Thermal imaging is simply the technique of using the heat given off by an object to produce an image of it or locate it. New thermal imaging frameworks for detection, segmentation and unique feature extraction and similarity measurements for human physiological biometrics recognition have been introduced in literature. The research investigates specialized algorithms that would use the individual's heat signature for human detection, crowd counting and applications that take benefits of this new technology. The highly accurate results obtained by the algorithms presented clearly demonstrate the ability of the thermal infrared systems to extend in application to other thermal imaging based systems.
This work presents a new approach for crowd counting and classification based upon human thermal and motion features. The technique is efficient for automatic crowd density estimation and type of motion determination. Crowd density is measured without any need for camera calibration or assumption of prior knowledge about the input videos. It does not need any human intervention so it can be used successfully in a fully automated crowd control systems. Two new features are introduced for crowd counting purpose: the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature. The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity. Each ratio should exceed a certain predetermined threshold for human beings. These features have been investigated and proved to give accurate crowd counting performance in real time. Moreover, the two features are combined and used together for crowd classification into one of the three main types, which are: fully mobile, fully static, or mix of both types. Last but not least, the proposed system offers several advantages such as being a privacy preserving crowd counting system, reliable for homogeneous and inhomogeneous crowds, does not depend on a certain direction in motion detection, has no restriction on crowd size. The experimental results demonstrate the effectiveness of the approach.
Alzheimer's disease (AD), also referred to simply as Alzheimer's, is a chronic neurodegenerative disease that usually starts slowly and worsens over time. It is the cause of 60% to 70% of cases of dementia. In 2015, there were approximately 29.8 million people worldwide with AD. It most often begins in people over 65 years of age as it affects about 6% of people 65 years and older, although 4% to 5% of cases are early-onset Alzheimer's which begin before this. In 2015, researchers have figured out that dementia resulted in about 1.9 million deaths. Continuous efforts are made to cure the disease or to delay its progression. Brain imaging is one of the hottest areas in AD research. Techniques like CT, MRI, SPECT, and PET assist in disease detection and help in excluding other probable causes of dementia. Imaging helps to perceive the intended cause of the disease as well as track the disease through its course. This paper applies Image processing and machine learning techniques combined to MRI brain images to help in detection of AD and classify the case either to MDI or Dementia.
Breast cancer has been reported to be the first deadly disease that affects women worldwide. This type of cancer has been reported to be the second leading cause of death in women worldwide. Medical reports have also reported that every woman is exposed to having breast cancer with an average probability of about 12%. It has also been reported to be the most common cancer that affects women. Fatality could be due to the cancer detection delay; in other words, early detection of the tumor can increase the survival rate of patients. Routine techniques of imaging modalities for cancer screening such as Mammography, Computated Tomography (CT) scan, Magnetic Resonance Imaging (MRI) and ultrasound are impractical tools for many reasons such as the irreproducible nature, the high error rate in cases of thick breasts, the pain and the annoyance they cause. Consequently, there is a need for more convincing strategies with high accuracy rates in breast cancer detection. Therefore, among the large variety of medical breast scanning techniques, thermography has attracted attention in applications related to detection and diagnosis. It is capable of providing helpful and useful information about the physiological variations and accordingly, it can detect tumors even in early stages. In addition, it is a very safe scanning tool, so as many needed tests can be held in proper time and manner. Thermography relies on the fact that human body temperature generally is a natural norm for the diagnosis of diseases. Thermography in medical applications applies infrared body examination tool which is fast, noninvasive, noncontact, pain free, radiation free and flexible to monitor the temperature of the human body. The fundamental principle of thermography relies on physiology such as the distribution of temperature on the skin surface. Infrared thermography scanning for breasts is an imaging technique which essentially searches for temperature change in human body. Temperature variance could be considered as a good indicator of tumor occurrence in the scanned area. Tumor mainly causes a noteworthy increase in blood vessel circulation and metabolic activity, so it causes higher radiations emitted from the human body around the regions of tumor. The paper surveys the literature work conducted in the field of breast cancer detection from thermogram scans. The survey is followed by a discussion of the strengths and weaknesses of thermography-based tumor detection. A new research idea and some considerations are then suggested based on that discussion to achieve better results in this critical area.
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