Foreign body aspiration is a frequent pediatric emergency, with incidence peaking at two years of age. Foreign body (FB) can be described as the intrude object which is not belong to the human body. The localization of FB needs radiography X-ray, CT, MRI assessment. Foreign bodies such as coin and metallic items are easily seen on radiographs, but it is difficult to identify food and plastic objects on foreign body aspired radiography images. The process of location identification takes more time which leads more complication, even it leads to fatal (Lecron and Benjelloun in Med Imaging SPIE Proc 8314:1-8, 2012 ). Therefore, the proposed work aims to develop an approach for identifying the anatomic location in which the complications of diagnosis process will be reduced. Image processing plays vital role in this scenario, especially in automating the process of determining the anatomic location of the foreign body on pediatric radiographic images. This chapter mainly focuses on identifying the relative advantages of using specific combination of image enhancement, segmentation, feature extraction for optimal feature selection and proposing an approach for automatic anatomic location identification process on pediatric foreign body aspired images. This process includes the radiographic image acquisition of the foreign body aspired pediatric patients, image enhancement, and segmentation methods. The identification of suitable segmentation method for extracting the optimal features is related to a range of research studies published on image segmentation, feature extraction, feature selection methods. The observation of the existing work helps to understand the importance of various segmentation methods and also supports to develop improved segmentation methods such as constraint-based median filtering, constraint-based iterative thresholding, constraint-based Sobel boundary detection, and K-means clustering. The ability of the enhanced segmentation techniques are determined by the performance comparison with the existing segmentation techniques which is done by the quality metrics evaluation. The feature extraction is used for describing the true region of interest based on shape-, edge-, and texture-based descriptors. The most influenced features are identified by applying hybrid feature selection method which is a combination of filter and wrapper methods in predicting the location and shape of the foreign body. A novel, automatic anatomic location identification approach (AALIA) using 8-connected block searching algorithm and corner identification methods are applied to identification and classification of the anatomic location of foreign body. To evaluate performance of the proposed approach, the accuracy measure precision, recall, F-Measure, and receiver operator characteristic (ROC) with respect to sensitivity, specificity, positive predicted rate, and negative predicted rate are considered. The results obtained from the developed approach are comparatively better than the existing works. © Springer Nature Singapore Pte Ltd. 2018. All rights reserved.