Based on the obtained results, dynamics of preventive examinations for particular groups of women that is different from the standard two-year examinations, can be successfully defined. It can be concluded that the use of a computer system for tumor diagnosis in mammogram based on various methods of image processing can help doctors in decision-making, while the use of thermal imaging in the pre-screening phase would significantly reduce the list of women for screening mammograms.

In this paper, we present a system based on feature extraction techniques for detecting abnormal patterns in digital mammograms and thermograms. A comparative study of texture-analysis methods is performed for three image groups: mammograms from the Mammographic Image Analysis Society mammographic database; digital mammograms from the local database; and thermography images of the breast. Also, we present a procedure for the automatic separation of the breast region from the mammograms. Computed features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 texture features are extracted from the region of interest. The ability of feature set in differentiating abnormal from normal tissue is investigated using a support vector machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross-validation method and receiver operating characteristic analysis was performed.

BACKGROUND:Vertigo is a common symptom and reason for admission to the emergency department (ED).AIM:This research aimed to determine the incidence of clinically significant findings on computed tomography (CT) in patients with vertigo without focal neurological abnormalities in the ED.MATERIAL AND METHODS:The results of the native CT scans in the ED were retrospectively analysed. Exclusion criteria included: focal neurological abnormalities, underlying malignancy, brain metastasis, previous brain operation, headache, fever, nausea, vomiting, head trauma, coagulopathy. As a clinically significant finding, we took into an account tumour, haemorrhage and acute ischemic lesion. 72 patients fulfilled the set criteria, present vertigo, without focal neurological abnormalities. Out of 72 patients with a median age of 62 (23-87) years old, 54% of the patients were female, and 46% were male.RESULTS:Normal CT findings were found in 44 patients (61.1%), 28 patients (38.9%) had pathological findings, out of that number 23 (31.9%) findings were clinically irrelevant and 5 (6.9%) were clinically significant. Out of the 5 clinically significant findings, tumour process was found in 3 (4.2%) patients, haemorrhage was found in 1 (1.4%) patient, and the ischemic lesion was found in 1 (1.4%) patient. Additional evaluation of five clinically significant findings showed a change of initial diagnosis in one case, but the significance of the finding remained the same.CONCLUSION:Our study demonstrates a low diagnostic yield of head CT examination with 6.9% of clinically significant findings in patients with vertigo without focal neurological abnormalities.

According to the results of this study, it is clear that epilepsy professionals should invest more time in discussing with patients the topics which interest them the most, as well as refer them to other professionals that can help them with non-medical epilepsy-related issues, and advise them on reliable Internet sources.

Obtained experimental results indicate that the proposed three-class SVM classifier is more suitable for practical use than the other two methods.

By using the proposed regression function and parameter optimization we were able to improve segmentation results comparing to the literature. In addition, we showed that CAD system has high potential for being equipped with reliability estimate module.

We report a patient in whom mechanical compression of the internal carotid artery by a giant external carotid artery pseudoaneurysm caused a stroke. This was a case of vascular Eagle syndrome due to the impingement of an elongated styloid process on the external carotid artery with subsequent dissection and formation of a pseudoaneurysm. Carotid ultrasonographic examination allowed distinguishing the pseudoaneurysm from other vascular and solid masses of the neck.

Microcalcification clusters appear as groups of small, bright particles with arbitrary shapes on mammographic images. They are the earliest sign of breast carcinomas and their detection is the key for improving breast cancer prognosis. But due to the low contrast of microcalcifications and same properties as noise, it is difficult to detect microcalcification. This work is devoted to developing a system for the detection of microcalcification in digital mammograms. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), we first selected the region of interest (ROI) in order to demarcate the breast region on a mammogram. Segmenting region of interest represents one of the most important stages of mammogram processing procedure. The proposed segmentation method is based on a filtering using the Sobel filter. This process will identify the significant pixels, that belong to edges of microcalcifications. Microcalcifications were detected by increasing the contrast of the images obtained by applying Sobel operator. In order to confirm the effectiveness of this microcalcification segmentation method, the Support Vector Machine (SVM) and k-Nearest Neighborhood (k-NN) algorithm are employed for the classification task using cross-validation technique.