Delia Mitrea

Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images

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.

Ultrasonography Contribution to Hepatic Steatosis Quantification. Possibilities of Improving this Method through Computerized Analysis of Ultrasonic Image

Hepatic steatosis is a frequently encountered disease in medical practice and it has a great importance due to the potential evolution towards cirrhosis. The clinical and laboratory evaluation has a quite reduced positive predictive value; on the other hand, a series of imagistic techniques may be used for the diagnosis and quantification of these diseases: magnetic resonance imaging, computerized tomography, and not least, ultrasonography. In this paper, our purpose is to evaluate the contribution of the ultrasonography examination to the quantification of the hepatic steatosis as well as the possibility of improving this method

Texture based characterization and automatic diagnosis of the abdominal tumors from ultrasound images using third order GLCM features

The frequency of the cancer cases is continuously increasing. The golden standard for the malignant tumor diagnosis is the biopsy, but this is often a dangerous method. We aim to develop computerized, non-invasive techniques for the automatic diagnosis of the abdominal malignant tumors, based on ultrasound images. We take into consideration the hepatocellular carcinoma (HCC) and the colo-rectal tumors for this purpose. The texture is an important property of the internal organ tissues, providing subtle information about the pathology. We previously defined the textural model of HCC, consisting in the exhaustive set of the relevant textural features, appropriate for HCC characterization and in their specific values. In this work, we analyze the role that the third order Gray Level Cooccurrence Matrix (GLCM) has on the characterization and automatic diagnosis of the abdominal malignant tumors. We also determine the best spatial relation between the pixels that leads to the highest performances.

Modelling cutaneous senescence process

During the last years, skin aging has become an area of increasing research interest. High frequency ultrasound allows the “in vivo” appreciation of certain histological parameters and offers new characteristic markers, which may quantify the severity of the cutaneous senescence process. This paper focuses on measuring the changes in skin thickness and dermis echogenicity , as part of the complex ageing process, on different intervals of age. In particular by using a multiscale approach we will compute some parameters which are connected with complexity (fractal structure) of skin ageing.

Advanced classification methods for improving the automatic diagnosis of the hepatocellular carcinoma, based on ultrasound images

The hepatocellular carcinoma (HCC) is the most frequent malignant liver tumor. Nowadays, the only reliable method for the detection of HCC is the needle biopsy, but it is invasive, dangerous for the patient. We aim to elaborate a non-invasive method for the automatic diagnosis of HCC, based only on computerized techniques for ultrasound image analysis. Thus, we elaborated the imagistic textural model of HCC, consisting in the exhaustive set of the textural parameters, relevant for HCC characterization, and in their specific values for the HCC class. In this work, we study the effect of the classifier combination procedures on the improvement of the recognition performance, from speed and accuracy points of view. Various combination schemes are considered, and their influence on the accuracy parameters and on the learning curves is discussed. The role of the dimensionality reduction methods in the improvement of the automatic diagnosis process is discussed as well.

Improving the Textural Model of the Hepatocellular Carcinoma Using Dimensionality Reduction Methods

The diagnosis of the malignant tumors is one of the major issues in nowadays research. We aim to elaborate a computerized, non-invasive method, for detecting the Hepatocellular Carcinoma (HCC), based on information from ultrasound images. For performing automatic detection of HCC, we elaborated the imagistic textural model of this malignant tumor, consisting in the relevant textural features and in their specific values for HCC. In this paper, we enhance the imagistic textural model of HCC, by using dimensionality reduction methods, the final purpose being that of obtaining an improvement of the classification process. Principal Component Analysis is a well known dimensionality reduction method, which maps the data into a new space, lower in dimension by finding the principal directions of variation. We experiment this method, studying its influence on the automatic diagnosis accuracy and we also try to combine it with Correlation based Feature Selection, for adding class label sensitivity.

Iterative Methods for Obtaining Energy-Minimizing Parametric Snakes with Applications to Medical Imaging

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.

Experimenting various classification techniques for improving the automatic diagnosis of the malignant liver tumors, based on ultrasound images

The hepatocellular carcinoma (HCC) is the most frequent malignant liver tumor. Nowadays, the only reliable method for the detection of HCC is the needle biopsy, but it is invasive, dangerous for the patient. We aim to develop a non-invasive method for the automatic diagnosis of HCC, based only on computerized techniques for ultrasound image analysis. Thus, we elaborated the imagistic textural model of HCC, consisting in the exhaustive set of the textural parameters, relevant for HCC characterization, and in their specific values for the HCC class. In this work, we study the effect of the classifier combination procedures on the improvement of the recognition performance, from speed and accuracy points of view. Various combination schemes are considered, and their influence on the accuracy parameters and on the learning curves is discussed. The hepatocellular carcinoma is also divided into subclasses, and the multiclass classification techniques are experimented for accuracy improvement.

Computer-assisted identification of the gingival sulcus and periodontal epithelial junction on high-frequency ultrasound images.

UNLABELLED The primary aim of this study was to demonstrate that periodontal ultrasonography is a reliable method with which to identify and evaluate the attachment level of the gingival junctional epithelium. A secondary aim was to devise an automated computer-assisted method that allows the examiner to more easily identify the gingival sulcus contour on ultrasound images. MATERIAL AND METHODS This in vitro study was carried out on 36 sites on the lingual surface of eight pig mandibles. For each site, periodontal ultrasonography was performed by the same examiner, using DermaScan C Cortex Technology (Denmark) with a 20-MHz transducer. Subsequently, the mandibles were sectioned with a microtome and examined by direct microscopy. To facilitate identification of the gingival sulcus on ultrasound images, a computational method was adopted. RESULTS Computer processing of the ultrasound images slightly modified the contour of the gingival sulcus. The absolute mean differences in the linear measurements of the Dermascan-automated computer-generated values and the corresponding values of microscopy, which is the gold standard, varied between 0.06 and 1.75 mm. Statistical analysis showed that with respect to the gingival sulcus height, the correlation between the computer-processed ultrasound images and the direct microscopy images was stronger than the correlation between the non-processed ultrasound images and those from direct microscopy. CONCLUSIONS Ultrasonographic examination of the periodontal tissues allows the examiner to localize the gingival epithelial attachment level and provides substantial data regarding the soft gingival tissues.

The role of the superior order GLCM and of the generalized cooccurrence matrices in the characterization and automatic diagnosis of the hepatocellular carcinoma, based on ultrasound images

The hepatocellular carcinoma (HCC) is the most frequent malignant liver tumor. The golden standard for HCC diagnosis is the needle biopsy, but this is invasive, dangerous. We aim to develop computerized, non-invasive techniques for HCC automatic diagnosis, based on the information obtained from ultrasound images. The texture is an important property of the internal body tissues, able to provide subtle information about the pathology. We previously defined the textural model of HCC, consisting in the set of the relevant textural features, appropriate for HCC characterization and in the specific values of these features. In this work, we analyze the role that the superior order Gray Level Cooccurrence Matrices (GLCM) and the Edge Orientation Cooccurrence Matrices (EOCM) have concerning the improvement of HCC characterization and automatic diagnosis. We also determine the best spatial relation between the pixels that leads to the highest performances, for the both superior order GLCM and EOCM.