Thermography based breast cancer detection using texture features and minimum variance quantization

In this paper, we present a system based on feature extraction techniques and image segmentation techniques for detecting and diagnosing abnormal patterns in breast thermograms. The proposed system consists of three major steps: feature extraction, classification into normal and abnormal pattern and segmentation of abnormal pattern. Computed features based on Gray Level Co-occurrence Matrices (GLCM) are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 GLCM features are extracted from thermograms. 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. The verification results show that the proposed algorithm gives the best classification results using K-Nearest Neighbor classifier and a accuracy of 92.5 %. Image segmentation techniques can play an important role to segment and extract suspected hot regions of interests in the breast infrared images. Three image segmentation techniques: minimum variance quantization, dilation of image and erosion of image are discussed. The hottest regions of thermal breast images are extracted and compared to the original images. According to the results, the proposed method has potential to extract almost exact shape of tumors

Flexible GPS/GPRS based System for Parameters Monitoring in the District Heating System

Energy consumption for heating purposes accounts for a significant part of the budgets of individual and collective users. This increases the importance of issues related to the monitoring of heating energy flows, analysis of flow parameters, verification of fees and, in the first place, minimization of energy consumption. The goal of this paper is to develop, by employing Global Positioning System receivers, measurement techniques that are suited to the continuous monitoring of the heating substation parameters. This paper presents the design and implementation of GPS/GPRS (Global Positioning System/General Packet Radio Service) system for low power data acquisition using MSP430 Texas Instruments microcontroller for monitoring of the heating substation parameters. The system is implemented in heating stations for a temperature and pressure monitoring. It contains GPS/GPRS gateway and 8 analog sensor inputs. Acquisition module and the server base station are suitable for industrial applications, home applications and for other appliances. The proposed measurement procedures, which are different from commercially available measurement units, are based on general-purpose acquisition hardware and processing software, thus guaranteeing the possibility of being easily reconfigured and reprogrammed according to the specific requirements of different possible fields of application and to their future developments

Patient comfort level prediction during transport using artificial neural network

© TÜBİTAK Since patient comfort during transport is a matter of paramount importance, this paper aims to determine the possibilities of applying neural networks for its prediction and monitoring. Specific objectives of the research include monitoring and predicting patient transport comfort, with subjective assessment of comfort by medical personnel. An original Android application that collects signals from an accelerometer and a GPS sensor was used with the aim of achieving the research goals. The collected signals were processed and a total of twelve parameters were calculated. A multilayer perceptron was created in the proposed research. The evaluation results indicate acceptable accuracy and give the possibility to apply the same model to the next patient transport. The root mean square error was 0.0215 and the overall confusion matrix prediction accuracy was 90.07%. Moreover, the results were validated in real usage. The limitations and future work are highlighted

Manufacturing of Biodegradable Scaffolds to Engineer Artificial Blood Vessel

Blood vessels diseases such as cardiac infarction with coronary artery occlusion, peripheral arterial disorders, or stroke of carotid or cerebral arteries, are the leading causes of death in the world. One of medical procedures for clinical treatment of vascular diseases is the blood vessels grafting. As the autologous blood vessels, which are the “golden standard” for coronary grafting, are not always suitable for blood vessels grafting, there is a need to develop artificial blood vessels as a vascular prostheses, either from natural and synthetic materials, permanent synthetic or biodegradable scaffolds which would be suitable for vascular grafts. Considering this to be our study goal we made bilayered biodegradable polycaprolactone scaffolds with different properties and evaluated their morphological and biomechanical characteristics

NONINVASIVE DETERMINATION OF KNEE CARTILAGE DEFORMATION DURING JUMPING

The purpose of this investigation was to use a combination of image processing, force measurements and finite element modeling to calculate deformation of the knee cartilage during jumping. Professional athletes performed jumps analyzed using a force plate and high-speed video camera system. Image processing was performed on each frame of video using a color recognition algorithm. A simplified mass-spring-damper model was utilized for determination of global force and moment on the knee. Custom software for fitting the coupling characteristics was created. Simulated results were used as input data for the finite element calculation of cartilage deformation in the athlete's knee. Computer simulation data was compared with the average experimental ground reaction forces. The results show the three-dimensional mechanical deformation distribution inside the cartilage volume. A combination of the image recognition technology, force plate measurements and the finite element cartilage deformation in the knee may be used in the future as an effective noninvasive tool for prediction of injury during jumpin

Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators’ Psychological Status and Pain Syndromes

Non-ergonomic execution of repetitive physical tasks represents a major cause of work-related musculoskeletal disorders (WMSD). This study was focused on the pushing and pulling (P&P) of an industrial handcart (which is a generic physical task present across many industries), with the aim to investigate the dependence of P&P execution on the operators’ psychological status and the presence of pain syndromes of the upper limbs and spine. The developed acquisition system integrated two three-axis force sensors (placed on the left and right arm) and six electromyography (EMG) electrodes (placed on the chest, back, and hand flexor muscles). The conducted experiment involved two groups of participants (with and without increased psychological scores and pain syndromes). Ten force parameters (for both left and right side), one EMG parameter (for three different muscles, both left and right side), and two time-domain parameters were extracted from the acquired signals. Data analysis showed intergroup differences in the examined parameters, especially in force integral values and EMG mean absolute values. To the best of our knowledge, this is the first study that evaluated the composite effects of pain syndromes, spine mobility, and psychological status of the participants on the execution of P&P tasks—concluding that they have a significant impact on the P&P task execution and potentially on the risk of WMSD. The future work will be directed towards the development of a personalized risk assessment system by considering more muscle groups, supplementary data derived from operators’ poses (extracted with computer vision algorithms), and cognitive parameters (extracted with EEG sensors)

Exploring Perforated Jejunal GIST: A Rare Case Report and Review of Molecular and Clinical Literature

This case report details a rare instance of a perforated jejunal gastrointestinal stromal tumor (GIST) in a 76-year-old female patient. The patient presented with acute abdominal pain and distension without any changes in bowel habits or episodes of nausea and vomiting. Initial diagnostics, including abdominal plain radiography and ultrasonography, were inconclusive; however, a computed tomography (CT) scan revealed pneumoperitoneum and an irregular fluid collection suggestive of small intestine perforations. Surgical intervention uncovered a 35 mm jejunal GIST with a 10 mm perforation. Histopathological examination confirmed a mixed cell type GIST with high malignancy potential, further substantiated by immunohistochemistry markers CD117, DOG1, and vimentin. Molecular analysis illuminated the role of key oncogenes, primarily KIT and PDGFRA mutations, emphasizing the importance of molecular diagnostics in GIST management. Despite the severity of the presentation, the patient’s postoperative recovery was favorable, highlighting the effectiveness of prompt surgical and multidisciplinary approaches in managing complex GIST cases

Comparative analysis of breast cancer detection in mammograms and thermograms

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

Electromagnetic field investigation on different cancer cell lines

Background: There is a strong interest in the investigation of extremely low frequency Electromagnetic Fields (EMF) in the clinic. While evidence about anticancer effects exists, the mechanism explaining this effect is still unknown. Methods: We investigated in vitro, and with computer simulation, the influence of a 50 Hz EMF on three cancer cell lines: breast cancer MDA-MB-231, and colon cancer SW-480 and HCT-116. After 24 h preincubation, cells were exposed to 50 Hz extremely low frequency (ELF) radiofrequency EMF using in vitro exposure systems for 24 and 72 h. A computer reaction-diffusion model with the net rate of cell proliferation and effect of EMF in time was developed. The fitting procedure for estimation of the computer model parameters was implemented. Results: Experimental results clearly showed disintegration of cells treated with a 50 Hz EMF, compared to untreated control cells. A large percentage of treated cells resulted in increased early apoptosis after 24 h and 72 h, compared to the controls. Computer model have shown good comparison with experimental data. Conclusion: Using EMF at specific frequencies may represent a new approach in controlling the growth of cancer cells, while computer modelling could be used to predict such effects and make optimisation for complex experimental design. Further studies are required before testing this approach in humans