A New Method to Classify Breast Cancer Tumors and Their Fractionation

http://dx.doi.org/10.5902/2179460X19428In this paper, suspicious breast tumors were classified by using the neural network and the growth area method has been used for a fractionation of the benign or malignant areas of the normal tissue. Features extracted from input tissues are including statistical features and characteristics of spatial dependence. The advantage of this method is using of phase adaptive threshold based on entropy which leads to more accurate extraction of tumors and also corresponded with the nature of mammogram images. As a result, this method mimics of the human eye operation to detect abnormal masses. Database used in this paper is the MIAS mammogram database including 238 normal, benign and malignant mammograms. The accuracy obtained with 38 features is equal to 86.66% for detecting abnormal masses and 38.05 % for normal masses.In this paper, suspicious breast tumors were classified by using the neural network and the growth area method has been used for a fractionation of the benign or malignant areas of the normal tissue. Features extracted from input tissues are including statistical features and characteristics of spatial dependence. The advantage of this method is using of phase adaptive threshold based on entropy which leads to more accurate extraction of tumors and also corresponded with the nature of mammogram images. As a result, this method mimics of the human eye operation to detect abnormal masses. Database used in this paper is the MIAS mammogram database including 238 normal, benign and malignant mammograms. The accuracy obtained with 38 features is equal to 86.66% for detecting abnormal masses and 38.05 % for normal masses

Segmenting breast cancerous regions in thermal images using fuzzy active contours

Breast cancer is the main cause of death among young women in developing countries. The human body temperature carries critical medical information related to the overall body status. Abnormal rise in total and regional body temperature is a natural symptom in diagnosing many diseases. Thermal imaging (Thermography) utilizes infrared beams which are fast, non-invasive, and non-contact and the output created images by this technique are flexible and useful to monitor the temperature of the human body. In some clinical studies and biopsy tests, it is necessary for the clinician to know the extent of the cancerous area. In such cases, the thermal image is very useful. In the same line, to detect the cancerous tissue core, thermal imaging is beneficial. This paper presents a fully automated approach to detect the thermal edge and core of the cancerous area in thermography images. In order to evaluate the proposed method, 60 patients with an average age of 44/9 were chosen. These cases were suspected of breast tissue disease. These patients referred to Tehran Imam Khomeini Imaging Center. Clinical examinations such as ultrasound, biopsy, questionnaire, and eventually thermography were done precisely on these individuals. Finally, the proposed model is applied for segmenting the proved abnormal area in thermal images. The proposed model is based on a fuzzy active contour designed by fuzzy logic. The presented method can segment cancerous tissue areas from its borders in thermal images of the breast area. In order to evaluate the proposed algorithm, Hausdorff and mean distance between manual and automatic method were used. Estimation of distance was conducted to accurately separate the thermal core and edge. Hausdorff distance between the proposed and the manual method for thermal core and edge was 0.4719 ± 0.4389, 0.3171± 0.1056 mm respectively, and the average distance between the proposed and the manual method for core and thermal edge was 0.0845± 0.0619, 0.0710 ± 0.0381 mm respectively. Furthermore, the sensitivity in recognizing the thermal pattern in breast tissue masses is 85 % and its accuracy is 91.98 %.A thermal imaging system has been proposed that is able to recognize abnormal breast tissue masses. This system utilizes fuzzy active contours to extract the abnormal regions automatically

Diagnosis of Breast Cancer using a Combination of Genetic Algorithm and Artificial Neural Network in Medical Infrared Thermal Imaging

Introduction This study is an effort to diagnose breast cancer by processing the quantitative and qualitative information obtained from medical infrared imaging. The medical infrared imaging is free from any harmful radiation and it is one of the best advantages of the proposed method. By analyzing this information, the best diagnostic parameters among the available parameters are selected and its sensitivity and precision in cancer diagnosis is improved by utilizing genetic algorithm and artificial neural network. Materials and Methods In this research, the necessary information is obtained from thermal imaging of 200 people, and 8 diagnostic parameters are extracted from these images by the research team. Then these 8 parameters are used as input of our proposed combinatorial model which is formed using artificial neural network and genetic algorithm. Results Our results have revealed that comparison of the breast areas; thermal pattern and kurtosis are the most important parameters in breast cancer diagnosis from proposed medical infrared imaging. The proposed combinatorial model with a 50% sensitivity, 75% specificity and, 70% accuracy shows good precision in cancer diagnosis. Conclusion The main goal of this article is to describe the capability of infrared imaging in preliminary diagnosis of breast cancer. This method is beneficial to patients with and without symptoms. The results indicate that the proposed combinatorial model produces optimum and efficacious parameters in comparison to other parameters and can improve the capability and power of globalizing the artificial neural network. This will help physicians in more accurate diagnosis of this type of cancer

Segmentation of the Left Atrial Appendage in the Echocardiographic Images of the Heart Using a Deep Neural Network

Introduction: Cardiovascular diseases are one of the leading causes of mortality in today’s industrial world. Occlusion of left atrial appendage (LAA) using the manufactured devices is a growing trend. The objective of this study was to develop a computer-aided diagnosis system for the identification of LAA in echocardiographic images. Method: The data used in this descriptive analytical study included 3D echocardiographic images of the heart of 32 patients in King’s College Hospital in London. All patients were treated successfully using the LAA closure device. A total of 208 two-dimensional images were first obtained from each 3D echocardiographic image data set. Then, 1914 images in which the LAA region was clearly recognizable were selected for this study. The proposed neural network was compiled based on the YOLOv3 algorithm. Finally, 1369 and 545 images were used for training and testing the algorithm, respectively. Results: The performance of the algorithm in detecting the LAA on a set of 545 images was compared with the regions detected in similar images by an expert in echocardiography through intersection over union (IOU). The algorithm was able to correctly identify the LAA region in all 545 examined images with an average IOU of 99.37%. Conclusion: The proposed image-based algorithm could detect LAA region in echocardiographic images with a high accuracy. This method can be used to develop algorithms for automatic analysis of the LAA region to determine the size of the closure device and to plan an efficient procedure in LAA occlusion methods

Diagnosing Breast Cancer with the Aid of Fuzzy Logic Based on Data Mining of a Genetic Algorithm in Infrared Images

Background: Breast cancer is one of the most prevalent cancers among women today. The importance of breast cancer screening, its role in the timely identification of patients, and the reduction in treatment expenses are considered to be among the highest sanitary priorities of a modern country. Thermal imaging clearly possesses a special role in this stage due to rapid diagnosis and use of harmless rays.Methods: We used a thermal camera for imaging of the patients. Important parameters were derived from the images for their posterior analysis with the aid of a genetic algorithm. The principal components that were entered in a fuzzy neural network for clustering breast cancer were identified.Results: The number of images considered for the test included a database of 200 patients out of whom 15 were diagnosed with breast cancer via mammography. Results of the base method show a sensitivity of 93%. The selection of parameters in the combination module gave rise measured errors, which in training of the fuzzy-neural network were of the order of clustering 1.0923×10-5, which reached 2%.Conclusion: The study indicates that thermal image scanning coupled with the presented method based on artificial intelligence can possess a special status in screening women for breast cancer due to the use of harmless non-radiation rays. There are cases where physicians cannot decisively say that the observed pattern in theimage is benign or malignant. In such cases, the response of the computer model can be a valuable support tool for the physician enabling an accurate diagnosis based on the type of imaging pattern as a response from the computer model

Model of hierarchical self-organizing neural networks for detecting and classifying diabetic retinopathy

Background: One common symptom of diabetes is diabetic retinopathy, if not timely diagnosed and treated, leads to blindness. Retinal image analysis has been currently adopted to diagnose retinopathy. In this study, a model of hierarchical self-organized neural networks has been presented for the detection and classification of retina in diabetic patients. Methods: This study is a retrospective cross-sectional, conducted from December to February 2015 at the AJA University of Medical Sciences, Tehran. The study has been conducted on the MESSIDOR base, which included 1200 images from the posterior pole of the eye. Retinal images are classified into 3 categories: mild, moderate and severe. A system consisting of a new hybrid classification of SOM has been presented for the detection of retina lesions. The proposed system includes rapid preprocessing, extraction of lesions features, and finally provision of a classification model. In the preprocessing, the system is composed of three processes of primary separation of target lesions, separation of the optical disk, and separation of blood vessels from the retina. The second step is a collection of features based on various descriptions, such as morphology, color, light intensity, and moments. The classification includes a model of hierarchical self-organized networks named HSOM which is proposed to accelerate and increase the accuracy of lesions classification considering the high volume of information in the feature extraction. Results: The sensitivity, specificity and accuracy of the proposed model for the classification of diabetic retinopathy lesions is 98.9%, 96.77%, 97.87%, respectively. Conclusion: These days, the cases of diabetes with hypertension are constantly increasing, and one of the main adverse effects of this disease is related to eyes. In this respect, the diagnosis of retinopathy, which is the same as identification of exudates, microanurysm and bleeding, is of particular importance. The results show that the proposed model is able to detect lesions in diabetic retinopathy images and classify them with an acceptable accuracy. In addition, the results suggest that this method has an acceptable performance compared to other methods

Following a patient with breast cysts using thermal imaging: case report

Background: Breast cancer is a common malignancy in which early breast cancer detection by the help of imaging can improve the treatment outcome. Thermography utilizes infrared beams which are fast, non-invasive, and non-contact and the output created images by this technique are flexible and useful to monitor the temperature of the human body. Case presentation: Our patient is a 25-year-old woman who was referred to Tehran's Imam Khomeini Hospital, Tehran University of Medical Sciences, in October 2014 and June 2017 to perform clinical examinations of breast cancer at the Invasive and New Radiology Research Center of Tehran. The results of the sonography for the left breast and bilateral axillary regions and sonography guided biopsy from the left axillary region indicated that:  it was consistent with the tangential prominence at 11-12 O’ clock in the left breast tissue and echo gene was found without any suspected findings. Then, using the non-contact infrared imaging camera VisIR 640 (Thermoteknix Systems Ltd, Cambridge, UK), the feasibility of thermography method in the patient's follow-up was investigated. Conclusion: Thermography can be used to detect abnormal areas in the breast tissue that may have cystic origin. The results indicated that the accuracy of the identification and matching of patient cysts in mammography and ultrasonography with the results of thermography in both periods of October 2014 and June 2017. Considering the results, it is noteworthy that the diagnostic clock of the breast cysts in the patient is consistent with the results of the clinical trials with the thermography. Moreover, in a 2 years intervals, the status of thermal morphology status of the cystic region did not considerably change which showed a relatively stable status

Breast cancer diagnosis by thermal imaging in the fields of medical and artificial intelligence sciences: review article

Breast cancer is the most common cancer in women and one of the leading of death among them. The high and increasing incidence of the disease and its difficult treatment specifically in advanced stages, imposes hard situations for different countries’ health systems. Body temperature is a natural criteria for the diagnosis of diseases. In recent decades extensive research has been conducted to increase the use of thermal cameras and obtain a close relationship between heat and temperature of the skin's physiology. Thermal imaging (thermography) applies infrared method which is fast, non-invasive, non-contact and flexibile to monitor the temperature of the human body. This paper investigates highly diversified studies implemented before and after the year 2000. And it emphasizes mostly on the newely published articles including: performance and evaluation of thermal imaging, the various aspects of imaging as well as The available technology in this field and its disadvantages in the diagnosis of breast cancer. Thermal imaging has been adopted by researchers in the fields of medicine and biomedical engineering for the diagnosis of breast cancer. With the advent of modern infrared cameras, data acquisition and processing techniques, it is now possible to have real time high resolution thermographic images, which is likely to surge further research in this field.  Thermography does not provide information on the structures of the breast morphology, but it provides performance information of temperature and breast tissue vessels. It is assumed that the functional changes occured before the start of the structural changes which is the result of disease or cancer. These days, thermal imaging method has not been established as an applicative method for screening or diagnosing purposes in academic centers. But there are different centers that adopt this method for the diognosis and examining purposes. Thermal imaging is an effective method which is highly facilitative for breast cancer screening (due to the low cost and without harms), also, its impact will increase by combining other methods such as a mammogram and sonography. However, it has not been widely recognizesd as an accepted method for determineing the types of tumors (benign and malignant) and diseases of breast tissue