# Image Quality Assessment to Emulate Experts' Perception in Lumbar MRI Using Machine Learning
## 2 Methods and Materials
### Objective Evaluation  
Image feature extraction was undertaken in Matlab, in a semi-automatic way. Some features were evaluated on a Region Of Interest (ROI), others over the entire slice. The ROIs were positioned manually within vertebral bodies, intervertebral discs, fatty tissues, psoas, and paravertebral muscle in three different slices located in the center of the acquired volume. Image manipulation was conducted by engineers, blinded to the process and results of the \"subjective evaluation\". The dataset is composed of three different cases of MR lumbar images, of \(T_{1}\)-weighting acquisitions in axial and sagittal orientation and \(T_{2}\)-weighting in sagittal orientation. Three different slices were obtained for each exam modality. On the one hand, for the sagittal exams (\(T_{1}\) and \(T_{2}\)), 26 features were extracted from each of the three different slices, obtaining a total of 78 variables. From these features, 12 of them were computed from the whole image and 14 from several ROIs (8 SNR, 2 CNR, 3 Uniformity, and 1 Image Sharpness in fat). Moreover, for the axial exams, 16 features were extracted from each of the three different slices, obtaining 48 variables. From these features, 12 of them were computed from the whole image and four from several ROIs (2 SNR, 1 CNR, and 1 Image Sharpness in fat). Some features were selected to depict different characteristics known to influence image perception, such as spatial resolution or presence of noise; other features correspond to a mathematical description of the image not directly related to human perception. Some of the features are sensitive to spatial resolution, such as pixel dimension, slice thickness, or quantification of \"image sharpness\" relative to the presence of borders within the image. Other features are sensitive to the presence of noise or signal homogeneity, such as signal-to-noise ratio (SNR),contrast-to-noise ratio (CNR), or \"uniformity\" of the signal within an ROI. Some features are sensitive to the presence of artifacts: we used the index proposed by [PERSON] et al. [25], and also quantification of the ratio of the energy present in the signal in the foreground and the background. In the case of aliasing artifacts, the background energy is altered. The Wang index is a no-reference image quality metric made initially to measure distortions caused by JPEG compression on natural images. This measure is explored here for its utility in the noise and intensity non-uniformity detection. It is used in its implementation made public by their authors ([[https://github.com/dcatteeu/JpegQuality](https://github.com/dcatteeu/JpegQuality)]([https://github.com/dcatteeu/JpegQuality](https://github.com/dcatteeu/JpegQuality)). accessed on 20 July 2019). Less \"intuitive\" characteristics were also included so that another approach of image description is taken into account, different from the one trying to quantify parameters that could explain human perception directly, such as contrast or spatial resolution. In this category, we find measures of entropy, spatial, and spectral flatness. Image representations based on histograms are quite popular, and entropy is among the most widely used. Image distortions have been observed to affect the histograms of pixel intensities [26]. The histogram-based Shannon entropy could be an indicator of noise and intensity non-uniformity. An unpredictable image, i.e., nonredundant, in the spatial domain, will tend to have a white or flat looking spectrum. Conversely, predictable images will possess colored spectra; that is, their spectral shapes exhibit peaks. The spectral flatness measure is widely used to quantify signal information and compressibility [27]. A complementary quantity has been proposed, spatial flatness, which quantifies image shape [28]. The features extracted from the images are detailed in Table 2. \(S\) represents the pixel intensity, \(\hat{S}_{i}\) represents the average of intensities in region \(i\).