Evaluation of Statistical Features for Medical Image Retrieval

In this paper we present a complete system allowing the classification of medical images in order to detect possible diseases present in them. The proposed method is developed in two distinct stages: calculation of descriptors and their classification. In the first stage we compute a vector of thirty-three statistical features: seven are related to statistics of the first level order, fifteen to that of second level where thirteen are calculated by means of co-occurrence matrices and two with absolute gradient; the last thirteen finally are calculated using run-length matrices. In the second phase, using the descriptors already calculated, there is the actual image classification. Naive Bayes, RBF, Support VectorMa- chine, K-Nearest Neighbor, Random Forest and Random Tree classifiers are used. The results obtained from the proposed system show that the analysis carried out both on textured and on medical images lead to have a high accuracy

On the Detection of Visual Features from Digital Curves using a Metaheuristic Approach

In computational shape analysis a crucial step consists in extracting meaningful features from digital curves. Dominant points are those points with curvature extreme on the curve that can suitably describe the curve both for visual perception and for recognition. Many approaches have been developed for detecting dominant points. In this paper we present a novel method that combines the dominant point detection and the ant colony optimization search. The method is inspired by the ant colony search (ACS) suggested by Yin in [1] but it results in a much more efficient and effective approximation algorithm. The excellent results have been compared both to works using an optimal search approach and to works based on exact approximation strateg

On the efficacy of handcrafted and deep features for seed image classification

Computer vision techniques have become important in agriculture and plant sciences due to their wide variety of applications. In particular, the analysis of seeds can provide meaningful information on their evolution, the history of agriculture, the domestication of plants, and knowledge of diets in ancient times. This work aims to propose an exhaustive comparison of several different types of features in the context of multiclass seed classification, leveraging two public plant seeds data sets to classify their families or species. In detail, we studied possible optimisations of five traditional machine learning classifiers trained with seven different categories of handcrafted features. We also fine-tuned several well-known convolutional neural networks (CNNs) and the recently proposed SeedNet to determine whether and to what extent using their deep features may be advantageous over handcrafted features. The experimental results demonstrated that CNN features are appropriate to the task and representative of the multiclass scenario. In particular, SeedNet achieved a mean F-measure of 96%, at least. Nevertheless, several cases showed satisfactory performance from the handcrafted features to be considered a valid alternative. In detail, we found that the Ensemble strategy combined with all the handcrafted features can achieve 90.93% of mean F-measure, at least, with a considerably lower amount of times. We consider the obtained results an excellent preliminary step towards realising an automatic seeds recognition and classification framework

From electronic structure to catalytic activity: A single descriptor for adsorption and reactivity on transition-metal carbides

Adsorption and catalytic properties of the polar (111) surface of transition-metal carbides (TMC's) are investigated by density-functional theory. Atomic and molecular adsorption are rationalized with the concerted-coupling model, in which two types of TMC surface resonances (SR's) play key roles. The transition-metal derived SR is found to be a single measurable descriptor for the adsorption processes, implying that the Br{\o}nsted-Evans-Polanyi relation and scaling relations apply. This gives a picture with implications for ligand and vacancy effects and which has a potential for a broad screening procedure for heterogeneous catalysts.Comment: 5 pages, 3 figure

On The Potential of Image Moments for Medical Diagnosis

Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques

A Shallow Learning Investigation for COVID-19 Classification

COVID-19, an infectious coronavirus disease, triggered a pandemic that resulted in countless deaths. Since its inception, clinical institutions have used computed tomography as a supplemental screening method to reverse transcription-polymerase chain reaction. Deep learning approaches have shown promising results in addressing the problem; however, less computationally expensive techniques, such as those based on handcrafted descriptors and shallow classifiers, may be equally capable of detecting COVID-19 based on medical images of patients. This work proposes an initial investigation of several handcrafted descriptors well known in the computer vision literature already been exploited for similar tasks. The goal is to discriminate tomographic images belonging to three classes, COVID-19, pneumonia, and normal conditions, and present in a large public dataset. The results show that kNN and ensembles trained with texture descriptors achieve outstanding accuracy in this task, reaching accuracy and F-measure of 93.05% and 89.63%, respectively. Although it did not exceed state of the art, it achieved satisfactory performance with only 36 features, enabling the potential to achieve remarkable improvements from a computational complexity perspective

Ab initio structure modeling of complex thin-film oxides: thermodynamical stability of TiC/thin-film alumina

We present an efficient and general method to identify promising candidate configurations for thin-film oxides and to determine structural characteristics of (metastable) thin-film structures using ab initio calculations. At the heart of this method is the complexity of the oxide bulk structure, from which a large number of thin films with structural building blocks, that is motifs, from metastable bulk oxide systems can be extracted. These span a coarse but well-defined network of initial configurations for which density functional theory (DFT) calculations predict and implement dramatic atomic relaxations in the corresponding, resulting thin-film candidates. The network of thin-film candidates (for various film thicknesses and stoichiometries) can be ordered according to their variation in ab initio total energy or in ab initio equilibrium Gibbs free energy. Analysis of the relaxed atomic structures for the most favored structures gives insight into the nature of stable and metastable thin-film oxides. We investigate ultrathin alumina nucleated on TiC as a model system to illustrate this method.Comment: Submitted to PRB; 16 pages, 11 figure

Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence

Ripening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes that occur during this period. Therefore, monitoring ripening time is a fundamental task to market a quality product in a timely manner. However, it is difficult to accurately determine the degree of cheese ripeness. Although some scientific methods have also been proposed in the literature, the conventional methods adopted in dairy industries are typically based on visual and weight control. This study proposes a novel approach aimed at automatically monitoring the cheese ripening based on the analysis of cheese images acquired by a photo camera. Both computer vision and machine learning techniques have been used to deal with this task. The study is based on a dataset of 195 images (specifically collected from an Italian dairy industry), which represent Pecorino cheese forms at four degrees of ripeness. All stages but the one labeled as 'day 18', which has 45 images, consist of 50 images. These images have been handled with image processing techniques and then classified according to the degree of ripening, i.e., 18, 22, 24, and 30 days. A 5-fold cross-validation strategy was used to empirically evaluate the performance of the models. During this phase, each training fold was augmented online. This strategy allowed to use 624 images for training, leaving 39 original images per fold for testing. Experimental results have demonstrated the validity of the approach, showing good performance for most of the trained models