ABSTRACT
PURPOSE: Image registration is important in medical applications accomplished by improving healthcare technology in recent years. Various studies have been proposed in medical applications, including clinical track of events and updating the treatment plan for radiotherapy and surgery. This study presents a fully automatic registration system for chest X-ray images to generate fusion results for difference analysis. Using the accurate alignment of the proposed system, the fusion result indicates the differences in the thoracic area during the treatment process. METHODS: The proposed method consists of a data normalization method, a hybrid L-SVM model to detect lungs, ribs and clavicles for object recognition, a landmark matching algorithm, two-stage transformation approaches and a fusion method for difference analysis to highlight the differences in the thoracic area. In evaluation, a preliminary test was performed to compare three transformation models, with a full evaluation process to compare the proposed method with two existing elastic registration methods. RESULTS: The results show that the proposed method produces significantly better results than two benchmark methods (P-value ≤ 0.001). The proposed system achieves the lowest mean registration error distance (MRED) (8.99 mm, 23.55 pixel) and the lowest mean registration error ratio (MRER) w.r.t. the length of image diagonal (1.61%) compared to the two benchmark approaches with MRED (15.64 mm, 40.97 pixel) and (180.5 mm, 472.69 pixel) and MRER (2.81%) and (32.51%), respectively. CONCLUSIONS: The experimental results show that the proposed method is capable of accurately aligning the chest X-ray images acquired at different times, assisting doctors to trace individual health status, evaluate treatment effectiveness and monitor patient recovery progress for thoracic diseases.
ABSTRACT
Breathing is one of the vital signs used to assess the physical health of a subject. Non-contact-based measurements of both breathing rate and changes in breathing rate help monitor health condition of subjects more flexibly. In this paper, we present an improved real-time camera-based adaptive breathing monitoring system, which includes real time (1) adaptive breathing motion detection, (2) adaptive region of interest detection to eliminate environmental noise, (3) breathing and body movement classification, (4) respiration rate estimation, (5) monitor change in respiration rate to examine overall health of an individual, and (6) online adaptation to lighting. The proposed system does not pose any positional and postural constraint. For evaluation, 30 videos of 15 animals are tested with drugs to simulate various medical conditions and breathing patterns, and the results from the proposed system are compared with the outputs of an existing FDA-approved invasive medical system for patient monitoring. The results show that the proposed method performs significantly correlated RR results to the reference medical device with the correlation coefficient equal to 0.92 and p-value less than 0.001, and more importantly the proposed video-based method is demonstrated to produce alarms 10 to 20 s earlier than the benchmark medical device. Graphical abstract The proposed system flowchart to extract the respiratory pattern from video.
