Advancing Smart Healthcare

Smart healthcare relies more than ever on the enhanced image analysis techniques for better diagnosis and patient outcomes. Advanced sharpening techniques, integrated with algorithms for edge detection, may be the way to offer enhancement to image processing, mainly in applications towards healthcare. This chapter discusses how the adaptive sharpening methods and a machine learning-based sharpening method can be implemented along with the traditional edge detection methods, such as Canny and Structured Edge Detection, for enhancing feature extractions as well as the clarity of images. The proposed framework integrates these processes into a coherent whole. It is rather possible to optimize image analysis in many areas with bringing peculiar benefits to medical imaging in smart healthcare. This integrated approach significantly improves edge clarity, detection accuracy and general quality of images, especially in medical imaging where details about the sharp delineation of anatomical structures are critical in making an early diagnosis and planning the treatment. Qualitative and quantitative analysis showed that the method presented is involved in enhancing feature extraction for application aspects of healthcare images, particularly critical ones like CT and MRI, etc. This, therefore, improves diagnostic procedure accuracy and timeliness. The approach can further be used in surveillance systems where object detection is a paramount task. Although some challenges are experienced when tuning the parameters and increasing the computational complexity, the advantages of this integrated approach are significant, especially in smart healthcare environments. This work finds a good platform for further progress in image processing toward the development of more vigorous and flexible, yet accurate methods, within the application area of healthcare and beyond.