Hossein Ghayoumi Zadeh

Bio: Hossein Ghayoumi Zadeh is an academic researcher from Hakim Sabzevari University. The author has contributed to research in topics: Breast cancer & Thermography. The author has an hindex of 8, co-authored 33 publications receiving 195 citations. Previous affiliations of Hossein Ghayoumi Zadeh include Vali Asr University of Rafsanjan & Rafsanjan University of Medical Sciences.

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

Abstract: 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.

Recognition and Classification of the Cancer Cells by Using Image Processing and LabVIEW

Abstract: —In this work, an image analysis approach for automated detection, segmentation, and classification of particular cells, specially the cancer cells from normal cells is introduced. In this technique we can also count the number of defected cells and find their position with image processing. The results of this analysis are useable in designing a neural network for more accurate analysis. The particular cells segregation is the most important property of this work. Using the LABVIEW Software gave practical usages to this image processing system because it can communicate with other equipments used in this system and controls them in order to have an automatic system. This demonstrates the potential effectiveness of such a system on diagnostic tasks that require the classification of individual cells.

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

Abstract: 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 the image 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.

Segmenting breast cancerous regions in thermal images using fuzzy active contours

Abstract: 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.

Emerging technologies for high performance infrared detectors

Abstract: Abstract Infrared photodetectors (IRPDs) have become important devices in various applications such as night vision, military missile tracking, medical imaging, industry defect imaging, environmental sensing, and exoplanet exploration. Mature semiconductor technologies such as mercury cadmium telluride and III–V material-based photodetectors have been dominating the industry. However, in the last few decades, significant funding and research has been focused to improve the performance of IRPDs such as lowering the fabrication cost, simplifying the fabrication processes, increasing the production yield, and increasing the operating temperature by making use of advances in nanofabrication and nanotechnology. We will first review the nanomaterial with suitable electronic and mechanical properties, such as two-dimensional material, graphene, transition metal dichalcogenides, and metal oxides. We compare these with more traditional low-dimensional material such as quantum well, quantum dot, quantum dot in well, semiconductor superlattice, nanowires, nanotube, and colloid quantum dot. We will also review the nanostructures used for enhanced light-matter interaction to boost the IRPD sensitivity. These include nanostructured antireflection coatings, optical antennas, plasmonic, and metamaterials.

Detection of Breast Abnormality from Thermograms Using Curvelet Transform Based Feature Extraction

Abstract: Breast cancer is one of the leading causes for high mortality rates among young women, in the developing countries. Currently mammography is used as the gold standard for screening breast cancer. Due to its inherent disadvantages, alternative techniques are being considered for this purpose. Breast thermography is one such imaging modality, which represents the temperature variations of breast in the form of intensity variations on an image. In the last decade, several studies have been made to evaluate the potential of breast thermograms in detecting abnormal breast conditions, from an image processing view point. This paper proposes a curvelet transform based feature extraction method for automatic detection of abnormality in breast thermograms. Statistical and texture features are extracted from thermograms in the curvelet domain, to feed a support vector machine for automatic classification. The classifier detects abnormal thermograms with an accuracy of 90.91 %. The results of the study indicate that texture features have better potential to detect abnormality in breast thermograms, when extracted in the multiresolution curvelet domain.