Segmentation is the significant key stage in image analysis towards partitioning an image into different regions which have homogeneous features such as color, shape, and texture which is very important in classifying different region shapes in an image. In general, images are considered fuzzy due to the uncertainty present in terms of vagueness. The regions contain imprecise gray levels and uncertain data values which makes the task of defining the membership function difficult due to lack of precise knowledge. The intuitionistic fuzzy rule-based shape classification approach is used to classify the different shapes, such as circular, polygon, sharp, and irregular of the aspired foreign body on pediatric radiography images. Experimental results show the effectiveness of the proposed method in contrast to conventional fuzzy rule base algorithm.

At present, radiography plays a significant role in the field of medical image processing. Medical practitioners and radiologists show keen interest in paediatric health informatics especially in foreign body aspiration (FBA). The radiographic techniques such as X-ray, computer topography, magnetic resonance imaging and ultrasound are the commonly used techniques to analyse the interested regions of interest. This paper focusses is in analysing various radiographic techniques used in paediatric FBA with the focus on the significance of radiographic techniques in medical image processing. A framework is proposed to identify and locate the intrude objects in paediatric X-ray images to reduce the misinterpretation in medical diagnosis.

One of the most important reasons of car accidents are collisions with vehicles that are not visible to a driver. This is why driver warning systems are developed. The main threat for a driver on the highway comes from the surrounding vehicles especially when the driver is not aware of the close presence generally known as driver’s blind spot. An area in and around the vehicle that cannot be directly observed by the driver are known as blind spots. Image processing plays a vital role in this scenario to safe guard drivers from sudden obstacles and blind spots. The pre-processed sequences of images acquired using front and rear camera of a vehicle are considered to train the case base reasoning (CBR) model, which detects the presence of dangerous objects in the blind spot area. To distinguish near and far obstacles, the same CBR model is used with a specified threshold in the vehicle blind spot area. The proposed knowledge based obstacle information system obtains promising results with standard blind spot camera which can improve safety of the driver and has the potential to be applied in vehicular applications for the detection of obstacles and blind spot area.

In general, the diagnosis and treatment planning of pediatric foreign body aspiration is done by medical experts with experience and uncertain clinical data of the patients, which makes the diagnosis a more approximate and time-consuming process. Foreign body diagnostic information requires the evidence such as size, shape, and location classification of the aspired foreign body. This evidence identification process requires the knowledge of human expertise to achieve accuracy in classification. The aim of the proposed work is to improve the performance of automatic anatomic location identification approach (AALIA) and to develop a reasoning-based systematic approach for pediatric foreign body aspiration treatment management. A CBR-based treatment management system is proposed for standardizing the pediatric foreign body aspiration treatment management process. The proposed approach considered a sample set of foreign body-aspired pediatric radiography images for experimental evaluation, and the performance is evaluated with respect to receiver operator characteristics (ROC) measure.

In general, the diagnosis and treatment planning of pediatric foreign body aspiration is done by medical experts with experience and uncertain clinical data of the patients, which makes the diagnosis a more approximate and time-consuming process. Foreign body diagnostic information requires the evidence such as size, shape, and location classification of the aspired foreign body. This evidence identification process requires the knowledge of human expertise to achieve accuracy in classification. The aim of the proposed work is to improve the performance of automatic anatomic location identification approach (AALIA) and to develop a reasoning-based systematic approach for pediatric foreign body aspiration treatment management. A CBR-based treatment management system is proposed for standardizing the pediatric foreign body aspiration treatment management process. The proposed approach considered a sample set of foreign body-aspired pediatric radiography images for experimental evaluation, and the performance is evaluated with respect to receiver operator characteristics (ROC) measure.