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The noninvasive diagnosis of the malignant tumors is an important issue in research nowadays. Our purpose is to elaborate computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images. In this paper, we considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors. We compared these structures with the benign tumors and with other visually similar diseases. Besides the textural features that proved in our previous research to be useful in the characterization and recognition of the malignant tumors, we improved our method by using the grey level cooccurrence matrix and the edge orientation cooccurrence matrix of superior order. As resulted from our experiments, the new textural features increased the malignant tumor classification performance, also revealing visual and physical properties of these structures that emphasized the complex, chaotic structure of the corresponding tissue. [ABSTRACT FROM AUTHOR]

Hepatocellular Carcinoma (HCC) is the most common malignant liver tumor, being present in 70% of liver cancer cases. It usually evolves on the top of the cirrhotic parenchyma. The most reliable method for HCC diagnosis is the needle biopsy, which is an invasive, dangerous method. In our research, specific techniques for non-invasive, computerized HCC diagnosis are developed, by exploiting the information from ultrasound images. In this work, the possibility of performing the automatic diagnosis of HCC within B-mode ultrasound and Contrast-Enhanced Ultrasound (CEUS) images, using advanced machine learning methods based on Convolutional Neural Networks (CNN), was assessed. The recognition performance was evaluated separately on B-mode ultrasound images and on CEUS images, respectively, as well as on combined B-mode ultrasound and CEUS images. For this purpose, we considered the possibility of combining the input images directly, performing feature level fusion, then providing the resulted data at the entrances of representative CNN classifiers. In addition, several multimodal combined classifiers were experimented, resulted by the fusion, at classifier, respectively, at the decision levels of two different branches based on the same CNN architecture, as well as on different CNN architectures. Various combination methods, and also the dimensionality reduction method of Kernel Principal Component Analysis (KPCA), were involved in this process. These results were compared with those obtained on the same dataset, when employing advanced texture analysis techniques in conjunction with conventional classification methods and also with equivalent state-of-the-art approaches. An accuracy above 97% was achieved when our new methodology was applied. [ABSTRACT FROM AUTHOR]

Introduction: The hepatocellular carcinoma (HCC) is the most frequent malignant liver tumor, appearing in 75% of liver cancer cases. The golden standard for HCC diagnosis is the needle biopsy, but it is invasive, dangerous. We develop non-invasive, computerized methods for the automatic and computer assisted diagnosis of HCC, within ultrasound images. The advanced texture analysis methods, the classification techniques including the deep learning approach, respectively other specific pattern classification and artificial intelligence methods play a major role in our research. Thus, we perform the characterization and supervised recognition of HCC, as well as the unsupervised discovery of the HCC evolution phases. Materials and methods: In order to perform image analysis, both classical and original texture analysis methods were taken into account, including multiresolution features, based on the Wavelet and Gabor transforms [1]. Advanced, original texture analysis methods were developed in the form of the superior order generalized co-occurrence matrices, based on gray levels, edge orientations, respectively complex textural microstructures. These features were provided at the input of some traditional classifiers [1], [2]. Deep learning techniques such as Stacked Denoising Autoencoders (SAE) and Convolutional Neural Networks (CNN) were also experimented [3]. Thus, the imagistic textural model of HCC, respectively of the HCC evolution phases was defined in both supervised [1] and unsupervised manner [4]. The experiments were performed on 300 cases of HCC, and 100 cases of hemangioma, acquired with an older ultrasound machine, respectively on 13 HCC cases acquired with a new generation ultrasound machine. In the case of the supervised classification, we considered the following pairs of classes: HCC/cirrhotic parenchyma; HCC/hemangioma. Results: Our methods led to a satisfying classification accuracy, around 85%. Conclusion: Due to the textural model of HCC, both automatic and computer assisted diagnosis can be performed. We aim to increase the classification performance, in our future research. [ABSTRACT FROM AUTHOR]

Based on some mathematical and statistical approaches, our study leads to some conclusions concerning the procedures related to the orodental prosthetics. Occlusal equilibration in orodental prosthetics is a major issue because besides motivating patients for a regular daily oral hygiene, it could significantly increase the longevity of FPR. More dental hygiene information should be given after prosthetic treatment and patients should be motivated to attend recalls on a regular basis for professional teeth-cleaning. Interdental cleaning aids should be explained and the patients have to be motivated to use them at least once a day and the using technique should be individualized. Regarding the application of the deformable models theory, implemented in the context of an expert type software environment, it is known that the fact that modelling by advanced methods and techniques based on the deformable surfaces theory increases the efficiency of the dentofacial prosthetics procedures is a domain of great interest in the actual medical research. [ABSTRACT FROM AUTHOR]

After a brief survey on the parametric deformable models, we develop an iterative method based on the finite difference schemes in order to obtain energy-minimizing snakes. We estimate the approximation error, the residue, and the truncature error related to the corresponding algorithm, then we discuss its convergence, consistency, and stability. Some aspects regarding the prosthetic sugical methods that implement the above numerical methods are also pointed out. [ABSTRACT FROM AUTHOR]