

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Abstract:The exposure of normal tissues to high radiation during cone-beam CT (CBCT) imaging increases the risk of cancer and genetic defects. Statistical iterative algorithms with the total variation (TV) penalty have been widely used for low dose CBCT reconstruction, with state-of-the-art performance in suppressing noise and preserving edges. However, TV is a first-order penalty and sometimes leads to the so-called staircase effect, particularly over regions with smooth intensity transition in the reconstruction images. A second-order penalty known as the Hessian penalty was recently used to replace TV to suppress the staircase effect in CBCT reconstruction at the cost of slightly blurring object edges. In this study, we proposed a new penalty, the TV-H, which combines TV and Hessian penalties for CBCT reconstruction in a structure-adaptive way. The TV-H penalty automatically differentiates the edges, gradual transition and uniform local regions within an image using the voxel gradient, and adaptively weights TV and Hessian according to the local image structures in the reconstruction process. Our proposed penalty retains the benefits of TV, including noise suppression and edge preservation. It also maintains the structures in regions with gradual intensity transition more successfully. A majorization-minimization (MM) approach was designed to optimize the objective energy function constructed with the TV-H penalty. The MM approach employed a quadratic upper bound of the original objective function, and the original optimization problem was changed to a series of quadratic optimization problems, which could be efficiently solved using the Gauss-Seidel update strategy. We tested the reconstruction algorithm on two simulated digital phantoms and two physical phantoms. Our experiments indicated that the TV-H penalty visually and quantitatively outperformed both TV and Hessian penalties.

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Limitations of the SSIM quality metric in the context of diagnostic imaging

Cited by 56 publications

References 11 publications

3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images

3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images

Image Pre-Processing Method of Machine Learning for Edge Detection with Image Signal Processor Enhancement

Structure-adaptive CBCT reconstruction using weighted total variation and Hessian penalties

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