The exponential growth of medical imaging and Internet of Things (IoT) healthcare devices has ushered in a new era of intelligent healthcare systems, enabling continuous monitoring, early diagnosis, and personalized treatment. High-quality data acquisition and effective preprocessing are critical to ensuring the reliability, accuracy, and clinical relevance of these systems. Medical imaging modalities, including MRI, CT, Ultrasound, and PET, generate complex datasets that require sophisticated preprocessing to reduce noise, correct artifacts, and enhance diagnostic features. Simultaneously, IoT-based healthcare devices produce heterogeneous and high-velocity physiological data streams that necessitate real-time preprocessing for anomaly detection, calibration, and temporal alignment. The integration of deep learning techniques, such as convolutional neural networks, autoencoders, and generative adversarial networks, has significantly advanced preprocessing for both imaging and sensor-based data, providing automated, adaptive, and high-fidelity outputs suitable for predictive analytics. The fusion of multimodal datasets from imaging and IoT devices enhances the comprehensiveness of patient monitoring and supports robust clinical decision-making. Despite these advancements, challenges persist in ensuring interoperability, maintaining data privacy, and achieving real-time processing in distributed environments. This chapter presents a systematic overview of state-of-the-art data acquisition strategies and preprocessing methodologies, emphasizing AI-driven frameworks, multimodal data integration, and scalable computational architectures. The discussion highlights current limitations, emerging trends, and future research directions for developing intelligent, interoperable, and patient-centric healthcare systems.
The rapid proliferation of digital technologies has fundamentally transformed the healthcare landscape, establishing a paradigm where data-driven insights directly influence patient outcomes [1]. Medical imaging modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Ultrasound, and Positron Emission Tomography (PET) generate intricate datasets that reveal structural and functional characteristics of human anatomy [2]. The precision and reliability of these imaging techniques are pivotal for accurate diagnosis, early detection of anomalies, and treatment planning [3]. Concurrently, IoT-enabled healthcare devicesâ€â€Âincluding wearable sensors, implantable monitors, and mobile health platformsâ€â€Âfacilitate continuous tracking of physiological parameters such as heart rate, glucose levels, oxygen saturation, and blood pressure [3]. The seamless integration of imaging and sensor data forms the foundation of intelligent healthcare ecosystems, enabling predictive analytics, personalized interventions, and real-time monitoring that were previously unattainable in conventional medical practice [4], [5].
Data acquisition forms the critical first step in the development of intelligent healthcare systems, determining the quality, accuracy, and usability of subsequent analytical processes [6]. Medical imaging data acquisition involves the precise capture of high-resolution images while mitigating artifacts caused by patient movement, hardware limitations, or environmental interference [7]. Similarly, IoT healthcare devices must account for signal variability, sensor drift, and network latency to maintain reliable data streams [8]. The heterogeneity of devices, differences in sampling rates, and variable resolutions introduce challenges that demand robust acquisition protocols [9]. Standardization frameworks, such as DICOM for imaging and HL7/FHIR for sensor data, provide mechanisms for harmonized data representation, yet practical implementation often reveals gaps in interoperability, latency management, and data integrity. Ensuring the fidelity of acquired data was essential, as it directly impacts preprocessing, feature extraction, and downstream predictive modeling [10].
Preprocessing techniques are essential to transform raw, heterogeneous data into formats suitable for advanced analysis, enhancing both accuracy and interpretability [11]. In medical imaging, preprocessing involves tasks such as denoising, intensity normalization, contrast enhancement, image registration, and segmentation to reduce artifacts and preserve critical structural details [12]. For IoT healthcare devices, preprocessing includes calibration, anomaly detection, temporal alignment, and filtering of high-frequency noise inherent in continuous sensor streams [13]. Emerging AI-driven approaches, such as convolutional neural networks (CNNs), autoencoders, and generative adversarial networks (GANs), provide automated solutions capable of learning complex patterns, correcting distortions, and reconstructing high-fidelity representations [14]. These techniques enable adaptive, modality-specific preprocessing pipelines, allowing data to be leveraged effectively for predictive analytics, decision support, and multimodal integration, thereby enhancing the overall utility of healthcare data [15].
