Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering

Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result.

Benchmark of filter methods for feature selection in high-dimensional gene expression survival data

Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies for data with a survival outcome are rare. We compare 14 filter methods for feature selection based on 11 high-dimensional gene expression survival data sets. The aim is to provide guidance on the choice of filter methods for other researchers and practitioners. We analyze the accuracy of predictive models that employ the features selected by the filter methods. Also, we consider the run time, the number of selected features for fitting models with high predictive accuracy as well as the feature selection stability. We conclude that the simple variance filter outperforms all other considered filter methods. This filter selects the features with the largest variance and does not take into account the survival outcome. Also, we identify the correlation-adjusted regression scores filter as a more elaborate alternative that allows fitting models with similar predictive accuracy. Additionally, we investigate the filter methods based on feature rankings, finding groups of similar filters.

Estimation of Tire Normal Forces including Suspension Dynamics

Tire normal forces are difficult to measure, but information on the vehicle normal force can be used in many automotive engineering applications, e.g., rollover detection and vehicle and wheel stability. Previous papers use algebraic equations to estimate the tire normal force. In this article, the estimation of tire normal force is formulated as an input estimation problem. Two observers are proposed to solve this problem by using a quarter-car suspension model. First, the Youla Controller Output Observer framework is presented. It converts the estimation problem into a control problem and produces a Youla parameterized controller as observer. Second, a Kalman filter approach is taken and the input estimation problem is addressed with an Unbiased Minimum Variance Filter. Both methods use accelerometer and suspension deflection sensors to determine the vehicle normal force. The design of the observers is validated in simulation and a sensitivity analysis is performed to evaluate their robustness.

Evaluation of Feature Selection Techniques in a Multifrequency Large Amplitude Pulse Voltammetric Electronic Tongue

An electronic tongue is a device composed of a sensor array that takes advantage of the cross sensitivity property of several sensors to perform classification and quantification in liquid substances. In practice, electronic tongues generate a large amount of information that needs to be correctly analyzed, to define which interactions and features are more relevant to distinguish one substance from another. This work focuses on implementing and validating feature selection methodologies in the liquid classification process of a multifrequency large amplitude pulse voltammetric (MLAPV) electronic tongue. Multi-layer perceptron neural network (MLP NN) and support vector machine (SVM) were used as supervised machine learning classifiers. Different feature selection techniques were used, such as Variance filter, ANOVA F-value, Recursive Feature Elimination and model-based selection. Both 5-fold Cross validation and GridSearchCV were used in order to evaluate the performance of the feature selection methodology by testing various configurations and determining the best one. The methodology was validated in an imbalanced MLAPV electronic tongue dataset of 13 different liquid substances, reaching a 93.85% of classification accuracy.

Image Restoration Model Using Total Variance, Bilateral and Wavelet Denoising Filter

Noise in images are most common due to various degradation. Noises in images are random variations in images due to lighting conditions, camera electronics, surface reflectance, lens, atmospheric conditions and motions (Either camera is moving or object is moving). Image Restoration is a process which restores a degraded image into its original image which has been degraded by some degradation model which degraded the image. Images are degraded due to various reasons. The first and foremost reason for image degradation is the fault in the imaging devices during the image acquisition process. The noise is generated in the imaging devices and is propagated to the image. The second source of degradation in image is the noise added during the image transmission. This type of image degradation is most common. The third source of image degradation is due to the motion blur and atmospheric turbulence. This paper analyzes various image noise models and restoration techniques. Particularly in analyses three kind of filters namely total variance filter, bilateral filter and wavelet image denoising. The image restoration is measured using the PSNR and SSI of original and degraded images